LOLNLP Final Notebook.ipynb.REMOTE.69370.ipynb

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      "#LOLNLP GIF Recommender\n",
      "Arvind Narayanan, Tom Silver, Ved Topkar\n",
      "\n",
      "## Overview\n",
      "\n",
      "In this project, we set out to create a GIF recommender that can recommend relevant GIFs from an input piece of text. The current method of finding GIFs relevant to text is to use some sort of search engine for GIFs, which takes time and effort. The goal was to use our knowledge of Data Science to create a GIF recommender that is both easy to use and that quickly recommends GIFs that are relevant to the input text, without much effort on the part of the user. We realized that the corpus of GIF \"listicles\" on BuzzFeed made for the perfect training set of text associated with a large number of GIFs. We also realized that classifying a GIF with a specific emotion or range of emotions is a task that cannot be done very well by machine learning algorithms alone. Consequently, we split this project up into the following parts:\n",
      "\n",
      "1. [Web Scraping and Crowdsourced GIF Sentiment Tagging](#web-scraping)\n",
      "    1. [Crawling BuzzFeed for GIF Listicles](#crawling-buzzfeed)\n",
      "    2. [Scraping Articles for GIFs and Tags](#scraping-buzzfeed)\n",
      "    3. [Scraping Giphy for supplemental GIFs](#scraping-giphy)\n",
      "    4. [Crowdsourcing Sentiment Tagging](#crowdsourced-tagging)\n",
      "2. [Natural Language Processing and Image Analysis](#analysis)\n",
      "    1. [Image color and luminance analysis](#image-analysis)\n",
      "    1. [Image motion analysis](#motion-analysis)\n",
      "    1. [Parts of Speech Tagging](#pos-tagging)\n",
      "    2. [POS-Based Sentiment Analysis](#sentiment-analysis)\n",
      "    3. [Dictionary-Based Sentiment Analysis](#dictionary-analysis)\n",
      "    4. [Content Analysis](#content-analysis)\n",
      "3. Recommendation Engine"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"web-scraping\"></a>\n",
      "# Web Scraping"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"crawling-buzzfeed\"></a>\n",
      "## Crawling BuzzFeed\n",
      "\n",
      "<img src='http://media.giphy.com/media/HVmfekBkrqncs/giphy.gif'/>\n",
      "\n",
      "The first step in this process is for us to crawl BuzzFeed and collect a large list of URLs of articles that contain GIFs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import requests\n",
      "import Queue\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "srcs = [\"shawnkathleen/flying-this-holiday-season-dont-be-one-of-these-iakw\"]\n",
      "# This is one of many source URLs that we use to collect GIF listicles\n",
      "def diff(a, b):\n",
      "    \"\"\"Return the different between two lists.\"\"\"\n",
      "    b = set(b)\n",
      "    return [c for c in a if c not in b]\n",
      "\n",
      "def parse_href(url_string):\n",
      "    \"\"\"Parse the URL from an HTML string containing one. If no URL, return False.\"\"\"\n",
      "    href_idx = url_string.find('href=\"')\n",
      "    \n",
      "    if href_idx <= 0:\n",
      "        return False\n",
      "    \n",
      "    partial_string = url_string[href_idx+7:]\n",
      "    return partial_string[0:partial_string.find('\"')]\n",
      "\n",
      "def is_gif_article(gif_article):\n",
      "    \"\"\"Return True if the article is a listicle containing GIFs, False otherwise.\"\"\"\n",
      "    \n",
      "    buzzfeed_article = requests.get(\"http://www.buzzfeed.com/\"+gif_article)\n",
      "    listicle_items = buzzfeed_article.content.split('class=\"buzz_superlist_item')\n",
      "    \n",
      "    for item in listicle_items[1:-1]:\n",
      "        # Check if item contains a GIF.\n",
      "        if item.find('.gif') > 0:\n",
      "            return True\n",
      "    return False\n",
      "\n",
      "def find_other_gif_articles(gif_article):\n",
      "    \"\"\"Search 'related articles' listed on input article for possible GIFs.\"\"\"\n",
      "    gif_articles = []\n",
      "    \n",
      "    buzzfeed_article = requests.get(\"http://www.buzzfeed.com/\"+gif_article)\n",
      "    related_strings = buzzfeed_article.content.split('rel:gt_act=\"related-link/name\"')\n",
      "\n",
      "    for related_string in related_strings[1:]:\n",
      "        href = parse_href(related_string)\n",
      "        if is_gif_article(href) > 0:\n",
      "            gif_articles.append(href)\n",
      "            \n",
      "    return gif_articles\n",
      "\n",
      "def crawl_for_gif_pages(source, limit=1000, delay=0.5):\n",
      "    \"\"\"Crawl Buzzfeed, starting at the source, collecting pages that contain GIFs.\"\"\"\n",
      "    found = []\n",
      "\n",
      "    sources = []\n",
      "    sources.append(source)\n",
      "\n",
      "    while (len(found)<limit and len(sources)>0):\n",
      "        source = sources.pop()\n",
      "\n",
      "        new_article_candidates = find_other_gif_articles(source)\n",
      "        new_articles = diff(new_article_candidates, found)\n",
      "\n",
      "        found.extend(new_articles)\n",
      "        sources.extend(new_articles)\n",
      "        \n",
      "        time.sleep(delay)\n",
      "    \n",
      "    return pd.Series(found)\n",
      "\n",
      "# Crawl all of the sources\n",
      "gif_pages = crawl_for_gif_pages(srcs)\n",
      "gif_pages.to_csv('data/buzzfeed_gif_links.txt')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Display all of the GIFs\n",
      "gif_pages = pd.read_csv('data/buzzfeed_gif_links.txt')\n",
      "print gif_pages.shape\n",
      "gif_pages.head()"
     ],
     "language": "python",
     "metadata": {},
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        "      <th></th>\n",
        "      <th>simplysinova/25-mouthwatering-dessert-gifs-k808</th>\n",
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        "      <th>0</th>\n",
        "      <td> giulsi/20-amazing-will-ferrells-gifs-for-any-o...</td>\n",
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        "      <th>1</th>\n",
        "      <td> erinf45e036d0f/34-david-bowie-gifs-for-all-occ...</td>\n",
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        "    <tr>\n",
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        "    </tr>\n",
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        "      <th>3</th>\n",
        "      <td> giulsi/20-amazing-will-ferrells-gifs-for-any-o...</td>\n",
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        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> erinf45e036d0f/34-david-bowie-gifs-for-all-occ...</td>\n",
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        "  </tbody>\n",
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        "     simplysinova/25-mouthwatering-dessert-gifs-k808\n",
        "0  giulsi/20-amazing-will-ferrells-gifs-for-any-o...\n",
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"scraping-buzzfeed\"></a>\n",
      "## Scraping BuzzFeed\n",
      "<img src = 'http://media.giphy.com/media/12gQnMGq0lv7W0/giphy.gif'/>\n",
      "Once we have a list of a large number of BuzzFeed GIF articles, we scraped each of them individually for the article title, each GIF, and each GIF's associated text."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def newscrapepage(url):\n",
      "    \"\"\"\n",
      "    Given the URL of a BuzzFeed listicle, return a Dictionary containing the title text,\n",
      "    GIF URL, and GIF text corresponding to each GIF in the listicle.\n",
      "    \"\"\"\n",
      "    dfs = []\n",
      "    try:\n",
      "        page = urllib2.urlopen(url).read()\n",
      "        soup = BeautifulSoup(page)\n",
      "        soup.prettify()\n",
      "        imgs = []\n",
      "        texts = []\n",
      "        title = \"blank\"\n",
      "        # Find all img tags in the listicle\n",
      "        for tag in soup.findAll('img'):\n",
      "            if tag.has_key('src'):\n",
      "                img = tag['src']\n",
      "                \"\"\"\n",
      "                If the img is a GIF, add it to the gif list, and \n",
      "                add the caption above it (if one exists) to the text list\n",
      "                \"\"\"\n",
      "                if(img[-12:] == \"_preview.gif\"):\n",
      "                    img = img[:-12] + \".gif\"\n",
      "                    imgs.append(img)\n",
      "                    new = tag.findPrevious('h2')\n",
      "                    text = parser.unescape(''.join(new.findAll(text=True)))\n",
      "                    texts.append(text)\n",
      "        # Set the title for all of the GIFs on the page to be the title text\n",
      "        for tag in soup.findAll('title'):\n",
      "            title = parser.unescape(''.join(tag.findAll(text=True)))\n",
      "        for i in xrange(min(len(imgs),len(texts))):\n",
      "            dfs.append({'URL' : imgs[i], 'Text' : texts[i], 'Title' : title})\n",
      "        return dfs\n",
      "    except:\n",
      "        print (u\"Could not open page \" + url)\n",
      "        return []"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Use the above scraping function on each crawled link\n",
      "to collect GIF information from each page\n",
      "\"\"\"\n",
      "\n",
      "urlfile = open('data/buzzfeed_gif_links.txt','r')\n",
      "pages = {}\n",
      "# Format the URLs into proper Buzzfeed URLs\n",
      "for line in urlfile:\n",
      "    line = 'http://www.buzzfeed.com/' + line.replace('\\n','')\n",
      "    if not(line in pages):\n",
      "        pages[line] = 1\n",
      "urlfile_new = open('data/buzzfeed_gif_links_pt2.txt','r')\n",
      "for line in urlfile_new:\n",
      "    line = 'http://www.buzzfeed.com/' + line.replace('\\n','')\n",
      "    if not(line in pages):\n",
      "        pages[line] = 1\n",
      "gifs = []\n",
      "# Scrape each page for GIFs and create a DataFrame of the GIFs\n",
      "for page in pages:\n",
      "    gifs.append(newscrapepage(page))\n",
      "bf = [item for sublist in gifs for item in sublist]\n",
      "df = pd.DataFrame(bf)\n",
      "df.to_csv('data/buzzfeed_gifs_final.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "buzzfeed_gifs = pd.read_csv('data/buzzfeed_gifs_final.csv',encoding='utf-8')\n",
      "buzzfeed_gifs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>Text</th>\n",
        "      <th>Title</th>\n",
        "      <th>URL</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1. Because I just wasn\u2019t one of those people w...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2. Because there are SO many options to choose...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> 3. Some days you hate every subject in the wor...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3</td>\n",
        "      <td> 4. And other days you\u2019re super ambitious and t...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4</td>\n",
        "      <td> 5. But either way you know that you don\u2019t know...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
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       "text": [
        "   Unnamed: 0                                               Text  \\\n",
        "0           0  1. Because I just wasn\u2019t one of those people w...   \n",
        "1           1  2. Because there are SO many options to choose...   \n",
        "2           2  3. Some days you hate every subject in the wor...   \n",
        "3           3  4. And other days you\u2019re super ambitious and t...   \n",
        "4           4  5. But either way you know that you don\u2019t know...   \n",
        "\n",
        "                                               Title  \\\n",
        "0  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "1  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "2  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "3  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "4  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "\n",
        "                                                 URL  \n",
        "0  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  \n",
        "1  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  \n",
        "2  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  \n",
        "3  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  \n",
        "4  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  "
       ]
      }
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     "prompt_number": 14
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a href=\"#scraping-giphy\"></a>\n",
      "## Scraping Giphy\n",
      "<img src = 'http://media.giphy.com/media/1IZ7XOCWvoQfu/giphy.gif'/>\n",
      "In case we needed more GIF data, we also built a simple GIF scraper for giphy, a website where people can upload and tag GIFs.\n",
      "\n",
      "Although Giphy tries to keep people from being able to scrape their site, after some exploring we found a trick in their URL encoding that allowed us to query each of the emotion tags in their tag repository and iterate through each page of GIFs.\n",
      "\n",
      "Below, we see the scraper that iterates through each of these emotions and pages and collects about 40,000 GIFs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import urllib2\n",
      "from bs4 import BeautifulSoup"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfs = []\n",
      "emotions = ['angry', 'bored', 'disappointed', 'drunk', 'embarassed', \n",
      "           'excited', 'frustrated', 'happy', 'hungry', 'inspired', \n",
      "           'lonely', 'love', 'nervous', 'pain', 'reaction', 'relaxed', \n",
      "           'sad', 'sassy', 'scared', 'shocked', 'sick', 'stressed', \n",
      "           'surprised', 'suspicious', 'tired', 'unimpressed']\n",
      "\n",
      "for emotion in emotions:\n",
      "    print emotion\n",
      "    # Search giphy for each emotion tag\n",
      "    urls = ['http://giphy.com/search/' + emotion + '/' + str(x) +'?is=2' for x in range(100)]\n",
      "    for url in urls:\n",
      "        try:\n",
      "            page = urllib2.urlopen(url).read()\n",
      "            soup = BeautifulSoup(page)\n",
      "            images = soup.findAll('div', class_='hoverable-gif')\n",
      "            # Add each found gif to the list\n",
      "            data = []\n",
      "            for image in images:\n",
      "                tags = [x.text.split('#')[1] for x in image.span.findAll('a')]\n",
      "                url = image.a.figure.img['src'].replace('_s', '')\n",
      "                dfs.append({'tags':tags, 'url':url, 'emotion': emotion})\n",
      "        except:\n",
      "            print 'Done with ' + emotion\n",
      "            break\n",
      "    \n",
      "data = pd.DataFrame(dfs)\n",
      "data.to_csv('data/giphy_emotions_gifs.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "giphy_gifs = pd.read_csv('data/giphy_emotions_gifs.csv')\n",
      "print giphy_gifs.shape\n",
      "giphy_gifs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(39975, 4)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>emotion</th>\n",
        "      <th>tags</th>\n",
        "      <th>url</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> angry</td>\n",
        "      <td>           [u'angry', u'horse', u'maximus']</td>\n",
        "      <td> http://media2.giphy.com/media/dOZj6qlD8Kwta/20...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> angry</td>\n",
        "      <td>                [u'angry', u'taylor swift']</td>\n",
        "      <td> http://media0.giphy.com/media/ORKQqVjegOvzq/20...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> angry</td>\n",
        "      <td>          [u'angry', u'coach', u'throwing']</td>\n",
        "      <td> http://media2.giphy.com/media/110gOs75GuUWyY/2...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3</td>\n",
        "      <td> angry</td>\n",
        "      <td>                  [u'angry', u'frustrated']</td>\n",
        "      <td> http://media0.giphy.com/media/yI9YaxO7OCz5K/20...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4</td>\n",
        "      <td> angry</td>\n",
        "      <td> [u'angry', u'mario', u'nintendo', u'zoom']</td>\n",
        "      <td> http://media0.giphy.com/media/kLEuIxZJh8VG0/20...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "   Unnamed: 0 emotion                                        tags  \\\n",
        "0           0   angry            [u'angry', u'horse', u'maximus']   \n",
        "1           1   angry                 [u'angry', u'taylor swift']   \n",
        "2           2   angry           [u'angry', u'coach', u'throwing']   \n",
        "3           3   angry                   [u'angry', u'frustrated']   \n",
        "4           4   angry  [u'angry', u'mario', u'nintendo', u'zoom']   \n",
        "\n",
        "                                                 url  \n",
        "0  http://media2.giphy.com/media/dOZj6qlD8Kwta/20...  \n",
        "1  http://media0.giphy.com/media/ORKQqVjegOvzq/20...  \n",
        "2  http://media2.giphy.com/media/110gOs75GuUWyY/2...  \n",
        "3  http://media0.giphy.com/media/yI9YaxO7OCz5K/20...  \n",
        "4  http://media0.giphy.com/media/kLEuIxZJh8VG0/20...  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"crowdsourced-tagging\"></a>\n",
      "# Crowdsourced GIF Sentiment Tagging\n",
      "<img src='http://media.giphy.com/media/bk0r4b5o1HN7i/giphy.gif'/>\n",
      "Once we had a large corpus of GIFs and associated text and titles from our BuzzFeed crawling and scraping, we knew that we needed to build some type of dataset that associated each GIF with some type of emotional component. The objective in mind is that in order to match text to a GIF, we must find the emotional content of the text, and find a GIF with corresponding emotional content.\n",
      "\n",
      "We decided to do this by crowdsourcing the association of GIFs to sentiment (henceforth called \"Sentiment Tagging\"). To do this, we created a website where people could classify a random GIF from our corpus of GIFs. Our spectrum of GIF emotions came from the NC State tweet sentiment visualization map, which can be found [here](http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/).\n",
      "\n",
      "The horizontal axis is \"valence\", ranging from negative on the left side and positive on the right side. The vertical axis is \"arousal\", or general alertness, with high alertness on top and low alertness on bottom. The full spectrum is shown below. The URL of the crowdsourcing site is [http://tomssilver.com/cs109/](http://tomssilver.com/cs109/).\n",
      "\n",
      "\n",
      "<img src=\"http://tomssilver.com/cs109/images/sentiment_circle.png\"/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The final version of our crowdsourcing platform, found here: [http://tomssilver.com/cs109/](http://tomssilver.com/cs109/), can be seen below. A user clicks somewhere on the map to place the GIF somewhere on the 2D emotional spectrum."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://tomssilver.com/cs109/images/crowdsourcing_screencap.png\"/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The user can also go to the \"GIF Map\" section of the site to see all of the results. In the example below, we have clicked on a dot that has been placed in the \"Excited\" category."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://tomssilver.com/cs109/images/results.png\"/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Building the Crowdsourcing Site\n",
      "\n",
      "Building the crowdsourcing site required the programming of a dynamic PHP/MySQL website. We have listed the main PHP and Javascript snippets that provided for the core functionality of the site.\n",
      "\n",
      "\n",
      "The first component is the Javascript that loads the GIFs and handles interaction with the sentiment tagging graph. This code can be seen below:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "$('.gif_point').hide();\n",
      "\n",
      "// Use AJAX to get a GIF of the given gif_id\n",
      "function set_gif(id)\n",
      "{\n",
      "    $.ajax({\n",
      "          url: \"print_gif.php?id=\"+id,\n",
      "          cache: false\n",
      "        })\n",
      "          .done(function(data) {\n",
      "            // Load the GIF into the DOM\n",
      "            $(\"#gif\").html(data);\n",
      "          });\n",
      "}\n",
      "\n",
      "function bind_set_gif(id)\n",
      "{\n",
      "    $(\"#\"+id).on('click', function ()\n",
      "    {\n",
      "        set_gif(id);\n",
      "    });\n",
      "}\n",
      "\n",
      "function update_sizes(id, w, h)\n",
      "{\n",
      "    $(\"#\"+id).css(\"top\", parseInt(h)+\"px\");\n",
      "    $(\"#\"+id).css(\"left\", parseInt(w)+\"px\");\n",
      "\n",
      "    $(\"#\"+id).show();\n",
      "\n",
      "    bind_set_gif(id);\n",
      "}\n",
      "\n",
      "function resize(id)\n",
      "{\n",
      "\n",
      "  offset = $('#sent_circ').offset();\n",
      "  w = parseFloat($('#sent_circ').width());\n",
      "  h = parseFloat($('#sent_circ').height());\n",
      "\n",
      "  l = parseFloat($(\"#\"+id).css('left'));\t\t\t\n",
      "  t = parseFloat($(\"#\"+id).css('top'));\t\n",
      "\n",
      "  update_sizes(id, w*l, t*h);\t\t  \n",
      "\n",
      "\n",
      "}\n",
      "\n",
      "$(document).ready(function() {    \t\n",
      "\n",
      "    $('#done').ready(function() {\n",
      "\n",
      "        $('.gif_point').each(function() {\n",
      "\n",
      "          resize($(this).attr(\"id\"));\n",
      "\n",
      "        });\n",
      "\n",
      "    });\n",
      "});\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, we had PHP code that retrieved a random GIF from the MySQL database and returned appropriate HTML to be injected into the DOM."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "<?\n",
      "\trequire_once(\"connect.php\");\n",
      "\t$query = \"SELECT * FROM `links` ORDER BY RAND() LIMIT 1\";\n",
      "\t$result = mysql_query($query);\n",
      "\n",
      "\twhile ($row = mysql_fetch_array($result)) {\n",
      "    \t$gif_id = $row['gif_id'];\n",
      "\t\t$gif_link = $row['gif_link'];\n",
      "\t\tbreak;\n",
      "\t}\n",
      "\t\n",
      "?>\t\n",
      "\n",
      "<input type=\"hidden\" id=\"gif_id\" value=\"<? echo $gif_id; ?>\">\n",
      "<img class=\"img-responsive\" src=\"<? echo $gif_link; ?>\" alt=\"\">\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Of couse, we also have PHP that retrieves a GIF of a specified `gif_id` from the MySQL database and returns appropriate HTML to be injected in the DOM."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "<?\n",
      "\trequire_once(\"connect.php\");\n",
      "\t$query = \"SELECT * FROM `links` WHERE gif_id = \".mysql_real_escape_string($_GET['id']);;\n",
      "\t$result = mysql_query($query);\n",
      "\n",
      "\twhile ($row = mysql_fetch_array($result)) {\n",
      "\t\t$gif_link = $row['gif_link'];\n",
      "\t\tbreak;\n",
      "\t}\n",
      "\t\n",
      "?>\t\n",
      "\n",
      "<img class=\"img-responsive\" src=\"<? echo $gif_link; ?>\" alt=\"\">\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We also have PHP code that logs a GIF's `x` and `y` data when a user clicks on the map:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "<?php\n",
      "\trequire_once(\"connect.php\");\n",
      "\n",
      "\t$gif_id = mysql_real_escape_string($_GET['gif_id']);\n",
      "\t$x = mysql_real_escape_string($_GET['x']);\n",
      "\t$y = mysql_real_escape_string($_GET['y']);\n",
      "\n",
      "\tmysql_query(\"INSERT INTO gifs (gif_id, x, y) VALUES ('\".$gif_id.\"', '\".$x.\"', '\".$y.\"')\");\n",
      "\n",
      "?>\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, for the results page, we have code that loads all of the GIF tags as tagged by the users into a single array, and then inject them into the DOM."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "<?\n",
      "\n",
      "\trequire_once(\"connect.php\");\n",
      "\t\n",
      "\t$query = \"SELECT gif_id, AVG(x), AVG(y) FROM `gifs` GROUP BY gif_id\";\n",
      "\t$result = mysql_query($query);\n",
      "\t\n",
      "\t$gif_array = Array();\n",
      "\t\n",
      "\twhile ($row = mysql_fetch_array($result)) {\n",
      "    \tarray_push($gif_array, $row);\n",
      "\t}\n",
      "\n",
      "?>\n",
      "\n",
      "<? \n",
      "    foreach ($gif_array as $row) {\n",
      "        echo '<div id=\"'.$row[0].'\" class=\"gif_point\" style=\"left: '.floatval($row[1]).'px; top: '.floatval($row[2]).'px;\"></div>';\n",
      "    }\n",
      "    echo \"<div id='done' style='display: hidden;'></div>\";\n",
      "?>\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"analysis\"></a>\n",
      "# Natural Language Processing and Image Analysis\n",
      "\n",
      "Once we had a corpus of GIFs with associated text and sentiment tags, we proceeded to analyze this dataset in a number of different ways."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"image-analysis\"></a>\n",
      "\n",
      "## Image Color Analysis\n",
      "\n",
      "One aspect of the GIFs that we had Sentiment Tagged that we were very intersted in analyzing was the colors of the GIFs. To this end, we pursued a number of analysis tasks, the first of which being **color palette comparisons**.\n",
      "\n",
      "The way this code works is that it takes a GIF URL, and then follows the following steps:\n",
      "\n",
      "1. Store the GIF in memory\n",
      "2. To spare compute time, shrink the GIF to a smaller thumbnail version (preserving color)\n",
      "3. Split each frame of the GIF into a separate image in-memory\n",
      "4. Merge the pixel values of all of the pixels in all of the frames to create a large array of all pixels\n",
      "5. Run KMeans to find the cluster centers of the aggregated pixels\n",
      "6. The centers of these clusters are the dominant colors in the colorscheme of the image"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "from __future__ import division\n",
      "from IPython.display import Image as PyImage\n",
      "from IPython.display import SVG\n",
      "from IPython import display\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import math\n",
      "from PIL import Image\n",
      "import requests\n",
      "from StringIO import StringIO\n",
      "import matplotlib.pyplot as plt\n",
      "from sklearn.cluster import KMeans\n",
      "import seaborn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def colorscheme_from_url(url, n_colors=3):\n",
      "    \"\"\"\n",
      "    Given a url and a given number of colors to extract\n",
      "    Generate a colorscheme of n_colors colors\n",
      "    \"\"\"\n",
      "    \n",
      "    \n",
      "    \"\"\"\n",
      "    Get the GIF from the url and save it in memory\n",
      "    \"\"\"\n",
      "    response = requests.get(url)\n",
      "    img = Image.open(StringIO(response.content))\n",
      "    \n",
      "    \n",
      "    \"\"\"\n",
      "    Iterate through each frame and save it as a separate image (in memory)\n",
      "    \"\"\"\n",
      "    n = 0\n",
      "    frames = []\n",
      "    while img:\n",
      "        i = img.copy()\n",
      "        i.thumbnail((150,150))\n",
      "        frames.append(i)\n",
      "        n += 1\n",
      "        try:\n",
      "            img.seek(n)\n",
      "        except EOFError:\n",
      "            break\n",
      "    print 'Frames: ' + str(len(frames))\n",
      "    \n",
      "\n",
      "    \"\"\"\n",
      "    Get all pixels from all frames into a single 2D array\n",
      "    \"\"\"\n",
      "    all_pixels = np.array([[100,100,100]])\n",
      "    for frame in frames:\n",
      "        img = frame.convert('RGB')\n",
      "        all_pixels = np.vstack((np.asarray(img).reshape(-1, 3), all_pixels))\n",
      "\n",
      "    all_pixels = all_pixels[:all_pixels.shape[0] - 1]\n",
      "    \n",
      "    \n",
      "    \"\"\"\n",
      "    Run the pixels through KMeans clustering with n_colors clusters\n",
      "    The centers are the primary colors\n",
      "    \"\"\"\n",
      "    clt = KMeans(n_clusters=n_colors)\n",
      "    clt.fit(all_pixels)\n",
      "\n",
      "    primary_colors = [[int(x) for x in i] for i in clt.cluster_centers_]\n",
      "    return primary_colors\n",
      "\n",
      "\n",
      "def display_colorscheme(url, colors):\n",
      "    \"\"\"\n",
      "    For use in IPython Notebooks\n",
      "    Given a URL and a list of RGB colors\n",
      "    Output the GIF and the colorscheme in HTML\n",
      "    \"\"\"\n",
      "    \n",
      "    html = ''\n",
      "    for color in colors:\n",
      "        html += '<div style=\"background-color: rgb({0}); padding:20px;width:20px;display:inline-block;\"></div>'.format(','.join(str(int(c)) for c in color))\n",
      "    \n",
      "    display.display(PyImage(url=url))\n",
      "    display.display(HTML(html))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This effectively gives us the ability to extract the primary colors in any GIF, which was very exciting. Here are examples, with each GIF and its associated colorscheme shown below it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = 'http://media.giphy.com/media/bnfczfvZS6zni/giphy.gif'\n",
      "colors = colorscheme_from_url(url, n_colors=5)\n",
      "display_colorscheme(url, colors)\n",
      "\n",
      "url = 'http://media.giphy.com/media/6F5rSKknrwPXq/giphy.gif'\n",
      "colors = colorscheme_from_url(url, n_colors=5)\n",
      "display_colorscheme(url, colors)\n",
      "\n",
      "url = 'http://media.giphy.com/media/iS6w9L0bYq1Ne/giphy.gif'\n",
      "colors = colorscheme_from_url(url, n_colors=5)\n",
      "display_colorscheme(url, colors)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Frames: 20\n"
       ]
      },
      {
       "html": [
        "<img src=\"http://media.giphy.com/media/bnfczfvZS6zni/giphy.gif\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Image at 0x10a0bdd10>"
       ]
      },
      {
       "html": [
        "<div style=\"background-color: rgb(103,187,248); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(62,89,49); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(39,174,246); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(109,144,110); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(216,221,231); padding:20px;width:20px;display:inline-block;\"></div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10a0bdd50>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Frames: 20\n"
       ]
      },
      {
       "html": [
        "<img src=\"http://media.giphy.com/media/6F5rSKknrwPXq/giphy.gif\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Image at 0x10a0bd1d0>"
       ]
      },
      {
       "html": [
        "<div style=\"background-color: rgb(242,244,241); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(32,27,53); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(21,198,233); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(227,185,30); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(32,90,167); padding:20px;width:20px;display:inline-block;\"></div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10f483890>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Frames: 16\n"
       ]
      },
      {
       "html": [
        "<img src=\"http://media.giphy.com/media/iS6w9L0bYq1Ne/giphy.gif\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Image at 0x10f483bd0>"
       ]
      },
      {
       "html": [
        "<div style=\"background-color: rgb(62,203,222); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(238,177,42); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(208,239,215); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(218,67,17); padding:20px;width:20px;display:inline-block;\"></div><div style=\"background-color: rgb(34,87,91); padding:20px;width:20px;display:inline-block;\"></div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10f5a1fd0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once we got this type of functionality up and running, we decided to run this analysis on ever sentiment tagged GIF in our dataset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Get the primary colors for each GIF in our tagged table\n",
      "\"\"\"\n",
      "\n",
      "urls = categorized_gifs['url']\n",
      "colors = []\n",
      "for i,url in enumerate(urls):\n",
      "    c = colorscheme_from_url(url, n_colors=5)\n",
      "    colors.append(c)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Our final DataFrame with primary color information for each GIF\n",
      "categorized_gifs = pd.read_csv('data/categorized_gifs_with_colors.csv')\n",
      "categorized_gifs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
        "      <th>id</th>\n",
        "      <th>gif_id</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>url</th>\n",
        "      <th>strength</th>\n",
        "      <th>emotion</th>\n",
        "      <th>colors</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 110</td>\n",
        "      <td> 4057</td>\n",
        "      <td>-0.124000</td>\n",
        "      <td>-0.394737</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.413755</td>\n",
        "      <td>    bored</td>\n",
        "      <td> [[234, 232, 238], [111, 111, 117], [40, 37, 36...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 109</td>\n",
        "      <td> 6246</td>\n",
        "      <td> 0.348000</td>\n",
        "      <td>-0.218045</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.410667</td>\n",
        "      <td>   serene</td>\n",
        "      <td> [[203, 62, 50], [90, 144, 152], [248, 198, 97]...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 114</td>\n",
        "      <td> 7702</td>\n",
        "      <td> 0.390667</td>\n",
        "      <td>-0.033835</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-01/enhanc...</td>\n",
        "      <td> 0.392129</td>\n",
        "      <td> pleasant</td>\n",
        "      <td> [[252, 247, 217], [192, 4, 2], [248, 51, 18], ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 113</td>\n",
        "      <td> 6925</td>\n",
        "      <td> 0.053333</td>\n",
        "      <td>-0.097744</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.111348</td>\n",
        "      <td>  relaxed</td>\n",
        "      <td> [[66, 60, 55], [162, 167, 137], [79, 87, 88], ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 112</td>\n",
        "      <td> 1355</td>\n",
        "      <td>-0.354667</td>\n",
        "      <td>-0.154135</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-10/20/16/...</td>\n",
        "      <td> 0.386712</td>\n",
        "      <td>      sad</td>\n",
        "      <td> [[184, 112, 21], [255, 255, 255], [86, 44, 14]...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "   Unnamed: 0  Unnamed: 0.1   id  gif_id         x         y  \\\n",
        "0           0             0  110    4057 -0.124000 -0.394737   \n",
        "1           1             1  109    6246  0.348000 -0.218045   \n",
        "2           2             2  114    7702  0.390667 -0.033835   \n",
        "3           3             3  113    6925  0.053333 -0.097744   \n",
        "4           4             4  112    1355 -0.354667 -0.154135   \n",
        "\n",
        "                                                 url  strength   emotion  \\\n",
        "0  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.413755     bored   \n",
        "1  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.410667    serene   \n",
        "2  http://s3-ec.buzzfed.com/static/2014-01/enhanc...  0.392129  pleasant   \n",
        "3  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.111348   relaxed   \n",
        "4  http://s3-ec.buzzfed.com/static/2014-10/20/16/...  0.386712       sad   \n",
        "\n",
        "                                              colors  \n",
        "0  [[234, 232, 238], [111, 111, 117], [40, 37, 36...  \n",
        "1  [[203, 62, 50], [90, 144, 152], [248, 198, 97]...  \n",
        "2  [[252, 247, 217], [192, 4, 2], [248, 51, 18], ...  \n",
        "3  [[66, 60, 55], [162, 167, 137], [79, 87, 88], ...  \n",
        "4  [[184, 112, 21], [255, 255, 255], [86, 44, 14]...  "
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A natural question that we asked next was if there were any trends that we could extract from this primary color analysis. The first thing that we did was compute the average luminance of the primary colors for each GIF.\n",
      "\n",
      "Luminance is a metric for describing the \"brightness\" of a color as calculated by how our eyes perceieve each of the three components of light (Red, Green, Blue). The standard equation (from here) is:\n",
      "\n",
      "$$Luminance = \\sqrt{0.241\\times \\text{Red}^2 + 0.691\\times \\text{Green}^2 + 0.068\\times \\text{Blue}^2}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_luminance(rgb):\n",
      "    return math.sqrt( 0.241*rgb[0]**2 + 0.691*rgb[1]**2 + 0.068*rgb[2]**2 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def str_to_list(text):\n",
      "    t = text.replace('[', '').replace(']', '').replace(' ', '').split(',')\n",
      "    t = np.array([int(x) for x in t]).reshape([-1, 3])\n",
      "    return t\n",
      "\n",
      "colors = []\n",
      "for i in categorized_gifs['colors']:\n",
      "    colors.append(str_to_list(i))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "average_middle_luminances = []\n",
      "for color in colors:\n",
      "    x = np.array([x[1] for x in color])\n",
      "    x = np.sort(x)\n",
      "    average_middle_luminances.append(np.median(x))\n",
      "categorized_gifs['All Colors'] = colors\n",
      "categorized_gifs['Average Luminance'] = average_middle_luminances"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,10))\n",
      "emotion_strengths = []\n",
      "emotions = categorized_gifs['emotion'].unique()\n",
      "for emotion in emotions:\n",
      "    strengths = np.asarray(categorized_gifs[categorized_gifs['emotion']==emotion]['Average Luminance'])\n",
      "    emotion_strengths.append(strengths)\n",
      "g = seaborn.violinplot(emotion_strengths, names=emotions)\n",
      "g.set_xticklabels(emotions,rotation='vertical')\n",
      "plt.title('Spread of GIF Average Luminances for each Emotion', size=30)\n",
      "plt.xlabel('Emotion', size=20)\n",
      "plt.ylabel('Average Primary Color Luminance', size=20)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZU3sYIYRLga+lT+8EfCztde78zKcDL0qfbiVf936tPZO8IfwXwA/M7C/6vD7T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sxs5lO3l4eoqrS4vIxrvHe1O5VwLEE2MDh79/rjD+yOuLw6n6sptiETRFxcqGdceX7+jtksmpMQ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FUqn0T4F/Dxyb/ZTL5U/GimJM8D0PU7M6XlMI/BjssfzC255+D+92xyet6GnRrFeZzpr85Ler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eOM4TUPz5vwzfoZRwdPlLtZbJQqNC2H/vD6fTqzvEmOyheXRDqdxuzNVlr4ZZKWy+VuJC3a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\nL6XdbjNOIH3AZMx10WIuKuV32mg2Gee5+fCQ8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zgOuKeFkyidTpfRaBTbjB3T7fZim+A4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P9XbU8Z\n4z7ra3t9RJzQ76wLad5QT789xvUkSdKEuRCnJEkaZklEXM3wLdLv7G9TOkmZuS8iLgXuALZFxB2U\nRS6XAedT1rS4fOBtdwGfAm6JiM3A48C/M/NrjZinfY7M3BQRq4ALgR0R8WPKDI7VlMLIrc3tTiVJ\n0tyzaCFJkobpUbY8HTb2F+CBxnlvhthhY60yc0tEnA1cBaykLIi5E7gZuDYzdw3E/zIirgQ+AlwB\nHAc8DPSLFsNyWANso2x/elmNeQi4MTNvHifnGe4hSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZp//g9VoYjjW5Im/wAA\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b567ed0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As can be seen in the plot above, there are not immediately exciting results from these results. However, upon close inspection, we can see some slight trends that one might expect.\n",
      "\n",
      "For example, the categories with the highest average primary color luminances are generally the one that are the most active. For example, the happy, excited, and elated categories have above average luminances, whereas the upset and unpleasant categories have below average luminances.\n",
      "\n",
      "This generally corresponds with the intuition that more pleasant, active GIFs are brighter, while less happy and active GIFs are darker."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Applying Luminance Analysis to entire images\n",
      "\n",
      "Above, we did luminance analysis on only the primary colors of each GIF. We then set out to do the same analysis on *every pixel* of each GIF. The code and analysis are below:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def average_luminance_from_url(url):\n",
      "    \"\"\"\n",
      "    Given a url and a given number of colors to extract\n",
      "    Get the luminances\n",
      "    \"\"\"\n",
      "    \n",
      "    \n",
      "    \"\"\"\n",
      "    Get the GIF from the url and save it in memory\n",
      "    \"\"\"\n",
      "    response = requests.get(url)\n",
      "    img = Image.open(StringIO(response.content))\n",
      "    \n",
      "    \n",
      "    \"\"\"\n",
      "    Iterate through each frame and save it as a separate image (in memory)\n",
      "    \"\"\"\n",
      "    n = 0\n",
      "    frames = []\n",
      "    while img:\n",
      "        i = img.copy()\n",
      "        i.thumbnail((30,30))\n",
      "        frames.append(i)\n",
      "        n += 1\n",
      "        try:\n",
      "            img.seek(n)\n",
      "        except EOFError:\n",
      "            break\n",
      "    print 'Frames: ' + str(len(frames))\n",
      "    \n",
      "\n",
      "    \"\"\"\n",
      "    Get all pixels from all frames into a single 2D array\n",
      "    \"\"\"\n",
      "    all_pixels = np.array([[100,100,100]])\n",
      "    for frame in frames[::2]:\n",
      "        img = frame.convert('RGB')\n",
      "        all_pixels = np.vstack((np.asarray(img).reshape(-1, 3), all_pixels))\n",
      "\n",
      "    all_pixels = all_pixels[:all_pixels.shape[0] - 1]\n",
      "    \n",
      "    luminances = [compute_luminance(x) for x in all_pixels]\n",
      "    return luminances\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Frames: 20\n",
        "149.554530399\n",
        "44.9936702428\n",
        "162.039090963\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Run the average_luminance_from_url function on each GIF\n",
      "Aggregate the results\n",
      "\"\"\"\n",
      "\n",
      "all_image_luminances = []\n",
      "urls = categorized_gifs['url']\n",
      "\n",
      "for i,url in enumerate(urls):\n",
      "    luminances = average_luminance_from_url(url)\n",
      "    mean = np.mean(luminances)\n",
      "    median = np.median(luminances)\n",
      "    std = np.std(luminances)\n",
      "    all_image_luminances.append({'url': url, 'mean': mean, 'median': median, 'std': std})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Our final data\n",
      "\"\"\"\n",
      "\n",
      "df = pd.read_csv('data/categorized_gifs_with_luminance_stats.csv')\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>mean</th>\n",
        "      <th>median</th>\n",
        "      <th>std</th>\n",
        "      <th>url</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
        "      <th>id</th>\n",
        "      <th>gif_id</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>strength</th>\n",
        "      <th>emotion</th>\n",
        "      <th>colors</th>\n",
        "      <th>All Colors</th>\n",
        "      <th>Average Luminance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 116.713054</td>\n",
        "      <td> 116.353792</td>\n",
        "      <td> 65.050715</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 110</td>\n",
        "      <td> 4057</td>\n",
        "      <td>-0.124000</td>\n",
        "      <td>-0.394737</td>\n",
        "      <td> 0.413755</td>\n",
        "      <td>    bored</td>\n",
        "      <td> [[234, 232, 238], [111, 111, 117], [40, 37, 36...</td>\n",
        "      <td> [[234 232 238]\\n [111 111 117]\\n [ 40  37  36]...</td>\n",
        "      <td> 111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 150.766208</td>\n",
        "      <td> 166.573191</td>\n",
        "      <td> 72.552147</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 109</td>\n",
        "      <td> 6246</td>\n",
        "      <td> 0.348000</td>\n",
        "      <td>-0.218045</td>\n",
        "      <td> 0.410667</td>\n",
        "      <td>   serene</td>\n",
        "      <td> [[203, 62, 50], [90, 144, 152], [248, 198, 97]...</td>\n",
        "      <td> [[203  62  50]\\n [ 90 144 152]\\n [248 198  97]...</td>\n",
        "      <td> 144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> 228.921814</td>\n",
        "      <td> 253.943947</td>\n",
        "      <td> 50.670328</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-01/enhanc...</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 114</td>\n",
        "      <td> 7702</td>\n",
        "      <td> 0.390667</td>\n",
        "      <td>-0.033835</td>\n",
        "      <td> 0.392129</td>\n",
        "      <td> pleasant</td>\n",
        "      <td> [[252, 247, 217], [192, 4, 2], [248, 51, 18], ...</td>\n",
        "      <td> [[252 247 217]\\n [192   4   2]\\n [248  51  18]...</td>\n",
        "      <td> 172</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3</td>\n",
        "      <td>  72.998042</td>\n",
        "      <td>  56.801021</td>\n",
        "      <td> 48.041785</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 113</td>\n",
        "      <td> 6925</td>\n",
        "      <td> 0.053333</td>\n",
        "      <td>-0.097744</td>\n",
        "      <td> 0.111348</td>\n",
        "      <td>  relaxed</td>\n",
        "      <td> [[66, 60, 55], [162, 167, 137], [79, 87, 88], ...</td>\n",
        "      <td> [[ 66  60  55]\\n [162 167 137]\\n [ 79  87  88]...</td>\n",
        "      <td>  87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4</td>\n",
        "      <td> 172.719028</td>\n",
        "      <td> 149.399749</td>\n",
        "      <td> 75.392914</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-10/20/16/...</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 112</td>\n",
        "      <td> 1355</td>\n",
        "      <td>-0.354667</td>\n",
        "      <td>-0.154135</td>\n",
        "      <td> 0.386712</td>\n",
        "      <td>      sad</td>\n",
        "      <td> [[184, 112, 21], [255, 255, 255], [86, 44, 14]...</td>\n",
        "      <td> [[184 112  21]\\n [255 255 255]\\n [ 86  44  14]...</td>\n",
        "      <td> 112</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "   Unnamed: 0        mean      median        std  \\\n",
        "0           0  116.713054  116.353792  65.050715   \n",
        "1           1  150.766208  166.573191  72.552147   \n",
        "2           2  228.921814  253.943947  50.670328   \n",
        "3           3   72.998042   56.801021  48.041785   \n",
        "4           4  172.719028  149.399749  75.392914   \n",
        "\n",
        "                                                 url  Unnamed: 0.1  \\\n",
        "0  http://s3-ec.buzzfed.com/static/enhanced/webdr...             0   \n",
        "1  http://s3-ec.buzzfed.com/static/enhanced/webdr...             1   \n",
        "2  http://s3-ec.buzzfed.com/static/2014-01/enhanc...             2   \n",
        "3  http://s3-ec.buzzfed.com/static/enhanced/webdr...             3   \n",
        "4  http://s3-ec.buzzfed.com/static/2014-10/20/16/...             4   \n",
        "\n",
        "   Unnamed: 0.1   id  gif_id         x         y  strength   emotion  \\\n",
        "0             0  110    4057 -0.124000 -0.394737  0.413755     bored   \n",
        "1             1  109    6246  0.348000 -0.218045  0.410667    serene   \n",
        "2             2  114    7702  0.390667 -0.033835  0.392129  pleasant   \n",
        "3             3  113    6925  0.053333 -0.097744  0.111348   relaxed   \n",
        "4             4  112    1355 -0.354667 -0.154135  0.386712       sad   \n",
        "\n",
        "                                              colors  \\\n",
        "0  [[234, 232, 238], [111, 111, 117], [40, 37, 36...   \n",
        "1  [[203, 62, 50], [90, 144, 152], [248, 198, 97]...   \n",
        "2  [[252, 247, 217], [192, 4, 2], [248, 51, 18], ...   \n",
        "3  [[66, 60, 55], [162, 167, 137], [79, 87, 88], ...   \n",
        "4  [[184, 112, 21], [255, 255, 255], [86, 44, 14]...   \n",
        "\n",
        "                                          All Colors  Average Luminance  \n",
        "0  [[234 232 238]\\n [111 111 117]\\n [ 40  37  36]...                111  \n",
        "1  [[203  62  50]\\n [ 90 144 152]\\n [248 198  97]...                144  \n",
        "2  [[252 247 217]\\n [192   4   2]\\n [248  51  18]...                172  \n",
        "3  [[ 66  60  55]\\n [162 167 137]\\n [ 79  87  88]...                 87  \n",
        "4  [[184 112  21]\\n [255 255 255]\\n [ 86  44  14]...                112  "
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,10))\n",
      "emotion_strengths = []\n",
      "emotions = df['emotion'].unique()\n",
      "for emotion in emotions:\n",
      "    strengths = np.asarray(df[df['emotion']==emotion]['median'])\n",
      "    emotion_strengths.append(strengths)\n",
      "g = seaborn.violinplot(emotion_strengths, names=emotions)\n",
      "g.set_xticklabels(emotions,rotation='vertical')\n",
      "plt.title('Spread of GIF Average Luminances for each Emotion', size=30)\n",
      "plt.xlabel('Emotion', size=20)\n",
      "plt.ylabel('Average Primary Color Luminance', size=20)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4nU8Fnp8/3Yaeh74neBrlBPmPgG+KyB+Nub5g3C77rPFkykX5x9cQgO4MVJQA\neNKwcqOcvO4HvBB4UP783HFiYAjhSuCt+dM7AWePEzFFpCYijxCRV6wy7eOYZHuqWvd8YJh1j4jc\nkzK43jgm1V/sEfIAjMURhrcVkRcMXpOLg2cAN9uTaZtBfkS5aDxORO4zeEFe/89kD8YbCCF8i1LQ\nvRNw5ij3ABFJRGTY0cKvpLSO/JCIjN29F5HbicgbBxddInKKiPzumM/Vgb+qvHTxuO+ZAG+lDCj8\n9yLy2CFp2oT2gYV768fGnBI1USbYh06sbk6oPjlrID82uJi7Hgh8JBfYliEiD6U87a/N8rlgQXX+\nPHb+vUreR3ks6pNE5IVD0pUCb0Nj5AB8O4Qw9qhzZ+/DA4A6FpkDngk8U0QuR01Zv4cex9RDzSLv\nBzyG8til7ZS7I6P4NGqG9w0RORU1ieyhx1U+k1KJ/mgIYd27KyGEX4vIX6InJcwDp4rI3+TPL0cn\nSgegfo73QxfICXDuwK3eC7w8T9/xwHdF5P2oz/7N0WNDH4j6+f6AMaaGIYTTReRJwJ+gfrLfF5F3\nAxeiwR4fiO6idlDF/1GsPVL6IM8GvosGcPtL4F757/9TdPB9dJ4e8u96bn5qzYYTQrheRB6GnpBz\nJGrl8xURuRhdqP0E9dNO0AXZkaipbTVGwqSO2tsoqrtp41w/lpEflfZ5tGxuhpbT4I7JB4F/JhcG\nKC1rVvM9L0PdOB6Kumn8TEQ+BpyHtvU51Ff+HmidvjnaNv55tXkYwSTb03tETwI4Bq0TP8jb00Xo\n+PsAVDDroe2+mLzvEo19gv3F7pCISLX8xnFhpX98A/DH+eP/FD2N4nPoZPmuqCXcrVBB44kTSKdJ\nQghtETkZ+Bu0n/2qiLwHFdY66BHBJ6LleypaZ/YUT0PNum+HxpP5qYh8KE/bdjSg491R667bMrBJ\nFkK4SESeC7wzv/YzIvINVMT+BVoXDkZdCB9AuUga3DB4Furu9wPgy2jbuxFtp7dHx47CcuDH6EbG\nhhFCuEJEXgz8N9qWPyoiZ6HziK3oYv0Z6O8GGsNipTnIpFl3H7oBdXNd9WkGOFpEtrK6vvD7uYXe\ntHkpWgcE3VC4NC/DH6HBqR8G/AWapwx4db7RtIwQwo2iRyHfEzhGRN6BxhYrLDayFWKqDN5vh4ic\niI5hKTpGHI+23euA30EDkxeC/Dbcis8ZgosZjkUuRjvmGjp5WSl40A+Ap4cQvr/Cdd9GJ9XvIp84\nDbnmHEqRTTtcAAAgAElEQVRT+XUTQvi8iDwIXfTdHp1UjPMd3I5OlKr3+G2+YPoQKt4Mu8eV6MKs\nUL7HDcRPQE1hH44GXHrxwPs70d9GUDFjvb6Tv85dWj4J3Dm/77AF6QIqZIxzTyiYWEyBEMJPcxeB\n1wLPQQf/lcqpcGn6lxDC6ZNKy6QRkXtRThR+FUL48hpv8X5KoelEBsSMEMKvROQrqJtBEQRyO6sw\ntQ4hdETkT9BFzXPRM+mfwuj2lzEB4WgD2tNjgE+hlj0HAi8ZeH8nuuj5PVTMyBgRrX0S/cU6WK3V\ny6nk5RtC+FIugrwK/Y2eRHlkL6ho82pUkJ6mmLFSf7EnYpS8gnyRgIpVz83/FWTowvnfWb2YsZZ0\nD702F3TvB3wYXQgdCPx1/m+QoUci5qLeNaigcXNUaBtnvXYdegTyIBnaTn5vzGcvBo6b4BGnIwkh\nnJJbbr0Z7SuOy/8N8n30FJDBI6wHmWg9m2AfOrG6OYn6NGVeSNnvr8SLgf8c8d5G9Cmj2vCOfNz4\nBPAHqID8qiGXtoB/CCH825jveAVwNio+PCf/V2VN4lMI4TMi8jh03DgAnSs8eMilVwCPCSGEIe9V\nWXef59hj1hRPx1mREMIjUcX+megu6rdRU7Um2hlfj+70vwsNMnVUCOG7q7z3B1FLjHehlgE78/t9\nEXhSCOHPQgitMbeANVoq5KaXd0YnGB8BfoYuQtr5d38HOAUVGW4RQvjhkHt8Et0heR8aMbyFTggv\nQAete4QQLqykbWQaQwiL+W98AvC/eRp2or/HKcC9BwSF9VpmEEL4Cbqo/ivUx/qqPA83oFYh/wTc\nMYTwgZE3KdNS/JsYIYSFEMJLUGuV5wL/g+7+XYeW001ouX0aDZB5nxDC3VYQMlYsi1Vesx6eXrn/\nakSiQc5Cd0sy4BEicosh1xRWGEW5fCKEMGyhsgshhHYI4UXoru3rgW+hbb2Nils/QwXGlwF3DyEM\n89le82844fa0HZ2cPRMNyle0p8uBk4GjQwgfZvmJHjcwgkn0F2sk241/1fS+GrVW+hRlP/0rVCx6\nYAihavGxUhmtpgzXUt4rXbve91d9bb74fgS6e38e2q6WUOuFDwOPDCE8b5XfuZZ+cMVrQwjXhBCO\nQYXL96NjwQ60XVyNWkq8Gq2Xo+7xKXQ3/q9R4fqX6GkGTbRefBM94vlPgcNDCINt4FaoiP4+dEy4\nAbUMWETN3z+OCmX3CiH8chX5HsWa+osQwsmoAP+vqIXojWiefoP2TU8H7rlCmjasn59EHzrhurne\n+jTJ9r1aqvfZ7b5wyD1W+51rSd9QQghXhxD+ELXA+ATaBy+hYvclqCB31xWEDEIIn0WFyNPRurPI\n6Dyttj58ErVc/Ae0fl5HWRe+iIpHdw4hjHMdm0a9cGaEmValcn/Dr6BK8BwakOZlIvIadFC7Nr/0\n5SGEz+SfeRm6y9UFXhRC+PweT7hjBhF5MKVf92tCCK+dYnIcx9lLEZEL0d3Pm0III4OBOo7jOI7j\nOMpMu5mEEJZE5JgQwmIe3OnruTl6BrwphLAsaJqIHImqjkeiKv65IiJ5JHjHcRzHmTlys+t75k+/\nPMWkOI7jOI7jmGHm3UxCCIv5wznUR+vG/Pkwq5LjgDNys7pfoGa8993wRDqO4zjOEETkriIy0tIi\nF+ELF58MdT1xHMdxHMdxVmCmLTNAj4tC4x/cAXhHCOGHIvJ44IUicgLqw/ySEMJNwOHA+ZWPX4la\naDiO4zjONDgeeIWInIvGBbgC9Vc/DD0V5XjKsfija4kG7ziO4ziOszcz82JG7iJylIgcAHwuj3Hw\nDvRkAdDj/t6IBlcbhgd4cRzHcaZFhsZ9elT+b9Q1H6YMyOo4juM4juOswMyLGQUhhK0icg56ksKX\ni9dF5F3oMUGgR0rdpvKxW7PCUX3tdier19MJp9axwre//W1OOOEEkiTh+c9//kkveMELTpp2mhzH\niYfrr7+ec845h/PPP59f/vKX3HjjjWzbto1NmzZx6KGHcq973Yvjjz8+Ofroo5/IdI8ndRzHcRzH\nmTmSJBl5aMmsn2ZyCNAJIdwkIpuBzwEnAT8MIfw2v+bF6DGIT8p9j09H42TcCjgXPc5xpHXGNdds\nc8sNx3Ecx3Ecx3Ecx5kxDjts/5GaxaxbZtwSODWPm1ED3h9C+KKInCYiR6GmuT8HngMQQrhURD4C\nXIqeP/68cUKG4ziO4ziO4ziO4zj2mGnLjD2BW2Y4juM4juM4juM4zuwxzjJj5o9mdRzHcRzHcRzH\ncRzHqeJihuM4juM4juM4juM4pnAxw3Ecx3Ecx3Ecx3EcU7iY4TiO4ziO4ziO4ziOKVzMcBzHcRzH\ncRzHcRzHFC5mOI7jOI7jOI7jOI5jChczHMdxHMdxHMdxHMcxhYsZjuM4juM4juM4juOYwsUMx3Ec\nx3Ecx3Ecx3FM4WKG4ziO4ziO4ziO4zimcDHDcRzHcRzHcRzHcRxTuJjhOI7jOI7jOI7jOI4pXMxw\nHMdxHMdxHMdxHMcULmY4juM4juM4juM4jmMKFzMcx3Ecx3Ecx3EcxzGFixmO4ziO4ziO4ziO45jC\nxQzHcRzHcRzHcRzHcUzhYobjOI7jOI7jOI7jOKZwMcNxHMdxHMdxHMdxHFO4mOE4juM4juM4juM4\njilczHAcx3Ecx3Ecx3EcxxQuZjiO4ziO4ziO4ziOYwoXMxzHcRzHcRzHcRzHMYWLGY7jOI7jOI7j\nOI7jmMLFDMdxHMdxHMdxHMdxTOFihuM4juM4juM4juM4pnAxw3Ecx3Ecx3Ecx3EcU7iY4TiO4ziO\n4ziO4ziOKVzMcBzHcRzHcRzHcRzHFC5mOI7jOI7jOI7jOI5jChczHMdxHMdxHMdxHMcxhYsZjuM4\njuM4juM4juOYwsUMx3Ecx3Ecx3Ecx3FM4WKG4ziO4ziO4ziO4zimcDHDcRzHcZwN59/f8C+8733v\nmnYyHMdxHMeJBBczHMdxHMfZcC75wcV8+zvnTzsZjuM4juNEgosZjuM4juPsATIysmknwnEcx3Gc\nSHAxw3Ecx3Ecx3Ecx3EcU7iY4TiO4ziO4zjORPj0pz/JJz7xP9NOhuM4ewEuZjiO4ziOs/G4h4nj\n7BWcddYnOPvss6adDMdx9gJczHAcx3EcZ+NJpp0Ax3H2BBobx9VLx3E2HhczHMdxHMfZI7ie4TiO\n4zjOpHAxw3Ecx3GcjSfzvdpZotfrsbS0c9rJcBxng7n66t/ykpe8kG3btk47KY4zcVzMcBzHcRzH\n2cs455wzedaznsp1110z7aQ4jrOBnHfe17n66qu44IJvTzspjjNxXMxwHMdxnBnmq1/9MjfddNO0\nk+FExlVXXQXADTfcMOWUOI6zJ0gSd/Rz4sPFDMdxHMeZURYXFznllLdx2mnvnnZSHMdxHMeZANu2\nbXM3vwnhYobjOI7jzCidThuAZnNpyilxHMdZPR4fx3FG87cveQGvPemV005GFLiY4TiO4zgzSuYr\nAsdxHGcdZP2BxAeUWaHVarJjx/ZpJyMKXMxwHMdxnBnHfZ2dSZO5UuZsFF61Zoxk4K8zbbz7nRwu\nZjiO4zjOjFIsOKNZeEaSjbj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Z+td6P9zrL9Rs91mlGGO7jRQimfW2vtRsUm/kYobxth6L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jUx0Wyn86HKDKAiQnQlM5IoOmgz0bGqjOfVqooHKjOYZSN49fkbbNGbgVIj7SMzYFhgQmqnWtqG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R04toNaNdH/P5HJPZhF8yQ6EJM5dvnkVRBCEEcw7JjLmzgBQgObLT7yGn8qfMyPnXe9C1fJCV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xHAO1IykxM932PvnG5qeFY2rXWrZ3lLS97bMlnU9KzNRmpKSRjVWX8hTGNxSOqWW2P5F/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\nA+RfJttlr6fHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1160abc10>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unlike the previous violin plot, this violin plot is for the median luminance of *all GIF pixels* for each GIF, not just the luminances of the primary GIF colors. This gives us more interesting trends that corroborate what we saw before. The best example is the upset category, which has very low luminance and a small amount of spread, showing that upset GIFs are pretty dark."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"motion-analysis\"></a>\n",
      "## Motion Detection Analysis\n",
      "<img src='http://media.giphy.com/media/a7jgPGaFCMrGE/giphy.gif'/>\n",
      "<img src='http://media.giphy.com/media/WITkT2Cbwhrgs/giphy.gif'/>\n",
      "Another aspect of Image Analysis that we were interested in is motion detection of each GIF. We hypothesized that more active GIFs have more motiion in them. Using python motion detection packages, we tested this hypothesis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "from matplotlib import cm\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import urllib, cStringIO\n",
      "\n",
      "from PIL import Image\n",
      "from PIL import ImageChops\n",
      "from scipy.stats import zscore"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def image_intensity(img):\n",
      "    \"\"\"\n",
      "    Given an image, determine the intensity of each pixel\n",
      "    \"\"\"\n",
      "\n",
      "    hist = img.histogram()\n",
      "    hist = np.array(hist)\n",
      "\n",
      "    intensity = 0\n",
      "\n",
      "    for i, p in enumerate(hist):\n",
      "        intensity += np.log(i+1)*p\n",
      "\n",
      "    return intensity"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def motion_detection(gif_link):\n",
      "    \"\"\"\n",
      "    Given a link to a GIF, determine the amount of motion between frames\n",
      "    \"\"\"\n",
      "\n",
      "    file = cStringIO.StringIO(urllib.urlopen(gif_link).read())\n",
      "    gif = Image.open(file)\n",
      "\n",
      "    frame = 0\n",
      "    motion = 0\n",
      "    current_frame = gif.copy().convert('LA')\n",
      "\n",
      "    while True: \n",
      "        try:\n",
      "            previous_frame = current_frame\n",
      "\n",
      "            gif.seek(frame+1)\n",
      "            current_frame = gif.copy().convert('LA')\n",
      "\n",
      "            motion += image_intensity(ImageChops.difference(previous_frame, current_frame))\n",
      "\n",
      "        except EOFError:\n",
      "            break\n",
      "\n",
      "        frame += 1\n",
      "    if frame > 0:\n",
      "        return motion/(1.0*frame)\n",
      "    return 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Given the above functions, plot the relative motion of each GIF\n",
      "\"\"\"\n",
      "\n",
      "GIFs = pd.read_csv('data/crowdsourced_gif_emotions/gifs.csv', names=['id', 'gif_id', 'x', 'y'])\n",
      "links = pd.read_csv('data/crowdsourced_gif_emotions/links.csv', names=['gif_id', 'url'])\n",
      "\n",
      "data = GIFs.merge(links)\n",
      "\n",
      "motion_detection_pd = pd.read_csv(\"data/motion_detection.csv\", names=['url', 'motion'], header=0)\n",
      "\n",
      "master = data.merge(motion_detection_pd)\n",
      "master['motion'] = zscore(master['motion'])\n",
      "\n",
      "fig = plt.figure(figsize=(18,10))\n",
      "plt.title(\"Relative Motion in GIF\", size=30)\n",
      "plt.xlabel('Positivity', size=20)\n",
      "plt.ylabel('Arousal', size=20)\n",
      "plt.hexbin(master['x'], -1*master['y'] + 1.0, C = master['motion'], gridsize=50, cmap=cm.jet)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EEg0TyQWmy1Uhh8+U2Ew0n4g9WoLNtY1ssY9zMNCV9T1a4gVYbFfzrlg9Fyam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\nbtyIyPtnHzd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iN9JKKmA1cInFRaOD/9c1yQXoA4Ouxs+GIyTzwbbjNAXxSYPKCT1wHKuX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P990MemQyqDLhMgFVhM++w4DevamBU4TJNyCNnlMtsis4JKRyOudpBUcs/YT0XI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TPQ5r2yyODmeeKcKW4aKagJuaFPfM9QmdcFtKHmAg+hQjzkYCuy2j/LAppkUvpYtyhk5J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8bqV3ZRkTORVgOuY6CNYM43x5tHVf5WmjN/gKnDpy1akVCsSUq0hkFPkA3Yh+Y5kntrfD7dnqd9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UeD89TjuemT8VmgJMGcJWMTzDxzMn8xqb2QhDr8/i5f+9gje+v4hCLzGyAiruNUjPMBkzJttw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\nfmfcKiwmndG0QeAvgXLgXyzLes+yrC3jVF0xCY2yDQpxyYzyWryfoTex72T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       "text": [
        "<matplotlib.figure.Figure at 0x10b5ac310>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After this motion detection analysis, we unfortunately see that there is little correlation between the activity of a GIF (the \"arousal\" as defined in the NC State definition of the emotion spectrum) and the motion detected activity of the GIF."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"pos-tagging\"></a>\n",
      "\n",
      "## Parts of Speech Tagging\n",
      "<img src='http://media.giphy.com/media/1VqmDm4AHb5S0/giphy.gif'/>\n",
      "\n",
      "Next, we wanted to do Parts of Speech (POS) tagging on the title and text associated with each GIF in our dataset. Here, we use NLTK to do parts of speech tagging. We then use the parts of speech to analyze the content and sentiment of text.\n",
      "\n",
      "A reference for the UPenn Treebank Project parts of speech tags can be found here: http://www.ling.upenn.edu/courses/Fall_2007/ling001/penn_treebank_pos.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nltk\n",
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gif_data = pd.read_csv('data/buzzfeed_gifs_master.csv')\n",
      "texts = gif_data['Text'].fillna('')\n",
      "titles = gif_data['Title'].fillna('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Collect all of the tags for each gif-associated text\n",
      "\"\"\"\n",
      "all_text_tags = []\n",
      "for text in texts:\n",
      "    all_text_tags.append(nltk.pos_tag(nltk.word_tokenize(text)))\n",
      "    \n",
      "\"\"\"\n",
      "Collect all of the tags for each gif-associated title\n",
      "\"\"\"\n",
      "all_title_tags = []\n",
      "for title in titles:\n",
      "    all_title_tags.append(nltk.pos_tag(nltk.word_tokenize(title)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Extract a certain part of speech from a list of tags\n",
      "\"\"\"\n",
      "pos_map = {\n",
      "    'noun': 'N',\n",
      "    'verb': 'V',\n",
      "    'adverb': 'R',\n",
      "    'adjective': 'J'\n",
      "}\n",
      "\n",
      "def get_pos(text_tags, pos):\n",
      "    result = []\n",
      "    c = pos_map[pos]\n",
      "    for tags in text_tags:\n",
      "        if tags[1][0] == c:\n",
      "            result.append(tags[0])\n",
      "    return result\n",
      "\n",
      "# For example\n",
      "a = all_tags[0]\n",
      "get_pos(a, 'noun')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Finally, here, we grab the noun, verb, adjective, and adverb parts of speech tags for each text and title for\n",
      "each GIF and save it to our DataFrame\n",
      "\"\"\"\n",
      "\n",
      "nouns = []\n",
      "adverbs = []\n",
      "adjectives = []\n",
      "verbs = []\n",
      "\n",
      "for tags in all_text_tags:\n",
      "    nouns.append(get_pos(tags, 'noun'))\n",
      "    adverbs.append(get_pos(tags, 'adverb'))\n",
      "    adjectives.append(get_pos(tags, 'adjective'))\n",
      "    verbs.append(get_pos(tags, 'verb'))\n",
      "    \n",
      "gif_data['text verbs'] = verbs\n",
      "gif_data['text adverbs'] = adverbs\n",
      "gif_data['text adjectives'] = adjectives\n",
      "gif_data['text nouns'] = nouns\n",
      "gif_data['all text tags'] = all_text_tags\n",
      "\n",
      "nouns = []\n",
      "adverbs = []\n",
      "adjectives = []\n",
      "verbs = []\n",
      "\n",
      "for tags in all_title_tags:\n",
      "    nouns.append(get_pos(tags, 'noun'))\n",
      "    adverbs.append(get_pos(tags, 'adverb'))\n",
      "    adjectives.append(get_pos(tags, 'adjective'))\n",
      "    verbs.append(get_pos(tags, 'verb'))\n",
      "    \n",
      "gif_data['title verbs'] = verbs\n",
      "gif_data['title adverbs'] = adverbs\n",
      "gif_data['title adjectives'] = adjectives\n",
      "gif_data['title nouns'] = nouns\n",
      "gif_data['all title tags'] = all_title_tags"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Our final dataframe\n",
      "\"\"\"\n",
      "df = pd.read_csv('data/pos_tagged_gif_data.csv')\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
        "      <th>Text</th>\n",
        "      <th>Title</th>\n",
        "      <th>URL</th>\n",
        "      <th>text verbs</th>\n",
        "      <th>text adverbs</th>\n",
        "      <th>text adjectives</th>\n",
        "      <th>text nouns</th>\n",
        "      <th>all text tags</th>\n",
        "      <th>title verbs</th>\n",
        "      <th>title adverbs</th>\n",
        "      <th>title adjectives</th>\n",
        "      <th>title nouns</th>\n",
        "      <th>all title tags</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
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        "      <td> 1. Because I just wasn\u2019t one of those people w...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>               ['wanted', 'do']</td>\n",
        "      <td> ['just', 'exactly']</td>\n",
        "      <td>               ['wasn\\xe2\\x80\\x99t', 'knew']</td>\n",
        "      <td>               ['people', 'lives', 'age']</td>\n",
        "      <td> [('1.', 'CD'), ('Because', 'IN'), ('I', 'PRP')...</td>\n",
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        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2. Because there are SO many options to choose...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>              ['are', 'choose']</td>\n",
        "      <td>              ['SO']</td>\n",
        "      <td>                                    ['many']</td>\n",
        "      <td>                              ['options']</td>\n",
        "      <td> [('2.', 'CD'), ('Because', 'IN'), ('there', 'E...</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3. Some days you hate every subject in the wor...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> ['hate', 'decide', 'destined']</td>\n",
        "      <td>            ['just']</td>\n",
        "      <td>                       ['you\\xe2\\x80\\x99re']</td>\n",
        "      <td>  ['days', 'subject', 'world', 'failure']</td>\n",
        "      <td> [('3.', 'CD'), ('Some', 'DT'), ('days', 'NNS')...</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 4. And other days you\u2019re super ambitious and t...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>                ['think', 'do']</td>\n",
        "      <td>                  []</td>\n",
        "      <td> ['other', 'you\\xe2\\x80\\x99re', 'ambitious']</td>\n",
        "      <td> ['days', 'super', 'everything', 'world']</td>\n",
        "      <td> [('4.', 'CD'), ('And', 'CC'), ('other', 'JJ'),...</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 5. But either way you know that you don\u2019t know...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>              ['know', 'doing']</td>\n",
        "      <td>                  []</td>\n",
        "      <td>   ['don\\xe2\\x80\\x99t', 'you\\xe2\\x80\\x99re']</td>\n",
        "      <td>                  ['way', 'know', 'life']</td>\n",
        "      <td> [('5.', 'CD'), ('But', 'CC'), ('either', 'DT')...</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> []</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "   Unnamed: 0  Unnamed: 0.1  \\\n",
        "0           0             0   \n",
        "1           1             1   \n",
        "2           2             2   \n",
        "3           3             3   \n",
        "4           4             4   \n",
        "\n",
        "                                                Text  \\\n",
        "0  1. Because I just wasn\u2019t one of those people w...   \n",
        "1  2. Because there are SO many options to choose...   \n",
        "2  3. Some days you hate every subject in the wor...   \n",
        "3  4. And other days you\u2019re super ambitious and t...   \n",
        "4  5. But either way you know that you don\u2019t know...   \n",
        "\n",
        "                                               Title  \\\n",
        "0  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "1  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "2  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "3  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "4  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "\n",
        "                                                 URL  \\\n",
        "0  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "1  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "2  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "3  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "4  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "\n",
        "                       text verbs         text adverbs  \\\n",
        "0                ['wanted', 'do']  ['just', 'exactly']   \n",
        "1               ['are', 'choose']               ['SO']   \n",
        "2  ['hate', 'decide', 'destined']             ['just']   \n",
        "3                 ['think', 'do']                   []   \n",
        "4               ['know', 'doing']                   []   \n",
        "\n",
        "                               text adjectives  \\\n",
        "0                ['wasn\\xe2\\x80\\x99t', 'knew']   \n",
        "1                                     ['many']   \n",
        "2                        ['you\\xe2\\x80\\x99re']   \n",
        "3  ['other', 'you\\xe2\\x80\\x99re', 'ambitious']   \n",
        "4    ['don\\xe2\\x80\\x99t', 'you\\xe2\\x80\\x99re']   \n",
        "\n",
        "                                 text nouns  \\\n",
        "0                ['people', 'lives', 'age']   \n",
        "1                               ['options']   \n",
        "2   ['days', 'subject', 'world', 'failure']   \n",
        "3  ['days', 'super', 'everything', 'world']   \n",
        "4                   ['way', 'know', 'life']   \n",
        "\n",
        "                                       all text tags title verbs  \\\n",
        "0  [('1.', 'CD'), ('Because', 'IN'), ('I', 'PRP')...          []   \n",
        "1  [('2.', 'CD'), ('Because', 'IN'), ('there', 'E...          []   \n",
        "2  [('3.', 'CD'), ('Some', 'DT'), ('days', 'NNS')...          []   \n",
        "3  [('4.', 'CD'), ('And', 'CC'), ('other', 'JJ'),...          []   \n",
        "4  [('5.', 'CD'), ('But', 'CC'), ('either', 'DT')...          []   \n",
        "\n",
        "  title adverbs title adjectives  \\\n",
        "0            []               []   \n",
        "1            []               []   \n",
        "2            []               []   \n",
        "3            []               []   \n",
        "4            []               []   \n",
        "\n",
        "                                         title nouns  \\\n",
        "0  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "1  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "2  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "3  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "4  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "\n",
        "                                      all title tags  \n",
        "0  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...  \n",
        "1  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...  \n",
        "2  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...  \n",
        "3  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...  \n",
        "4  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...  "
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Above we can see our final dataframe, which containst the extracted nouns, verbs, adjectives, and adverbs for the text and title of each GIF."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"sentiment-analysis\"></a>\n",
      "\n",
      "#POS-Based Sentiment Analysis\n",
      "<img src='http://media.giphy.com/media/29eb69yzEuO8o/giphy.gif'/>\n",
      "\n",
      "Once we had all of our crowdsourced data that associates GIFs with emotions on the spectrum, we set out to create a sentiment analysis algorithm that can take an input string and associate it with an emotion. The first step is parts of speech tagging, which we did in the previous section.\n",
      "\n",
      "Then, by combining these parts of speech, we trained an SVM linear classifier to classify a tokenized version of these parts of speech into an emotional category."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nltk\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from IPython import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Prepare our dataframe\n",
      "A combination of the parts of speech tagging and the cateogrized emotions dataframes\n",
      "\"\"\"\n",
      "\n",
      "data1 = pd.read_csv('data/pos_tagged_gif_data.csv')\n",
      "data2 = pd.read_csv('data/categorized_emotions.csv')\n",
      "data = data1.merge(data2, left_on='URL', right_on='url')\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0_x</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
        "      <th>Text</th>\n",
        "      <th>Title</th>\n",
        "      <th>URL</th>\n",
        "      <th>text verbs</th>\n",
        "      <th>text adverbs</th>\n",
        "      <th>text adjectives</th>\n",
        "      <th>text nouns</th>\n",
        "      <th>all text tags</th>\n",
        "      <th>...</th>\n",
        "      <th>title nouns</th>\n",
        "      <th>all title tags</th>\n",
        "      <th>Unnamed: 0_y</th>\n",
        "      <th>id</th>\n",
        "      <th>gif_id</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>url</th>\n",
        "      <th>strength</th>\n",
        "      <th>emotion</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  2</td>\n",
        "      <td>  2</td>\n",
        "      <td> 3. Some days you hate every subject in the wor...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>           ['hate', 'decide', 'destined']</td>\n",
        "      <td>         ['just']</td>\n",
        "      <td>                            ['you\\xe2\\x80\\x99re']</td>\n",
        "      <td>           ['days', 'subject', 'world', 'failure']</td>\n",
        "      <td> [('3.', 'CD'), ('Some', 'DT'), ('days', 'NNS')...</td>\n",
        "      <td>...</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "      <td>  830</td>\n",
        "      <td>  714</td>\n",
        "      <td>  4</td>\n",
        "      <td>-0.418667</td>\n",
        "      <td> 0.142857</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 0.442369</td>\n",
        "      <td>     upset</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  3</td>\n",
        "      <td>  3</td>\n",
        "      <td> 4. And other days you\u2019re super ambitious and t...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>                          ['think', 'do']</td>\n",
        "      <td>               []</td>\n",
        "      <td>      ['other', 'you\\xe2\\x80\\x99re', 'ambitious']</td>\n",
        "      <td>          ['days', 'super', 'everything', 'world']</td>\n",
        "      <td> [('4.', 'CD'), ('And', 'CC'), ('other', 'JJ'),...</td>\n",
        "      <td>...</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "      <td> 1215</td>\n",
        "      <td> 1038</td>\n",
        "      <td>  5</td>\n",
        "      <td>-0.144000</td>\n",
        "      <td> 0.169173</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 0.222161</td>\n",
        "      <td>   nervous</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  7</td>\n",
        "      <td>  7</td>\n",
        "      <td> 8. Every time you speak to an adult they\u2019re li...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> ['speak', 'supposed', 'have', 'figured']</td>\n",
        "      <td>   ['out', 'now']</td>\n",
        "      <td> ['\\xe2\\x80\\x9cso', 'major', 'you\\xe2\\x80\\x99re']</td>\n",
        "      <td> ['Every', 'time', 'adult', 'they\\xe2\\x80\\x99re...</td>\n",
        "      <td> [('8.', 'CD'), ('Every', 'NNP'), ('time', 'NN'...</td>\n",
        "      <td>...</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "      <td>  292</td>\n",
        "      <td>  319</td>\n",
        "      <td>  9</td>\n",
        "      <td> 0.148611</td>\n",
        "      <td>-0.023438</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 0.150448</td>\n",
        "      <td>  pleasant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  7</td>\n",
        "      <td>  7</td>\n",
        "      <td> 8. Every time you speak to an adult they\u2019re li...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> ['speak', 'supposed', 'have', 'figured']</td>\n",
        "      <td>   ['out', 'now']</td>\n",
        "      <td> ['\\xe2\\x80\\x9cso', 'major', 'you\\xe2\\x80\\x99re']</td>\n",
        "      <td> ['Every', 'time', 'adult', 'they\\xe2\\x80\\x99re...</td>\n",
        "      <td> [('8.', 'CD'), ('Every', 'NNP'), ('time', 'NN'...</td>\n",
        "      <td>...</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "      <td>  293</td>\n",
        "      <td> 2611</td>\n",
        "      <td>  9</td>\n",
        "      <td>-0.355172</td>\n",
        "      <td> 0.199029</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 0.407136</td>\n",
        "      <td>  stressed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 14</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15. And you\u2019re just like, \u201cUhhhh well okay then.\u201d</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>                                       []</td>\n",
        "      <td> ['just', 'well']</td>\n",
        "      <td>       ['you\\xe2\\x80\\x99re', '\\xe2\\x80\\x9cUhhhh']</td>\n",
        "      <td>                                          ['okay']</td>\n",
        "      <td> [('15.', 'CD'), ('And', 'CC'), ('you\\xe2\\x80\\x...</td>\n",
        "      <td>...</td>\n",
        "      <td> ['Reasons', 'Choosing', 'College', 'Major', 'I...</td>\n",
        "      <td> [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...</td>\n",
        "      <td>  981</td>\n",
        "      <td>  842</td>\n",
        "      <td> 16</td>\n",
        "      <td> 0.434360</td>\n",
        "      <td>-0.125571</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 0.452147</td>\n",
        "      <td> contented</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 23 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "   Unnamed: 0_x  Unnamed: 0.1  \\\n",
        "0             2             2   \n",
        "1             3             3   \n",
        "2             7             7   \n",
        "3             7             7   \n",
        "4            14            14   \n",
        "\n",
        "                                                Text  \\\n",
        "0  3. Some days you hate every subject in the wor...   \n",
        "1  4. And other days you\u2019re super ambitious and t...   \n",
        "2  8. Every time you speak to an adult they\u2019re li...   \n",
        "3  8. Every time you speak to an adult they\u2019re li...   \n",
        "4  15. And you\u2019re just like, \u201cUhhhh well okay then.\u201d   \n",
        "\n",
        "                                               Title  \\\n",
        "0  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "1  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "2  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "3  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "4  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "\n",
        "                                                 URL  \\\n",
        "0  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "1  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "2  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "3  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "4  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   \n",
        "\n",
        "                                 text verbs      text adverbs  \\\n",
        "0            ['hate', 'decide', 'destined']          ['just']   \n",
        "1                           ['think', 'do']                []   \n",
        "2  ['speak', 'supposed', 'have', 'figured']    ['out', 'now']   \n",
        "3  ['speak', 'supposed', 'have', 'figured']    ['out', 'now']   \n",
        "4                                        []  ['just', 'well']   \n",
        "\n",
        "                                    text adjectives  \\\n",
        "0                             ['you\\xe2\\x80\\x99re']   \n",
        "1       ['other', 'you\\xe2\\x80\\x99re', 'ambitious']   \n",
        "2  ['\\xe2\\x80\\x9cso', 'major', 'you\\xe2\\x80\\x99re']   \n",
        "3  ['\\xe2\\x80\\x9cso', 'major', 'you\\xe2\\x80\\x99re']   \n",
        "4        ['you\\xe2\\x80\\x99re', '\\xe2\\x80\\x9cUhhhh']   \n",
        "\n",
        "                                          text nouns  \\\n",
        "0            ['days', 'subject', 'world', 'failure']   \n",
        "1           ['days', 'super', 'everything', 'world']   \n",
        "2  ['Every', 'time', 'adult', 'they\\xe2\\x80\\x99re...   \n",
        "3  ['Every', 'time', 'adult', 'they\\xe2\\x80\\x99re...   \n",
        "4                                           ['okay']   \n",
        "\n",
        "                                       all text tags  \\\n",
        "0  [('3.', 'CD'), ('Some', 'DT'), ('days', 'NNS')...   \n",
        "1  [('4.', 'CD'), ('And', 'CC'), ('other', 'JJ'),...   \n",
        "2  [('8.', 'CD'), ('Every', 'NNP'), ('time', 'NN'...   \n",
        "3  [('8.', 'CD'), ('Every', 'NNP'), ('time', 'NN'...   \n",
        "4  [('15.', 'CD'), ('And', 'CC'), ('you\\xe2\\x80\\x...   \n",
        "\n",
        "                         ...                          \\\n",
        "0                        ...                           \n",
        "1                        ...                           \n",
        "2                        ...                           \n",
        "3                        ...                           \n",
        "4                        ...                           \n",
        "\n",
        "                                         title nouns  \\\n",
        "0  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "1  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "2  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "3  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "4  ['Reasons', 'Choosing', 'College', 'Major', 'I...   \n",
        "\n",
        "                                      all title tags Unnamed: 0_y    id  \\\n",
        "0  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...          830   714   \n",
        "1  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...         1215  1038   \n",
        "2  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...          292   319   \n",
        "3  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...          293  2611   \n",
        "4  [('18', 'CD'), ('Reasons', 'NNS'), ('Why', 'WR...          981   842   \n",
        "\n",
        "  gif_id         x         y  \\\n",
        "0      4 -0.418667  0.142857   \n",
        "1      5 -0.144000  0.169173   \n",
        "2      9  0.148611 -0.023438   \n",
        "3      9 -0.355172  0.199029   \n",
        "4     16  0.434360 -0.125571   \n",
        "\n",
        "                                                 url  strength    emotion  \n",
        "0  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  0.442369      upset  \n",
        "1  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  0.222161    nervous  \n",
        "2  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  0.150448   pleasant  \n",
        "3  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  0.407136   stressed  \n",
        "4  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  0.452147  contented  \n",
        "\n",
        "[5 rows x 23 columns]"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Extract and combine each of the parts of speech for the text and titles\n",
      "Then tokenize these strings using the sklearn CountVectorizer extraction transform\n",
      "\"\"\"\n",
      "\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "count_vect = CountVectorizer()\n",
      "\n",
      "verbs = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['text verbs']]\n",
      "nouns = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['text nouns']]\n",
      "adverbs = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['text adverbs']]\n",
      "adjectives = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['text adjectives']]\n",
      "\n",
      "title_verbs = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['title verbs']]\n",
      "title_nouns = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['title nouns']]\n",
      "title_adverbs = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['title adverbs']]\n",
      "title_adjectives = [x.replace('[', '').replace(\"'\", '').replace(',', '').replace(']', '') for x in data['title adjectives']]\n",
      "\n",
      "X = [verbs[i] + ' ' + nouns[i] + ' ' + title_verbs[i] + ' ' + title_nouns[i] + ' ' + title_adjectives[i] + ' ' + title_adverbs[i] + ' ' + adverbs[i] + ' ' + adjectives[i] for i in range(len(verbs))]\n",
      "\n",
      "X_train_counts = count_vect.fit_transform(X)\n",
      "X_train_counts.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "(4000, 5487)"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Fit the tokenized parts of speech training data using the SGDClassifier\n",
      "\"\"\"\n",
      "\n",
      "from sklearn.linear_model import SGDClassifier \n",
      "Y = np.asarray(data['emotion'], dtype=\"|S\")\n",
      "clf = SGDClassifier(n_iter=100)\n",
      "clf.fit(X_train_counts, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,\n",
        "       fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',\n",
        "       loss='hinge', n_iter=100, n_jobs=1, penalty='l2', power_t=0.5,\n",
        "       random_state=None, rho=None, shuffle=False, verbose=0,\n",
        "       warm_start=False)"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once we had this SGDClassifier trained with our training data, we wanted to, as a proof of concept, see if we could use this naive sentiment analysis to recommend GIFs. We did this by taking in a given input string, classifying the string using our classifier, and then randomly getting a GIF from that emotional category:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Given an input piece of text\n",
      "Classify it using our fit SGDClassifier from above\n",
      "Then choose a random GIF from that emotional class\n",
      "\"\"\"\n",
      "\n",
      "def get_relavent_gif(text):\n",
      "    doc = count_vect.transform([text])\n",
      "    predicted = clf.predict(doc)[0]\n",
      "    print text\n",
      "    print 'Predicted emotion: ' + str(predicted)\n",
      "    \n",
      "    filtered_urls = data[data['emotion'] == predicted]['url']\n",
      "    i = np.random.choice(filtered_urls.index, 1)[0]\n",
      "    url= filtered_urls.ix[i]\n",
      "    display.display(display.Image(url=url))\n",
      "\n",
      "get_relavent_gif('these are nice lights')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "these are nice lights\n",
        "Predicted emotion: excited\n"
       ]
      },
      {
       "html": [
        "<img src=\"http://s3-ec.buzzfed.com/static/enhanced/webdr01/2013/7/22/15/anigif_enhanced-buzz-32078-1374521997-0.gif\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Image at 0x1193ecd50>"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By playing around with the very rudimentary gif recommender above, we saw that we got relatively good results, though we had lots of weird results as well. We aimed to fix this by adding a dictionary based sentiment analysis and content analysis, as done in the next sections."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"dictionary-analysis\"></a>\n",
      "\n",
      "#Dictionary-Based Sentiment Analysis\n",
      "\n",
      "What we realized from the parts of speech based sentiment analysis from above is that our dataset was too small to train an accurate sentiment analysis engine that could robustly place text into one of so many different categories. To account for this, we decided to use dictionary-based sentiment analysis so that our training data would be much larger, hopefully leading to a more accurate sentiment classifier.\n",
      "\n",
      "The dictionary we use is from the Affective Norms for English Words (ANEW) dictionary, which can be found [here](http://personal.stevens.edu/~rchen/readings/anew.pdf). More information about this dictionary is found [here](http://csea.phhp.ufl.edu/media/anewmessage.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import hypot\n",
      "from matplotlib import pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import random\n",
      "from IPython import display\n",
      "from IPython.display import Image as PyImage"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Prepare our master DataFrame\n",
      "Combination of the GIF data, the links data, and the GIF emotion data\n",
      "\"\"\"\n",
      "\n",
      "gif_data = pd.read_csv('data/buzzfeed_gifs_master.csv')\n",
      "GIFs = pd.read_csv('data/crowdsourced_gif_emotions/gifs.csv', names=['id', 'gif_id', 'x', 'y'])\n",
      "links = pd.read_csv('data/crowdsourced_gif_emotions/links.csv', names=['gif_id', 'url'])\n",
      "\n",
      "master = pd.merge(gif_data, links, how='right', left_on='URL', right_on='url')\n",
      "\n",
      "master = master.merge(GIFs)\n",
      "master['GIF_x'] = master.apply(lambda gif: (gif['x']-0.5),axis=1)\n",
      "master['GIF_y'] = master.apply(lambda gif: (-1.0*gif['y']+0.5),axis=1)\n",
      "\n",
      "master.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>Text</th>\n",
        "      <th>Title</th>\n",
        "      <th>URL</th>\n",
        "      <th>gif_id</th>\n",
        "      <th>url</th>\n",
        "      <th>id</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>GIF_x</th>\n",
        "      <th>GIF_y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  2</td>\n",
        "      <td> 3. Some days you hate every subject in the wor...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  4</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  714</td>\n",
        "      <td> 0.081333</td>\n",
        "      <td> 0.357143</td>\n",
        "      <td>-0.418667</td>\n",
        "      <td> 0.142857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  3</td>\n",
        "      <td> 4. And other days you\u2019re super ambitious and t...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  5</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 1038</td>\n",
        "      <td> 0.356000</td>\n",
        "      <td> 0.330827</td>\n",
        "      <td>-0.144000</td>\n",
        "      <td> 0.169173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  7</td>\n",
        "      <td> 8. Every time you speak to an adult they\u2019re li...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  9</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  319</td>\n",
        "      <td> 0.648611</td>\n",
        "      <td> 0.523438</td>\n",
        "      <td> 0.148611</td>\n",
        "      <td>-0.023438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  7</td>\n",
        "      <td> 8. Every time you speak to an adult they\u2019re li...</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  9</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 2611</td>\n",
        "      <td> 0.144828</td>\n",
        "      <td> 0.300971</td>\n",
        "      <td>-0.355172</td>\n",
        "      <td> 0.199029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 14</td>\n",
        "      <td> 15. And you\u2019re just like, \u201cUhhhh well okay then.\u201d</td>\n",
        "      <td> 18 Reasons Why Choosing A College Major Is Imp...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td> 16</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-05/enhanc...</td>\n",
        "      <td>  842</td>\n",
        "      <td> 0.934360</td>\n",
        "      <td> 0.625571</td>\n",
        "      <td> 0.434360</td>\n",
        "      <td>-0.125571</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "   Unnamed: 0                                               Text  \\\n",
        "0           2  3. Some days you hate every subject in the wor...   \n",
        "1           3  4. And other days you\u2019re super ambitious and t...   \n",
        "2           7  8. Every time you speak to an adult they\u2019re li...   \n",
        "3           7  8. Every time you speak to an adult they\u2019re li...   \n",
        "4          14  15. And you\u2019re just like, \u201cUhhhh well okay then.\u201d   \n",
        "\n",
        "                                               Title  \\\n",
        "0  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "1  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "2  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "3  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "4  18 Reasons Why Choosing A College Major Is Imp...   \n",
        "\n",
        "                                                 URL  gif_id  \\\n",
        "0  http://s3-ec.buzzfed.com/static/2014-05/enhanc...       4   \n",
        "1  http://s3-ec.buzzfed.com/static/2014-05/enhanc...       5   \n",
        "2  http://s3-ec.buzzfed.com/static/2014-05/enhanc...       9   \n",
        "3  http://s3-ec.buzzfed.com/static/2014-05/enhanc...       9   \n",
        "4  http://s3-ec.buzzfed.com/static/2014-05/enhanc...      16   \n",
        "\n",
        "                                                 url    id         x  \\\n",
        "0  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   714  0.081333   \n",
        "1  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  1038  0.356000   \n",
        "2  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   319  0.648611   \n",
        "3  http://s3-ec.buzzfed.com/static/2014-05/enhanc...  2611  0.144828   \n",
        "4  http://s3-ec.buzzfed.com/static/2014-05/enhanc...   842  0.934360   \n",
        "\n",
        "          y     GIF_x     GIF_y  \n",
        "0  0.357143 -0.418667  0.142857  \n",
        "1  0.330827 -0.144000  0.169173  \n",
        "2  0.523438  0.148611 -0.023438  \n",
        "3  0.300971 -0.355172  0.199029  \n",
        "4  0.625571  0.434360 -0.125571  "
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Read in the sentiment dictionary and calculate the sentiment means\n",
      "\"\"\"\n",
      "\n",
      "sentiment_dict = pd.read_csv(\"data/sentiment-dictionary.csv\", index_col='Word')\n",
      "sentiments = sentiment_dict[['V.Mean.Sum', 'A.Mean.Sum', 'V.SD.Sum', 'A.SD.Sum']]\n",
      "\n",
      "vm = sentiments['V.Mean.Sum']\n",
      "vm = (vm - vm.mean()) / (vm.max() - vm.min())\n",
      "sentiments['valence_mean'] = vm\n",
      "\n",
      "am = sentiments['A.Mean.Sum']\n",
      "am = (am - am.mean()) / (am.max() - am.min())\n",
      "sentiments['arousal_mean'] = am\n",
      "\n",
      "\n",
      "def estimate_word_sentiment(word):\n",
      "    \"\"\"\n",
      "    Given a word, estimate the valence and arousal of the word \n",
      "    (placing it on the x,y coordinate plane)\n",
      "    \"\"\"\n",
      "    valence_mean = sentiments.ix[word]['valence_mean']\n",
      "    valence_std = sentiments.ix[word]['V.SD.Sum']\n",
      "    \n",
      "    arousal_mean = sentiments.ix[word]['arousal_mean']\n",
      "    arousal_std = sentiments.ix[word]['A.SD.Sum']\n",
      "    \n",
      "    return valence_mean, arousal_mean, valence_std,  arousal_std\n",
      "    \n",
      "def estimate_phrase_sentiment(phrase):\n",
      "    \"\"\"\n",
      "    Given a phrase, estimate the sentiment of the phrase\n",
      "    Based on the aggregation of word sentiments\n",
      "    \"\"\"\n",
      "    \n",
      "    words = phrase.split(' ')\n",
      "    \n",
      "    total_vm = 0.0\n",
      "    total_am = 0.0\n",
      "    total_vsd = 0.0\n",
      "    total_asd = 0.0\n",
      "    count = 0\n",
      "    \n",
      "    for word in words:\n",
      "        if word in sentiments.index:\n",
      "            vm, am, vsd,  asd = estimate_word_sentiment(word)\n",
      "            total_vm += vm\n",
      "            total_am += am\n",
      "            total_vsd += vsd\n",
      "            total_asd += asd\n",
      "            count += 1\n",
      "    \n",
      "    if count == 0:\n",
      "        return False\n",
      "    \n",
      "    return (total_vm/count, total_am/count, total_vsd/count, total_asd/count)\n",
      "\n",
      "def extract_points_to_plot(text_col):\n",
      "    \"\"\"\n",
      "    Given text, do the sentiment analysis above\n",
      "    Then place the sentiment on the x,y sentiment plane.\n",
      "    \"\"\"\n",
      "    \n",
      "    xs = []\n",
      "    ys = []\n",
      "    \n",
      "    for text in text_col:\n",
      "        \n",
      "        sentiment = estimate_phrase_sentiment(text)\n",
      "        \n",
      "        if sentiment:\n",
      "            xs.append(sentiment[0])\n",
      "            ys.append(sentiment[1])\n",
      "        else:\n",
      "            xs.append(None)\n",
      "            ys.append(None)\n",
      "            \n",
      "    return xs, ys\n",
      "\n",
      "xs, ys = extract_points_to_plot(master['Text'].fillna(''))\n",
      "master['text_x'] = xs\n",
      "master['text_y'] = ys"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We found that this dictionary based sentiment analysis was much better at placing text into a sentiment class, especially when there are many sentiments to consider."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"content-analysis\"></a>\n",
      "\n",
      "#Content Analysis\n",
      "\n",
      "To further refine the results from our sentiment classifier shown above, we decided to add a filter that ensures that a GIF also has content that is relavent to the input text, not just a similar sentiment. To make sure that we took full advantage of the small amoutn of training data we have (the text and titles associated with each GIF), we used a few techniques to more easily match input text with similar content tags on GIFs. \n",
      "\n",
      "First, we tokenized each GIF tag to make sure that punctuation, irregular spacing, and capitalization did not hinder our ability to match a tag with content in our GIF corpus. We bolstered this by filtering out \"stopwords\", as defined by the NLTK corpus of English stopwords (e.g. articles or single words like \"the\" or \"a\").\n",
      "\n",
      "Second, and more importantly, we used stemming (based on the NLTK snowball stemmer to "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nltk.corpus\n",
      "import nltk.tokenize.punkt\n",
      "import string\n",
      "import re"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a set of stopwords based on the default NLTK english stopwords\n",
      "# Be sure to also include punctuation and the empty string\n",
      "stopwords = nltk.corpus.stopwords.words('english')\n",
      "stopwords.extend(string.punctuation)\n",
      "stopwords.append('')\n",
      "\n",
      "# Instantiate a tokenizer that tokenizes a given string\n",
      "# Also instantiate the NLTK stemmer\n",
      "tokenizer = nltk.tokenize.punkt.PunktWordTokenizer()\n",
      "stemmer = nltk.stem.snowball.SnowballStemmer('english')\n",
      " \n",
      "def check_matching_tokens(a, b):\n",
      "    \"\"\"\n",
      "    A fuzzy matcher that sees if significant word stems match between two strings.\n",
      "    Inspired by the examples here: http://bommaritollc.com/2014/06/fuzzy-match-sentences-python/\n",
      "    \"\"\"\n",
      "    \n",
      "    tokens_a = [token.lower().strip(string.punctuation) for token in tokenizer.tokenize(a) if token.lower().strip(string.punctuation) not in stopwords]\n",
      "    stems_a = [stemmer.stem(token) for token in tokens_a]\n",
      "    \n",
      "    tokens_b = [token.lower().strip(string.punctuation) for token in tokenizer.tokenize(b) if token.lower().strip(string.punctuation) not in stopwords]\n",
      "    stems_b = [stemmer.stem(token) for token in tokens_b]\n",
      "    \n",
      "    match = set(stems_a) & set(stems_b)\n",
      "    return len(match)\n",
      "\n",
      "\n",
      "def find_matching_gifs_by_content(input_string, data=data, output=True):\n",
      "    \"\"\"\n",
      "    Given an input string and a master dataframe (data)\n",
      "    Find the GIFs with matching tokenized content stems (ordered by number of matches)\n",
      "    \"\"\"\n",
      "    \n",
      "    matches = []\n",
      "    \n",
      "    # Iterate over each text and\n",
      "    texts = data['Text'].fillna('')\n",
      "    titles = data['Title'].fillna('')\n",
      "    \n",
      "    combined_text = [texts[i] + ' ' + titles[i] for i in range(len(data))]\n",
      "    for text in combined_text:\n",
      "        text = re.sub(r'[^\\x00-\\x7F]+',' ', text)\n",
      "        n_matches = check_matching_tokens(text, input_string)\n",
      "        matches.append({'Match': True if n_matches > 0 else False, 'n': n_matches })\n",
      "\n",
      "    match_table = pd.DataFrame(matches)\n",
      "    df = data.copy()\n",
      "    df['Match'] = match_table['Match']\n",
      "    df['N Matches'] = match_table['n']\n",
      "    \n",
      "    if np.sum(df['Match']) > 0:\n",
      "        df = df[match_table['Match']].sort(columns='N Matches', ascending=False)\n",
      "        max_n = max(df['N Matches'])\n",
      "        df = df[df['N Matches'] == max_n]\n",
      "        i = np.random.choice(df.index, 1)[0]\n",
      "        url= df.ix[i]['url']\n",
      "        t = df.ix[i]['Text']\n",
      "        if output:\n",
      "            print t, max_n\n",
      "            display.display(display.Image(url=url))\n",
      "        return df\n",
      "    else:\n",
      "        return []\n",
      "    \n",
      "find_matching_gifs_by_content('dancing')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3. Sleeping Beauty on \u201cDancing with the Stars\u201d 1\n"
       ]
      },
      {
       "html": [
        "<img src=\"http://s3-ec.buzzfed.com/static/2013-10/enhanced/webdr01/7/13/anigif_enhanced-buzz-30732-1381167779-9.gif\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Image at 0x114665190>"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0_x</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
        "      <th>Text</th>\n",
        "      <th>Title</th>\n",
        "      <th>URL</th>\n",
        "      <th>text verbs</th>\n",
        "      <th>text adverbs</th>\n",
        "      <th>text adjectives</th>\n",
        "      <th>text nouns</th>\n",
        "      <th>all text tags</th>\n",
        "      <th>...</th>\n",
        "      <th>Unnamed: 0_y</th>\n",
        "      <th>id</th>\n",
        "      <th>gif_id</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>url</th>\n",
        "      <th>strength</th>\n",
        "      <th>emotion</th>\n",
        "      <th>Match</th>\n",
        "      <th>N Matches</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6   </th>\n",
        "      <td>   19</td>\n",
        "      <td>   19</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                   ['Now']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> ['For', 'Mass', 'Effect', 'Characters', 'Danci...</td>\n",
        "      <td> [('And', 'CC'), ('Now', 'RB'), ('For', 'NNP'),...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2818</td>\n",
        "      <td> 2538</td>\n",
        "      <td>   21</td>\n",
        "      <td>-0.368056</td>\n",
        "      <td>-0.035156</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.369731</td>\n",
        "      <td> unpleasant</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7   </th>\n",
        "      <td>   20</td>\n",
        "      <td>   20</td>\n",
        "      <td>                Your link was successfully shared!</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>              ['was', 'shared']</td>\n",
        "      <td>                          ['successfully']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                          ['link']</td>\n",
        "      <td> [('Your', 'PRP$'), ('link', 'NN'), ('was', 'VB...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3637</td>\n",
        "      <td> 3549</td>\n",
        "      <td>   22</td>\n",
        "      <td> 0.318000</td>\n",
        "      <td> 0.075188</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.326768</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3140</th>\n",
        "      <td> 6469</td>\n",
        "      <td> 6469</td>\n",
        "      <td>                    And dancing!Tom strikes again!</td>\n",
        "      <td> 10 Reasons \"Thor: The Dark World\" Is Really Th...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-11/enhanc...</td>\n",
        "      <td>         ['dancing', 'strikes']</td>\n",
        "      <td>                                 ['again']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                           ['Tom']</td>\n",
        "      <td> [('And', 'CC'), ('dancing', 'VBG'), ('!', '.')...</td>\n",
        "      <td>...</td>\n",
        "      <td>  227</td>\n",
        "      <td> 1349</td>\n",
        "      <td> 6471</td>\n",
        "      <td> 0.378667</td>\n",
        "      <td> 0.304511</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-11/enhanc...</td>\n",
        "      <td> 0.485917</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3139</th>\n",
        "      <td> 6469</td>\n",
        "      <td> 6469</td>\n",
        "      <td>                    And dancing!Tom strikes again!</td>\n",
        "      <td> 10 Reasons \"Thor: The Dark World\" Is Really Th...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-11/enhanc...</td>\n",
        "      <td>         ['dancing', 'strikes']</td>\n",
        "      <td>                                 ['again']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                           ['Tom']</td>\n",
        "      <td> [('And', 'CC'), ('dancing', 'VBG'), ('!', '.')...</td>\n",
        "      <td>...</td>\n",
        "      <td>  226</td>\n",
        "      <td>  272</td>\n",
        "      <td> 6471</td>\n",
        "      <td>-0.002667</td>\n",
        "      <td> 0.112782</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-11/enhanc...</td>\n",
        "      <td> 0.112814</td>\n",
        "      <td>     active</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2571</th>\n",
        "      <td> 5280</td>\n",
        "      <td> 5280</td>\n",
        "      <td>                                 16. More dancing:</td>\n",
        "      <td> 27 GIFs That Prove Lumpy Space Princess From \"...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/29/21/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                              ['More', 'dancing:']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> [('16.', 'CD'), ('More', 'JJR'), ('dancing:', ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2312</td>\n",
        "      <td> 2030</td>\n",
        "      <td> 5282</td>\n",
        "      <td> 0.407639</td>\n",
        "      <td> 0.035156</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/29/21/...</td>\n",
        "      <td> 0.409152</td>\n",
        "      <td>   pleasant</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2568</th>\n",
        "      <td> 5270</td>\n",
        "      <td> 5270</td>\n",
        "      <td> 6. Arguing because the DJ won\u2019t play \u2018Just Dan...</td>\n",
        "      <td> 27 GIFs That Prove Lumpy Space Princess From \"...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/29/21/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                              ['\\xe2\\x80\\x98Just']</td>\n",
        "      <td> ['Arguing', 'DJ', 'won\\xe2\\x80\\x99t', 'play', ...</td>\n",
        "      <td> [('6.', 'CD'), ('Arguing', 'NNP'), ('because',...</td>\n",
        "      <td>...</td>\n",
        "      <td>  221</td>\n",
        "      <td>  267</td>\n",
        "      <td> 5272</td>\n",
        "      <td>-0.004000</td>\n",
        "      <td>-0.003759</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/29/21/...</td>\n",
        "      <td> 0.005489</td>\n",
        "      <td>    unhappy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2218</th>\n",
        "      <td> 4568</td>\n",
        "      <td> 4568</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                        ['All']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> ['Use', 'More', 'Dancing', 'Shetland', 'Ponies...</td>\n",
        "      <td> [('We', 'PRP'), ('Could', 'MD'), ('All', 'VB')...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3580</td>\n",
        "      <td> 3458</td>\n",
        "      <td> 4570</td>\n",
        "      <td> 0.372464</td>\n",
        "      <td>-0.327869</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.496213</td>\n",
        "      <td>     serene</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2217</th>\n",
        "      <td> 4567</td>\n",
        "      <td> 4567</td>\n",
        "      <td>                                     14. Spot. On.</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                   ['Spot.', 'On']</td>\n",
        "      <td> [('14.', 'CD'), ('Spot.', 'NNP'), ('On', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3852</td>\n",
        "      <td> 3891</td>\n",
        "      <td> 4569</td>\n",
        "      <td>-0.440000</td>\n",
        "      <td>-0.093985</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.449926</td>\n",
        "      <td>        sad</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2216</th>\n",
        "      <td> 4566</td>\n",
        "      <td> 4566</td>\n",
        "      <td> 13. \u2026and is basically Gob Bluth whilst doing a...</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                ['is', 'doing']</td>\n",
        "      <td>                             ['basically']</td>\n",
        "      <td> ['\\xe2\\x80\\xa6and', '\\xe2\\x80\\x9cillusion.\\xe2...</td>\n",
        "      <td>                        ['Gob', 'Bluth', 'whilst']</td>\n",
        "      <td> [('13.', 'CD'), ('\\xe2\\x80\\xa6and', 'JJ'), ('i...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2269</td>\n",
        "      <td> 1988</td>\n",
        "      <td> 4568</td>\n",
        "      <td> 0.460000</td>\n",
        "      <td> 0.041353</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.461855</td>\n",
        "      <td>   pleasant</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2215</th>\n",
        "      <td> 4565</td>\n",
        "      <td> 4565</td>\n",
        "      <td> 12. And finally, Socks dances to \u201cThe Final Co...</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                               ['finally']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> ['Socks', 'dances', '\\xe2\\x80\\x9cThe', 'Final'...</td>\n",
        "      <td> [('12.', 'CD'), ('And', 'CC'), ('finally', 'RB...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3021</td>\n",
        "      <td> 2775</td>\n",
        "      <td> 4567</td>\n",
        "      <td> 0.388000</td>\n",
        "      <td> 0.131579</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.409704</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2214</th>\n",
        "      <td> 4564</td>\n",
        "      <td> 4564</td>\n",
        "      <td> 9. In this advert, Socks falls deeply in love ...</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                ['deeply']</td>\n",
        "      <td>                             ['\\xe2\\x80\\x9cTotal']</td>\n",
        "      <td> ['advert', 'Socks', 'falls', 'love', 'dances',...</td>\n",
        "      <td> [('9.', 'CD'), ('In', 'IN'), ('this', 'DT'), (...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2061</td>\n",
        "      <td> 1778</td>\n",
        "      <td> 4566</td>\n",
        "      <td> 0.002667</td>\n",
        "      <td>-0.157895</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.157918</td>\n",
        "      <td>    subdued</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2213</th>\n",
        "      <td> 4562</td>\n",
        "      <td> 4562</td>\n",
        "      <td>        5. \u2026all through the faux, CGI countryside.</td>\n",
        "      <td> We Could All Use More Dancing Shetland Ponies ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                               ['\\xe2\\x80\\xa6all']</td>\n",
        "      <td>                    ['faux', 'CGI', 'countryside']</td>\n",
        "      <td> [('5.', 'CD'), ('\\xe2\\x80\\xa6all', 'JJ'), ('th...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2224</td>\n",
        "      <td> 1942</td>\n",
        "      <td> 4564</td>\n",
        "      <td> 0.453333</td>\n",
        "      <td> 0.157895</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.480043</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2204</th>\n",
        "      <td> 4539</td>\n",
        "      <td> 4539</td>\n",
        "      <td> 5. When you go out, Alcohol is always your dan...</td>\n",
        "      <td>        25 Signs Alcohol Is Your Significant Other</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-12/enhanc...</td>\n",
        "      <td>                   ['go', 'is']</td>\n",
        "      <td>                         ['out', 'always']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                   ['Alcohol', 'dance', 'partner']</td>\n",
        "      <td> [('5.', 'CD'), ('When', 'WRB'), ('you', 'PRP')...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3342</td>\n",
        "      <td> 3155</td>\n",
        "      <td> 4541</td>\n",
        "      <td> 0.454667</td>\n",
        "      <td> 0.041353</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-12/enhanc...</td>\n",
        "      <td> 0.456544</td>\n",
        "      <td>   pleasant</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2090</th>\n",
        "      <td> 4334</td>\n",
        "      <td> 4334</td>\n",
        "      <td>                    17. Voldemort can dance again.</td>\n",
        "      <td> 19 Reasons Why \"A Very Potter Musical\" Is Time...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-06/29/23/...</td>\n",
        "      <td>                      ['dance']</td>\n",
        "      <td>                                 ['again']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                     ['Voldemort']</td>\n",
        "      <td> [('17.', 'CD'), ('Voldemort', 'NNP'), ('can', ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2225</td>\n",
        "      <td> 1943</td>\n",
        "      <td> 4336</td>\n",
        "      <td> 0.436000</td>\n",
        "      <td>-0.135338</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-06/29/23/...</td>\n",
        "      <td> 0.456522</td>\n",
        "      <td>  contented</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1823</th>\n",
        "      <td> 3775</td>\n",
        "      <td> 3775</td>\n",
        "      <td>          2. \u2026and you have your own dance routine.</td>\n",
        "      <td> 17 Signs You And Your Best Friend Are Turning ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>                       ['have']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                        ['\\xe2\\x80\\xa6and', 'own']</td>\n",
        "      <td>                              ['dance', 'routine']</td>\n",
        "      <td> [('2.', 'CD'), ('\\xe2\\x80\\xa6and', 'JJ'), ('yo...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3322</td>\n",
        "      <td> 3471</td>\n",
        "      <td> 3777</td>\n",
        "      <td> 0.447826</td>\n",
        "      <td> 0.188525</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.485891</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1822</th>\n",
        "      <td> 3775</td>\n",
        "      <td> 3775</td>\n",
        "      <td>          2. \u2026and you have your own dance routine.</td>\n",
        "      <td> 17 Signs You And Your Best Friend Are Turning ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>                       ['have']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                        ['\\xe2\\x80\\xa6and', 'own']</td>\n",
        "      <td>                              ['dance', 'routine']</td>\n",
        "      <td> [('2.', 'CD'), ('\\xe2\\x80\\xa6and', 'JJ'), ('yo...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3321</td>\n",
        "      <td> 3130</td>\n",
        "      <td> 3777</td>\n",
        "      <td> 0.298667</td>\n",
        "      <td> 0.304511</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.426531</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1800</th>\n",
        "      <td> 3711</td>\n",
        "      <td> 3711</td>\n",
        "      <td> Watch the music video for her new single \u201cShak...</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                        ['see']</td>\n",
        "      <td>                         ['intentionally']</td>\n",
        "      <td>                                    ['new', 'bad']</td>\n",
        "      <td> ['Watch', 'music', 'video', 'single', '\\xe2\\x8...</td>\n",
        "      <td> [('Watch', 'NN'), ('the', 'DT'), ('music', 'NN...</td>\n",
        "      <td>...</td>\n",
        "      <td>  858</td>\n",
        "      <td> 3349</td>\n",
        "      <td> 3713</td>\n",
        "      <td>-0.304700</td>\n",
        "      <td> 0.376712</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.484514</td>\n",
        "      <td>    nervous</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3150</th>\n",
        "      <td> 6490</td>\n",
        "      <td> 6490</td>\n",
        "      <td>     18. We learned that white people can\u2019t dance.</td>\n",
        "      <td>    33 Lessons Saturday Morning Cartoons Taught Us</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-03/enhanc...</td>\n",
        "      <td>                    ['learned']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                     ['white', 'can\\xe2\\x80\\x99t']</td>\n",
        "      <td>                               ['people', 'dance']</td>\n",
        "      <td> [('18.', 'CD'), ('We', 'PRP'), ('learned', 'VB...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2949</td>\n",
        "      <td> 2692</td>\n",
        "      <td> 6492</td>\n",
        "      <td>-0.210000</td>\n",
        "      <td>-0.406015</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-03/enhanc...</td>\n",
        "      <td> 0.457108</td>\n",
        "      <td>  depressed</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3159</th>\n",
        "      <td> 6506</td>\n",
        "      <td> 6506</td>\n",
        "      <td>                       Press play and scroll down!</td>\n",
        "      <td>            16 Animals Dancing To A '90s Booty Jam</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                       ['Press', 'play', 'scroll']</td>\n",
        "      <td> [('Press', 'NN'), ('play', 'NN'), ('and', 'CC'...</td>\n",
        "      <td>...</td>\n",
        "      <td>  169</td>\n",
        "      <td>  233</td>\n",
        "      <td> 6508</td>\n",
        "      <td> 0.080667</td>\n",
        "      <td> 0.345865</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.355148</td>\n",
        "      <td>      alert</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3160</th>\n",
        "      <td> 6506</td>\n",
        "      <td> 6506</td>\n",
        "      <td>                       Press play and scroll down!</td>\n",
        "      <td>            16 Animals Dancing To A '90s Booty Jam</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                       ['Press', 'play', 'scroll']</td>\n",
        "      <td> [('Press', 'NN'), ('play', 'NN'), ('and', 'CC'...</td>\n",
        "      <td>...</td>\n",
        "      <td>  170</td>\n",
        "      <td> 3574</td>\n",
        "      <td> 6508</td>\n",
        "      <td> 0.244667</td>\n",
        "      <td> 0.251880</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.351149</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3486</th>\n",
        "      <td> 7165</td>\n",
        "      <td> 7165</td>\n",
        "      <td>                   8. Fred Astaire\u2019s Spirit Animal</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                       ['Fred']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                              ['Spirit', 'Animal']</td>\n",
        "      <td> [('8.', 'CD'), ('Fred', 'VBN'), ('Astaire\\xe2\\...</td>\n",
        "      <td>...</td>\n",
        "      <td>  851</td>\n",
        "      <td>  727</td>\n",
        "      <td> 7167</td>\n",
        "      <td> 0.282000</td>\n",
        "      <td> 0.398496</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.488183</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3652</th>\n",
        "      <td> 7507</td>\n",
        "      <td> 7507</td>\n",
        "      <td>            102. The \u201cAnarchist Dance Troupe\u201d Look</td>\n",
        "      <td> A Complete Ranking Of Everything Britney Spear...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/16/21/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                         ['\\xe2\\x80\\x9cAnarchist']</td>\n",
        "      <td>           ['Dance', 'Troupe\\xe2\\x80\\x9d', 'Look']</td>\n",
        "      <td> [('102.', 'CD'), ('The', 'DT'), ('\\xe2\\x80\\x9c...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3273</td>\n",
        "      <td> 3070</td>\n",
        "      <td> 7509</td>\n",
        "      <td>-0.394667</td>\n",
        "      <td>-0.097744</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/16/21/...</td>\n",
        "      <td> 0.406591</td>\n",
        "      <td>        sad</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3498</th>\n",
        "      <td> 7192</td>\n",
        "      <td> 7192</td>\n",
        "      <td>           22. 22. Veronica dancing with her crew.</td>\n",
        "      <td> The 25 Best Moments Of The \"Veronica Mars\" Mov...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-01/enhanc...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                   ['Veronica', 'dancing', 'crew']</td>\n",
        "      <td> [('22.', 'CD'), ('22.', 'CD'), ('Veronica', 'N...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2565</td>\n",
        "      <td> 2263</td>\n",
        "      <td> 7194</td>\n",
        "      <td>-0.014583</td>\n",
        "      <td> 0.472656</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-01/enhanc...</td>\n",
        "      <td> 0.472881</td>\n",
        "      <td>     active</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3490</th>\n",
        "      <td> 7168</td>\n",
        "      <td> 7168</td>\n",
        "      <td>                              11. The Limb Flailer</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                               ['Limb', 'Flailer']</td>\n",
        "      <td> [('11.', 'CD'), ('The', 'DT'), ('Limb', 'NNP')...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3235</td>\n",
        "      <td> 3024</td>\n",
        "      <td> 7170</td>\n",
        "      <td> 0.293333</td>\n",
        "      <td> 0.368421</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.470933</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3489</th>\n",
        "      <td> 7167</td>\n",
        "      <td> 7167</td>\n",
        "      <td>                      10. Beyonce\u2019s Backing Dancer</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>     ['Beyonce\\xe2\\x80\\x99s', 'Backing', 'Dancer']</td>\n",
        "      <td> [('10.', 'CD'), ('Beyonce\\xe2\\x80\\x99s', 'NNS'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1820</td>\n",
        "      <td> 1565</td>\n",
        "      <td> 7169</td>\n",
        "      <td> 0.303571</td>\n",
        "      <td> 0.298165</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.425509</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3488</th>\n",
        "      <td> 7165</td>\n",
        "      <td> 7165</td>\n",
        "      <td>                   8. Fred Astaire\u2019s Spirit Animal</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                       ['Fred']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                              ['Spirit', 'Animal']</td>\n",
        "      <td> [('8.', 'CD'), ('Fred', 'VBN'), ('Astaire\\xe2\\...</td>\n",
        "      <td>...</td>\n",
        "      <td>  853</td>\n",
        "      <td> 2847</td>\n",
        "      <td> 7167</td>\n",
        "      <td> 0.292667</td>\n",
        "      <td> 0.349624</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.455951</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3487</th>\n",
        "      <td> 7165</td>\n",
        "      <td> 7165</td>\n",
        "      <td>                   8. Fred Astaire\u2019s Spirit Animal</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                       ['Fred']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                              ['Spirit', 'Animal']</td>\n",
        "      <td> [('8.', 'CD'), ('Fred', 'VBN'), ('Astaire\\xe2\\...</td>\n",
        "      <td>...</td>\n",
        "      <td>  852</td>\n",
        "      <td> 1350</td>\n",
        "      <td> 7167</td>\n",
        "      <td> 0.217532</td>\n",
        "      <td> 0.284404</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.358058</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3485</th>\n",
        "      <td> 7159</td>\n",
        "      <td> 7159</td>\n",
        "      <td>                             2. The Secret Crunker</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                             ['Secret', 'Crunker']</td>\n",
        "      <td> [('2.', 'CD'), ('The', 'DT'), ('Secret', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td>  653</td>\n",
        "      <td> 3052</td>\n",
        "      <td> 7161</td>\n",
        "      <td>-0.001621</td>\n",
        "      <td> 0.376712</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.376715</td>\n",
        "      <td>     active</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3161</th>\n",
        "      <td> 6513</td>\n",
        "      <td> 6513</td>\n",
        "      <td>          7. Let me hear you say \u201cscrub da ground\u201d</td>\n",
        "      <td>            16 Animals Dancing To A '90s Booty Jam</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                ['hear', 'say']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                     ['Let', 'da']</td>\n",
        "      <td> [('7.', 'CD'), ('Let', 'NNP'), ('me', 'PRP'), ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2469</td>\n",
        "      <td> 2172</td>\n",
        "      <td> 6515</td>\n",
        "      <td> 0.479333</td>\n",
        "      <td> 0.481203</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.679203</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3484</th>\n",
        "      <td> 7159</td>\n",
        "      <td> 7159</td>\n",
        "      <td>                             2. The Secret Crunker</td>\n",
        "      <td>          13 Dogs You Might See On The Dance Floor</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                             ['Secret', 'Crunker']</td>\n",
        "      <td> [('2.', 'CD'), ('The', 'DT'), ('Secret', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td>  652</td>\n",
        "      <td>  576</td>\n",
        "      <td> 7161</td>\n",
        "      <td>-0.359722</td>\n",
        "      <td> 0.288235</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.460955</td>\n",
        "      <td>   stressed</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>656 </th>\n",
        "      <td> 1333</td>\n",
        "      <td> 1333</td>\n",
        "      <td>                          6. She sings AND dances.</td>\n",
        "      <td> 15 Reasons Jessica Lange Should Stay On \"Ameri...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-09/25/0/e...</td>\n",
        "      <td>                      ['sings']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                        ['dances']</td>\n",
        "      <td> [('6.', 'CD'), ('She', 'PRP'), ('sings', 'VBZ'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1576</td>\n",
        "      <td> 1328</td>\n",
        "      <td> 1335</td>\n",
        "      <td> 0.356000</td>\n",
        "      <td> 0.052632</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-09/25/0/e...</td>\n",
        "      <td> 0.359870</td>\n",
        "      <td>   pleasant</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>630 </th>\n",
        "      <td> 1287</td>\n",
        "      <td> 1287</td>\n",
        "      <td>                     6. 6. Dance like this at prom</td>\n",
        "      <td> 20 Things You Wanted To Do In High School But ...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                 ['Dance', 'prom']</td>\n",
        "      <td> [('6.', 'CD'), ('6.', 'CD'), ('Dance', 'NNP'),...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2369</td>\n",
        "      <td> 2083</td>\n",
        "      <td> 1289</td>\n",
        "      <td> 0.295333</td>\n",
        "      <td> 0.161654</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.336680</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>456 </th>\n",
        "      <td>  946</td>\n",
        "      <td>  946</td>\n",
        "      <td>                        6. \u201c\u2026\u2026ilu future husband\u2026\u201d</td>\n",
        "      <td> And Now For Martin Freeman Dancing Awkwardly I...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td> ['\\xe2\\x80\\x9c\\xe2\\x80\\xa6\\xe2\\x80\\xa6ilu', 'f...</td>\n",
        "      <td>               ['husband\\xe2\\x80\\xa6\\xe2\\x80\\x9d']</td>\n",
        "      <td> [('6.', 'CD'), ('\\xe2\\x80\\x9c\\xe2\\x80\\xa6\\xe2\\...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3258</td>\n",
        "      <td> 3053</td>\n",
        "      <td>  948</td>\n",
        "      <td> 0.275527</td>\n",
        "      <td> 0.331050</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.430708</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>235 </th>\n",
        "      <td>  501</td>\n",
        "      <td>  501</td>\n",
        "      <td> 2. Werk it, girl. Dancing naked in the privacy...</td>\n",
        "      <td>  18 Flawless Beauty Tips From \"Fantasia\" Centaurs</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                      ['naked']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                      ['all-girl']</td>\n",
        "      <td> ['Werk', 'girl.', 'Dancing', 'privacy', 'harem...</td>\n",
        "      <td> [('2.', 'CD'), ('Werk', 'NNP'), ('it', 'PRP'),...</td>\n",
        "      <td>...</td>\n",
        "      <td>  938</td>\n",
        "      <td>  802</td>\n",
        "      <td>  503</td>\n",
        "      <td>-0.149109</td>\n",
        "      <td>-0.431507</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.456543</td>\n",
        "      <td>      bored</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>902 </th>\n",
        "      <td> 1807</td>\n",
        "      <td> 1807</td>\n",
        "      <td> 2. But the girls won\u2019t go down without doing a...</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                      ['doing']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                ['won\\xe2\\x80\\x99t', 'few', 'own']</td>\n",
        "      <td>                                   ['girls', 'go']</td>\n",
        "      <td> [('2.', 'CD'), ('But', 'CC'), ('the', 'DT'), (...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2143</td>\n",
        "      <td> 1860</td>\n",
        "      <td> 1809</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.018797</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.018797</td>\n",
        "      <td>     active</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199 </th>\n",
        "      <td>  429</td>\n",
        "      <td>  429</td>\n",
        "      <td>        28. When he showed off his dancing skills:</td>\n",
        "      <td>     29 Times Harry Styles Was Actually The Cutest</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-02/enhanc...</td>\n",
        "      <td>                     ['showed']</td>\n",
        "      <td>                                   ['off']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                       ['dancing']</td>\n",
        "      <td> [('28.', 'CD'), ('When', 'WRB'), ('he', 'PRP')...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1602</td>\n",
        "      <td> 1583</td>\n",
        "      <td>  431</td>\n",
        "      <td> 0.134740</td>\n",
        "      <td>-0.399083</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-02/enhanc...</td>\n",
        "      <td> 0.421215</td>\n",
        "      <td>       calm</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>198 </th>\n",
        "      <td>  429</td>\n",
        "      <td>  429</td>\n",
        "      <td>        28. When he showed off his dancing skills:</td>\n",
        "      <td>     29 Times Harry Styles Was Actually The Cutest</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-02/enhanc...</td>\n",
        "      <td>                     ['showed']</td>\n",
        "      <td>                                   ['off']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                       ['dancing']</td>\n",
        "      <td> [('28.', 'CD'), ('When', 'WRB'), ('he', 'PRP')...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1601</td>\n",
        "      <td> 1351</td>\n",
        "      <td>  431</td>\n",
        "      <td> 0.250000</td>\n",
        "      <td> 0.183486</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-02/enhanc...</td>\n",
        "      <td> 0.310108</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11  </th>\n",
        "      <td>   23</td>\n",
        "      <td>   23</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>...</td>\n",
        "      <td> 1961</td>\n",
        "      <td> 1686</td>\n",
        "      <td>   25</td>\n",
        "      <td> 0.406250</td>\n",
        "      <td>-0.148438</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.432519</td>\n",
        "      <td>  contented</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10  </th>\n",
        "      <td>   22</td>\n",
        "      <td>   22</td>\n",
        "      <td> Note: once you save these links, they will no ...</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>               ['Note', 'save']</td>\n",
        "      <td> ['once', 'no', 'longer', 'automatically']</td>\n",
        "      <td>                                        ['update']</td>\n",
        "      <td>                                         ['links']</td>\n",
        "      <td> [('Note', 'VB'), (':', ':'), ('once', 'RB'), (...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2136</td>\n",
        "      <td> 1848</td>\n",
        "      <td>   24</td>\n",
        "      <td> 0.029333</td>\n",
        "      <td> 0.206767</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.208837</td>\n",
        "      <td>     active</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9   </th>\n",
        "      <td>   21</td>\n",
        "      <td>   21</td>\n",
        "      <td>                                         More Buzz</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                          ['More']</td>\n",
        "      <td>                                          ['Buzz']</td>\n",
        "      <td>                [('More', 'JJR'), ('Buzz', 'NNP')]</td>\n",
        "      <td>...</td>\n",
        "      <td> 2670</td>\n",
        "      <td> 2378</td>\n",
        "      <td>   23</td>\n",
        "      <td> 0.060000</td>\n",
        "      <td> 0.018797</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.062875</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8   </th>\n",
        "      <td>   20</td>\n",
        "      <td>   20</td>\n",
        "      <td>                Your link was successfully shared!</td>\n",
        "      <td> And Now For 8 \"Mass Effect\" Characters Dancing...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>              ['was', 'shared']</td>\n",
        "      <td>                          ['successfully']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                          ['link']</td>\n",
        "      <td> [('Your', 'PRP$'), ('link', 'NN'), ('was', 'VB...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3638</td>\n",
        "      <td> 3887</td>\n",
        "      <td>   22</td>\n",
        "      <td> 0.297333</td>\n",
        "      <td> 0.233083</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.377802</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>901 </th>\n",
        "      <td> 1806</td>\n",
        "      <td> 1806</td>\n",
        "      <td> 1. Round 1: Taylor goes in first with a few sa...</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                       ['goes']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                         ['first', 'few', 'sassy']</td>\n",
        "      <td>           ['Round', 'Taylor', 'shoulder', 'pops']</td>\n",
        "      <td> [('1.', 'CD'), ('Round', 'NNP'), ('1', 'CD'), ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2397</td>\n",
        "      <td> 2112</td>\n",
        "      <td> 1808</td>\n",
        "      <td> 0.439222</td>\n",
        "      <td> 0.207763</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.485882</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>903 </th>\n",
        "      <td> 1809</td>\n",
        "      <td> 1809</td>\n",
        "      <td> 4. But Taylor didn\u2019t count on them knowing tha...</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                    ['knowing']</td>\n",
        "      <td>                                   ['too']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>          ['Taylor', 'didn\\xe2\\x80\\x99t', 'count']</td>\n",
        "      <td> [('4.', 'CD'), ('But', 'CC'), ('Taylor', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3198</td>\n",
        "      <td> 2983</td>\n",
        "      <td> 1811</td>\n",
        "      <td> 0.169368</td>\n",
        "      <td> 0.230594</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.286110</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1796</th>\n",
        "      <td> 3703</td>\n",
        "      <td> 3703</td>\n",
        "      <td>                              8. She can bunnyhop!</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                   ['bunnyhop']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> [('8.', 'CD'), ('She', 'PRP'), ('can', 'MD'), ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1643</td>\n",
        "      <td> 1391</td>\n",
        "      <td> 3705</td>\n",
        "      <td> 0.081169</td>\n",
        "      <td> 0.114679</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.140498</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1789</th>\n",
        "      <td> 3692</td>\n",
        "      <td> 3692</td>\n",
        "      <td> 19. Wait, is this an actual dance move? Hips d...</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                         ['is']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                        ['actual']</td>\n",
        "      <td> ['Wait', 'dance', 'move', 'Hips', 'don\\xe2\\x80...</td>\n",
        "      <td> [('19.', 'CD'), ('Wait', 'NNP'), (',', ','), (...</td>\n",
        "      <td>...</td>\n",
        "      <td>  395</td>\n",
        "      <td>  390</td>\n",
        "      <td> 3694</td>\n",
        "      <td>-0.180556</td>\n",
        "      <td>-0.054688</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.188656</td>\n",
        "      <td>        sad</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1795</th>\n",
        "      <td> 3701</td>\n",
        "      <td> 3701</td>\n",
        "      <td>                               10. Spirit fingers!</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                             ['Spirit', 'fingers']</td>\n",
        "      <td> [('10.', 'CD'), ('Spirit', 'NN'), ('fingers', ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1034</td>\n",
        "      <td>  885</td>\n",
        "      <td> 3703</td>\n",
        "      <td> 0.301333</td>\n",
        "      <td> 0.251880</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.392741</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1794</th>\n",
        "      <td> 3700</td>\n",
        "      <td> 3700</td>\n",
        "      <td>                 11. Shake it off and reel \u2018em in.</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>               ['Shake', 'reel', '\\xe2\\x80\\x98em']</td>\n",
        "      <td> [('11.', 'CD'), ('Shake', 'NNP'), ('it', 'PRP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1568</td>\n",
        "      <td> 2129</td>\n",
        "      <td> 3702</td>\n",
        "      <td> 0.471333</td>\n",
        "      <td> 0.477444</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.670900</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1793</th>\n",
        "      <td> 3700</td>\n",
        "      <td> 3700</td>\n",
        "      <td>                 11. Shake it off and reel \u2018em in.</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>               ['Shake', 'reel', '\\xe2\\x80\\x98em']</td>\n",
        "      <td> [('11.', 'CD'), ('Shake', 'NNP'), ('it', 'PRP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1567</td>\n",
        "      <td> 1322</td>\n",
        "      <td> 3702</td>\n",
        "      <td>-0.130667</td>\n",
        "      <td>-0.402256</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.422947</td>\n",
        "      <td>      bored</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1792</th>\n",
        "      <td> 3695</td>\n",
        "      <td> 3695</td>\n",
        "      <td> 16. She\u2019s got more limbs than she knows what t...</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>         ['got', 'knows', 'do']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                          ['more']</td>\n",
        "      <td>                     ['She\\xe2\\x80\\x99s', 'limbs']</td>\n",
        "      <td> [('16.', 'CD'), ('She\\xe2\\x80\\x99s', 'NNS'), (...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1958</td>\n",
        "      <td> 1683</td>\n",
        "      <td> 3697</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.015038</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.015038</td>\n",
        "      <td>    subdued</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1791</th>\n",
        "      <td> 3692</td>\n",
        "      <td> 3692</td>\n",
        "      <td> 19. Wait, is this an actual dance move? Hips d...</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                         ['is']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                        ['actual']</td>\n",
        "      <td> ['Wait', 'dance', 'move', 'Hips', 'don\\xe2\\x80...</td>\n",
        "      <td> [('19.', 'CD'), ('Wait', 'NNP'), (',', ','), (...</td>\n",
        "      <td>...</td>\n",
        "      <td>  397</td>\n",
        "      <td> 2670</td>\n",
        "      <td> 3694</td>\n",
        "      <td> 0.317241</td>\n",
        "      <td> 0.208738</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.379754</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1790</th>\n",
        "      <td> 3692</td>\n",
        "      <td> 3692</td>\n",
        "      <td> 19. Wait, is this an actual dance move? Hips d...</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                         ['is']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                        ['actual']</td>\n",
        "      <td> ['Wait', 'dance', 'move', 'Hips', 'don\\xe2\\x80...</td>\n",
        "      <td> [('19.', 'CD'), ('Wait', 'NNP'), (',', ','), (...</td>\n",
        "      <td>...</td>\n",
        "      <td>  396</td>\n",
        "      <td> 2669</td>\n",
        "      <td> 3694</td>\n",
        "      <td> 0.313793</td>\n",
        "      <td> 0.383495</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.495514</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1788</th>\n",
        "      <td> 3690</td>\n",
        "      <td> 3690</td>\n",
        "      <td>                 21. So grace. Much elegance. Wow.</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                    ['So']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>            ['grace.', 'Much', 'elegance.', 'Wow']</td>\n",
        "      <td> [('21.', 'CD'), ('So', 'RB'), ('grace.', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3212</td>\n",
        "      <td> 3367</td>\n",
        "      <td> 3692</td>\n",
        "      <td> 0.110345</td>\n",
        "      <td>-0.257282</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.279947</td>\n",
        "      <td>       calm</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>904 </th>\n",
        "      <td> 1809</td>\n",
        "      <td> 1809</td>\n",
        "      <td> 4. But Taylor didn\u2019t count on them knowing tha...</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                    ['knowing']</td>\n",
        "      <td>                                   ['too']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>          ['Taylor', 'didn\\xe2\\x80\\x99t', 'count']</td>\n",
        "      <td> [('4.', 'CD'), ('But', 'CC'), ('Taylor', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3199</td>\n",
        "      <td> 3710</td>\n",
        "      <td> 1811</td>\n",
        "      <td> 0.264000</td>\n",
        "      <td> 0.146617</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.301981</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1787</th>\n",
        "      <td> 3690</td>\n",
        "      <td> 3690</td>\n",
        "      <td>                 21. So grace. Much elegance. Wow.</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                    ['So']</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>            ['grace.', 'Much', 'elegance.', 'Wow']</td>\n",
        "      <td> [('21.', 'CD'), ('So', 'RB'), ('grace.', 'NNP'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3211</td>\n",
        "      <td> 2993</td>\n",
        "      <td> 3692</td>\n",
        "      <td>-0.065640</td>\n",
        "      <td>-0.075342</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.099925</td>\n",
        "      <td>  depressed</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1786</th>\n",
        "      <td> 3688</td>\n",
        "      <td> 3688</td>\n",
        "      <td>        23. Check out that mildly tricky footwork!</td>\n",
        "      <td> 27 GIFs Of Taylor Swift Dancing Awkwardly In H...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                ['mildly']</td>\n",
        "      <td>                                        ['tricky']</td>\n",
        "      <td>                             ['Check', 'footwork']</td>\n",
        "      <td> [('23.', 'CD'), ('Check', 'NNP'), ('out', 'IN'...</td>\n",
        "      <td>...</td>\n",
        "      <td> 1720</td>\n",
        "      <td> 1463</td>\n",
        "      <td> 3690</td>\n",
        "      <td> 0.269481</td>\n",
        "      <td> 0.229358</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-08/18/18/...</td>\n",
        "      <td> 0.353872</td>\n",
        "      <td>     elated</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1546</th>\n",
        "      <td> 3183</td>\n",
        "      <td> 3183</td>\n",
        "      <td>    3. Sleeping Beauty on \u201cDancing with the Stars\u201d</td>\n",
        "      <td>        If Disney Princesses Were Reality TV Stars</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>      ['\\xe2\\x80\\x9cDancing', 'Stars\\xe2\\x80\\x9d']</td>\n",
        "      <td>                            ['Sleeping', 'Beauty']</td>\n",
        "      <td> [('3.', 'CD'), ('Sleeping', 'NNP'), ('Beauty',...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3366</td>\n",
        "      <td> 3184</td>\n",
        "      <td> 3185</td>\n",
        "      <td> 0.457333</td>\n",
        "      <td> 0.154135</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2013-10/enhanc...</td>\n",
        "      <td> 0.482609</td>\n",
        "      <td>      happy</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>907 </th>\n",
        "      <td> 1814</td>\n",
        "      <td> 1814</td>\n",
        "      <td>                         9. Winner: Tegan and Sara</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                       ['Winner', 'Tegan', 'Sara']</td>\n",
        "      <td> [('9.', 'CD'), ('Winner', 'NNP'), (':', ':'), ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2479</td>\n",
        "      <td> 2184</td>\n",
        "      <td> 1816</td>\n",
        "      <td> 0.479333</td>\n",
        "      <td> 0.481203</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.679203</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>906 </th>\n",
        "      <td> 1814</td>\n",
        "      <td> 1814</td>\n",
        "      <td>                         9. Winner: Tegan and Sara</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>                       ['Winner', 'Tegan', 'Sara']</td>\n",
        "      <td> [('9.', 'CD'), ('Winner', 'NNP'), (':', ':'), ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2478</td>\n",
        "      <td> 2182</td>\n",
        "      <td> 1816</td>\n",
        "      <td> 0.479333</td>\n",
        "      <td> 0.481203</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.679203</td>\n",
        "      <td>    excited</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>905 </th>\n",
        "      <td> 1810</td>\n",
        "      <td> 1810</td>\n",
        "      <td> 5. Round 3: Taylor goes all out, giving sassy ...</td>\n",
        "      <td> Watch Taylor Swift Challenge Tegan And Sara To...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> ['goes', 'giving', 'audience']</td>\n",
        "      <td>                                        []</td>\n",
        "      <td>                                         ['sassy']</td>\n",
        "      <td>                      ['Round', 'Taylor', 'looks']</td>\n",
        "      <td> [('5.', 'CD'), ('Round', 'NNP'), ('3', 'CD'), ...</td>\n",
        "      <td>...</td>\n",
        "      <td> 2485</td>\n",
        "      <td> 2188</td>\n",
        "      <td> 1812</td>\n",
        "      <td>-0.436667</td>\n",
        "      <td> 0.308271</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/enhanced/webdr...</td>\n",
        "      <td> 0.534517</td>\n",
        "      <td>   stressed</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3662</th>\n",
        "      <td> 7534</td>\n",
        "      <td> 7534</td>\n",
        "      <td> 75. The \u201cCute But Not Cute Enough To Distract ...</td>\n",
        "      <td> A Complete Ranking Of Everything Britney Spear...</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/16/20/...</td>\n",
        "      <td>                             []</td>\n",
        "      <td>                                   ['Not']</td>\n",
        "      <td>         ['\\xe2\\x80\\x9cCute', 'Less-Than-Stellar']</td>\n",
        "      <td> ['Cute', 'Enough', 'To', 'Distract', 'From', '...</td>\n",
        "      <td> [('75.', 'CD'), ('The', 'DT'), ('\\xe2\\x80\\x9cC...</td>\n",
        "      <td>...</td>\n",
        "      <td> 3805</td>\n",
        "      <td> 3811</td>\n",
        "      <td> 7536</td>\n",
        "      <td>-0.001333</td>\n",
        "      <td>-0.462406</td>\n",
        "      <td> http://s3-ec.buzzfed.com/static/2014-07/16/20/...</td>\n",
        "      <td> 0.462408</td>\n",
        "      <td>    subdued</td>\n",
        "      <td> True</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>72 rows \u00d7 25 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "      Unnamed: 0_x  Unnamed: 0.1  \\\n",
        "6               19            19   \n",
        "7               20            20   \n",
        "3140          6469          6469   \n",
        "3139          6469          6469   \n",
        "2571          5280          5280   \n",
        "2568          5270          5270   \n",
        "2218          4568          4568   \n",
        "2217          4567          4567   \n",
        "2216          4566          4566   \n",
        "2215          4565          4565   \n",
        "2214          4564          4564   \n",
        "2213          4562          4562   \n",
        "2204          4539          4539   \n",
        "2090          4334          4334   \n",
        "1823          3775          3775   \n",
        "1822          3775          3775   \n",
        "1800          3711          3711   \n",
        "3150          6490          6490   \n",
        "3159          6506          6506   \n",
        "3160          6506          6506   \n",
        "3486          7165          7165   \n",
        "3652          7507          7507   \n",
        "3498          7192          7192   \n",
        "3490          7168          7168   \n",
        "3489          7167          7167   \n",
        "3488          7165          7165   \n",
        "3487          7165          7165   \n",
        "3485          7159          7159   \n",
        "3161          6513          6513   \n",
        "3484          7159          7159   \n",
        "...            ...           ...   \n",
        "656           1333          1333   \n",
        "630           1287          1287   \n",
        "456            946           946   \n",
        "235            501           501   \n",
        "902           1807          1807   \n",
        "199            429           429   \n",
        "198            429           429   \n",
        "11              23            23   \n",
        "10              22            22   \n",
        "9               21            21   \n",
        "8               20            20   \n",
        "901           1806          1806   \n",
        "903           1809          1809   \n",
        "1796          3703          3703   \n",
        "1789          3692          3692   \n",
        "1795          3701          3701   \n",
        "1794          3700          3700   \n",
        "1793          3700          3700   \n",
        "1792          3695          3695   \n",
        "1791          3692          3692   \n",
        "1790          3692          3692   \n",
        "1788          3690          3690   \n",
        "904           1809          1809   \n",
        "1787          3690          3690   \n",
        "1786          3688          3688   \n",
        "1546          3183          3183   \n",
        "907           1814          1814   \n",
        "906           1814          1814   \n",
        "905           1810          1810   \n",
        "3662          7534          7534   \n",
        "\n",
        "                                                   Text  \\\n",
        "6     And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "7                    Your link was successfully shared!   \n",
        "3140                     And dancing!Tom strikes again!   \n",
        "3139                     And dancing!Tom strikes again!   \n",
        "2571                                  16. More dancing:   \n",
        "2568  6. Arguing because the DJ won\u2019t play \u2018Just Dan...   \n",
        "2218  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2217                                      14. Spot. On.   \n",
        "2216  13. \u2026and is basically Gob Bluth whilst doing a...   \n",
        "2215  12. And finally, Socks dances to \u201cThe Final Co...   \n",
        "2214  9. In this advert, Socks falls deeply in love ...   \n",
        "2213         5. \u2026all through the faux, CGI countryside.   \n",
        "2204  5. When you go out, Alcohol is always your dan...   \n",
        "2090                     17. Voldemort can dance again.   \n",
        "1823           2. \u2026and you have your own dance routine.   \n",
        "1822           2. \u2026and you have your own dance routine.   \n",
        "1800  Watch the music video for her new single \u201cShak...   \n",
        "3150      18. We learned that white people can\u2019t dance.   \n",
        "3159                        Press play and scroll down!   \n",
        "3160                        Press play and scroll down!   \n",
        "3486                    8. Fred Astaire\u2019s Spirit Animal   \n",
        "3652             102. The \u201cAnarchist Dance Troupe\u201d Look   \n",
        "3498            22. 22. Veronica dancing with her crew.   \n",
        "3490                               11. The Limb Flailer   \n",
        "3489                       10. Beyonce\u2019s Backing Dancer   \n",
        "3488                    8. Fred Astaire\u2019s Spirit Animal   \n",
        "3487                    8. Fred Astaire\u2019s Spirit Animal   \n",
        "3485                              2. The Secret Crunker   \n",
        "3161           7. Let me hear you say \u201cscrub da ground\u201d   \n",
        "3484                              2. The Secret Crunker   \n",
        "...                                                 ...   \n",
        "656                            6. She sings AND dances.   \n",
        "630                       6. 6. Dance like this at prom   \n",
        "456                          6. \u201c\u2026\u2026ilu future husband\u2026\u201d   \n",
        "235   2. Werk it, girl. Dancing naked in the privacy...   \n",
        "902   2. But the girls won\u2019t go down without doing a...   \n",
        "199          28. When he showed off his dancing skills:   \n",
        "198          28. When he showed off his dancing skills:   \n",
        "11                                                  NaN   \n",
        "10    Note: once you save these links, they will no ...   \n",
        "9                                             More Buzz   \n",
        "8                    Your link was successfully shared!   \n",
        "901   1. Round 1: Taylor goes in first with a few sa...   \n",
        "903   4. But Taylor didn\u2019t count on them knowing tha...   \n",
        "1796                               8. She can bunnyhop!   \n",
        "1789  19. Wait, is this an actual dance move? Hips d...   \n",
        "1795                                10. Spirit fingers!   \n",
        "1794                  11. Shake it off and reel \u2018em in.   \n",
        "1793                  11. Shake it off and reel \u2018em in.   \n",
        "1792  16. She\u2019s got more limbs than she knows what t...   \n",
        "1791  19. Wait, is this an actual dance move? Hips d...   \n",
        "1790  19. Wait, is this an actual dance move? Hips d...   \n",
        "1788                  21. So grace. Much elegance. Wow.   \n",
        "904   4. But Taylor didn\u2019t count on them knowing tha...   \n",
        "1787                  21. So grace. Much elegance. Wow.   \n",
        "1786         23. Check out that mildly tricky footwork!   \n",
        "1546     3. Sleeping Beauty on \u201cDancing with the Stars\u201d   \n",
        "907                           9. Winner: Tegan and Sara   \n",
        "906                           9. Winner: Tegan and Sara   \n",
        "905   5. Round 3: Taylor goes all out, giving sassy ...   \n",
        "3662  75. The \u201cCute But Not Cute Enough To Distract ...   \n",
        "\n",
        "                                                  Title  \\\n",
        "6     And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "7     And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "3140  10 Reasons \"Thor: The Dark World\" Is Really Th...   \n",
        "3139  10 Reasons \"Thor: The Dark World\" Is Really Th...   \n",
        "2571  27 GIFs That Prove Lumpy Space Princess From \"...   \n",
        "2568  27 GIFs That Prove Lumpy Space Princess From \"...   \n",
        "2218  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2217  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2216  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2215  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2214  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2213  We Could All Use More Dancing Shetland Ponies ...   \n",
        "2204         25 Signs Alcohol Is Your Significant Other   \n",
        "2090  19 Reasons Why \"A Very Potter Musical\" Is Time...   \n",
        "1823  17 Signs You And Your Best Friend Are Turning ...   \n",
        "1822  17 Signs You And Your Best Friend Are Turning ...   \n",
        "1800  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "3150     33 Lessons Saturday Morning Cartoons Taught Us   \n",
        "3159             16 Animals Dancing To A '90s Booty Jam   \n",
        "3160             16 Animals Dancing To A '90s Booty Jam   \n",
        "3486           13 Dogs You Might See On The Dance Floor   \n",
        "3652  A Complete Ranking Of Everything Britney Spear...   \n",
        "3498  The 25 Best Moments Of The \"Veronica Mars\" Mov...   \n",
        "3490           13 Dogs You Might See On The Dance Floor   \n",
        "3489           13 Dogs You Might See On The Dance Floor   \n",
        "3488           13 Dogs You Might See On The Dance Floor   \n",
        "3487           13 Dogs You Might See On The Dance Floor   \n",
        "3485           13 Dogs You Might See On The Dance Floor   \n",
        "3161             16 Animals Dancing To A '90s Booty Jam   \n",
        "3484           13 Dogs You Might See On The Dance Floor   \n",
        "...                                                 ...   \n",
        "656   15 Reasons Jessica Lange Should Stay On \"Ameri...   \n",
        "630   20 Things You Wanted To Do In High School But ...   \n",
        "456   And Now For Martin Freeman Dancing Awkwardly I...   \n",
        "235    18 Flawless Beauty Tips From \"Fantasia\" Centaurs   \n",
        "902   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "199       29 Times Harry Styles Was Actually The Cutest   \n",
        "198       29 Times Harry Styles Was Actually The Cutest   \n",
        "11    And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "10    And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "9     And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "8     And Now For 8 \"Mass Effect\" Characters Dancing...   \n",
        "901   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "903   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "1796  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1789  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1795  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1794  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1793  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1792  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1791  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1790  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1788  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "904   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "1787  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1786  27 GIFs Of Taylor Swift Dancing Awkwardly In H...   \n",
        "1546         If Disney Princesses Were Reality TV Stars   \n",
        "907   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "906   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "905   Watch Taylor Swift Challenge Tegan And Sara To...   \n",
        "3662  A Complete Ranking Of Everything Britney Spear...   \n",
        "\n",
        "                                                    URL  \\\n",
        "6     http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
        "7     http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
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        "2568  http://s3-ec.buzzfed.com/static/2014-07/29/21/...   \n",
        "2218  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "2217  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "2216  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "2215  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "2214  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "2213  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
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        "3489  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "3488  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "3487  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "3485  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "3161  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "3484  http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "...                                                 ...   \n",
        "656   http://s3-ec.buzzfed.com/static/2014-09/25/0/e...   \n",
        "630   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "456   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "235   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "902   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "199   http://s3-ec.buzzfed.com/static/2014-02/enhanc...   \n",
        "198   http://s3-ec.buzzfed.com/static/2014-02/enhanc...   \n",
        "11    http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
        "10    http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
        "9     http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
        "8     http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
        "901   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "903   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "1796  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
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        "1793  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "1792  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "1791  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "1790  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "1788  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "904   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "1787  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "1786  http://s3-ec.buzzfed.com/static/2014-08/18/18/...   \n",
        "1546  http://s3-ec.buzzfed.com/static/2013-10/enhanc...   \n",
        "907   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "906   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "905   http://s3-ec.buzzfed.com/static/enhanced/webdr...   \n",
        "3662  http://s3-ec.buzzfed.com/static/2014-07/16/20/...   \n",
        "\n",
        "                          text verbs  \\\n",
        "6                                 []   \n",
        "7                  ['was', 'shared']   \n",
        "3140          ['dancing', 'strikes']   \n",
        "3139          ['dancing', 'strikes']   \n",
        "2571                              []   \n",
        "2568                              []   \n",
        "2218                         ['All']   \n",
        "2217                              []   \n",
        "2216                 ['is', 'doing']   \n",
        "2215                              []   \n",
        "2214                              []   \n",
        "2213                              []   \n",
        "2204                    ['go', 'is']   \n",
        "2090                       ['dance']   \n",
        "1823                        ['have']   \n",
        "1822                        ['have']   \n",
        "1800                         ['see']   \n",
        "3150                     ['learned']   \n",
        "3159                              []   \n",
        "3160                              []   \n",
        "3486                        ['Fred']   \n",
        "3652                              []   \n",
        "3498                              []   \n",
        "3490                              []   \n",
        "3489                              []   \n",
        "3488                        ['Fred']   \n",
        "3487                        ['Fred']   \n",
        "3485                              []   \n",
        "3161                 ['hear', 'say']   \n",
        "3484                              []   \n",
        "...                              ...   \n",
        "656                        ['sings']   \n",
        "630                               []   \n",
        "456                               []   \n",
        "235                        ['naked']   \n",
        "902                        ['doing']   \n",
        "199                       ['showed']   \n",
        "198                       ['showed']   \n",
        "11                                []   \n",
        "10                  ['Note', 'save']   \n",
        "9                                 []   \n",
        "8                  ['was', 'shared']   \n",
        "901                         ['goes']   \n",
        "903                      ['knowing']   \n",
        "1796                    ['bunnyhop']   \n",
        "1789                          ['is']   \n",
        "1795                              []   \n",
        "1794                              []   \n",
        "1793                              []   \n",
        "1792          ['got', 'knows', 'do']   \n",
        "1791                          ['is']   \n",
        "1790                          ['is']   \n",
        "1788                              []   \n",
        "904                      ['knowing']   \n",
        "1787                              []   \n",
        "1786                              []   \n",
        "1546                              []   \n",
        "907                               []   \n",
        "906                               []   \n",
        "905   ['goes', 'giving', 'audience']   \n",
        "3662                              []   \n",
        "\n",
        "                                   text adverbs  \\\n",
        "6                                       ['Now']   \n",
        "7                              ['successfully']   \n",
        "3140                                  ['again']   \n",
        "3139                                  ['again']   \n",
        "2571                                         []   \n",
        "2568                                         []   \n",
        "2218                                         []   \n",
        "2217                                         []   \n",
        "2216                              ['basically']   \n",
        "2215                                ['finally']   \n",
        "2214                                 ['deeply']   \n",
        "2213                                         []   \n",
        "2204                          ['out', 'always']   \n",
        "2090                                  ['again']   \n",
        "1823                                         []   \n",
        "1822                                         []   \n",
        "1800                          ['intentionally']   \n",
        "3150                                         []   \n",
        "3159                                         []   \n",
        "3160                                         []   \n",
        "3486                                         []   \n",
        "3652                                         []   \n",
        "3498                                         []   \n",
        "3490                                         []   \n",
        "3489                                         []   \n",
        "3488                                         []   \n",
        "3487                                         []   \n",
        "3485                                         []   \n",
        "3161                                         []   \n",
        "3484                                         []   \n",
        "...                                         ...   \n",
        "656                                          []   \n",
        "630                                          []   \n",
        "456                                          []   \n",
        "235                                          []   \n",
        "902                                          []   \n",
        "199                                     ['off']   \n",
        "198                                     ['off']   \n",
        "11                                           []   \n",
        "10    ['once', 'no', 'longer', 'automatically']   \n",
        "9                                            []   \n",
        "8                              ['successfully']   \n",
        "901                                          []   \n",
        "903                                     ['too']   \n",
        "1796                                         []   \n",
        "1789                                         []   \n",
        "1795                                         []   \n",
        "1794                                         []   \n",
        "1793                                         []   \n",
        "1792                                         []   \n",
        "1791                                         []   \n",
        "1790                                         []   \n",
        "1788                                     ['So']   \n",
        "904                                     ['too']   \n",
        "1787                                     ['So']   \n",
        "1786                                 ['mildly']   \n",
        "1546                                         []   \n",
        "907                                          []   \n",
        "906                                          []   \n",
        "905                                          []   \n",
        "3662                                    ['Not']   \n",
        "\n",
        "                                        text adjectives  \\\n",
        "6                                                    []   \n",
        "7                                                    []   \n",
        "3140                                                 []   \n",
        "3139                                                 []   \n",
        "2571                               ['More', 'dancing:']   \n",
        "2568                               ['\\xe2\\x80\\x98Just']   \n",
        "2218                                                 []   \n",
        "2217                                                 []   \n",
        "2216  ['\\xe2\\x80\\xa6and', '\\xe2\\x80\\x9cillusion.\\xe2...   \n",
        "2215                                                 []   \n",
        "2214                              ['\\xe2\\x80\\x9cTotal']   \n",
        "2213                                ['\\xe2\\x80\\xa6all']   \n",
        "2204                                                 []   \n",
        "2090                                                 []   \n",
        "1823                         ['\\xe2\\x80\\xa6and', 'own']   \n",
        "1822                         ['\\xe2\\x80\\xa6and', 'own']   \n",
        "1800                                     ['new', 'bad']   \n",
        "3150                      ['white', 'can\\xe2\\x80\\x99t']   \n",
        "3159                                                 []   \n",
        "3160                                                 []   \n",
        "3486                                                 []   \n",
        "3652                          ['\\xe2\\x80\\x9cAnarchist']   \n",
        "3498                                                 []   \n",
        "3490                                                 []   \n",
        "3489                                                 []   \n",
        "3488                                                 []   \n",
        "3487                                                 []   \n",
        "3485                                                 []   \n",
        "3161                                                 []   \n",
        "3484                                                 []   \n",
        "...                                                 ...   \n",
        "656                                                  []   \n",
        "630                                                  []   \n",
        "456   ['\\xe2\\x80\\x9c\\xe2\\x80\\xa6\\xe2\\x80\\xa6ilu', 'f...   \n",
        "235                                        ['all-girl']   \n",
        "902                  ['won\\xe2\\x80\\x99t', 'few', 'own']   \n",
        "199                                                  []   \n",
        "198                                                  []   \n",
        "11                                                   []   \n",
        "10                                           ['update']   \n",
        "9                                              ['More']   \n",
        "8                                                    []   \n",
        "901                           ['first', 'few', 'sassy']   \n",
        "903                                                  []   \n",
        "1796                                                 []   \n",
        "1789                                         ['actual']   \n",
        "1795                                                 []   \n",
        "1794                                                 []   \n",
        "1793                                                 []   \n",
        "1792                                           ['more']   \n",
        "1791                                         ['actual']   \n",
        "1790                                         ['actual']   \n",
        "1788                                                 []   \n",
        "904                                                  []   \n",
        "1787                                                 []   \n",
        "1786                                         ['tricky']   \n",
        "1546       ['\\xe2\\x80\\x9cDancing', 'Stars\\xe2\\x80\\x9d']   \n",
        "907                                                  []   \n",
        "906                                                  []   \n",
        "905                                           ['sassy']   \n",
        "3662          ['\\xe2\\x80\\x9cCute', 'Less-Than-Stellar']   \n",
        "\n",
        "                                             text nouns  \\\n",
        "6     ['For', 'Mass', 'Effect', 'Characters', 'Danci...   \n",
        "7                                              ['link']   \n",
        "3140                                            ['Tom']   \n",
        "3139                                            ['Tom']   \n",
        "2571                                                 []   \n",
        "2568  ['Arguing', 'DJ', 'won\\xe2\\x80\\x99t', 'play', ...   \n",
        "2218  ['Use', 'More', 'Dancing', 'Shetland', 'Ponies...   \n",
        "2217                                    ['Spot.', 'On']   \n",
        "2216                         ['Gob', 'Bluth', 'whilst']   \n",
        "2215  ['Socks', 'dances', '\\xe2\\x80\\x9cThe', 'Final'...   \n",
        "2214  ['advert', 'Socks', 'falls', 'love', 'dances',...   \n",
        "2213                     ['faux', 'CGI', 'countryside']   \n",
        "2204                    ['Alcohol', 'dance', 'partner']   \n",
        "2090                                      ['Voldemort']   \n",
        "1823                               ['dance', 'routine']   \n",
        "1822                               ['dance', 'routine']   \n",
        "1800  ['Watch', 'music', 'video', 'single', '\\xe2\\x8...   \n",
        "3150                                ['people', 'dance']   \n",
        "3159                        ['Press', 'play', 'scroll']   \n",
        "3160                        ['Press', 'play', 'scroll']   \n",
        "3486                               ['Spirit', 'Animal']   \n",
        "3652            ['Dance', 'Troupe\\xe2\\x80\\x9d', 'Look']   \n",
        "3498                    ['Veronica', 'dancing', 'crew']   \n",
        "3490                                ['Limb', 'Flailer']   \n",
        "3489      ['Beyonce\\xe2\\x80\\x99s', 'Backing', 'Dancer']   \n",
        "3488                               ['Spirit', 'Animal']   \n",
        "3487                               ['Spirit', 'Animal']   \n",
        "3485                              ['Secret', 'Crunker']   \n",
        "3161                                      ['Let', 'da']   \n",
        "3484                              ['Secret', 'Crunker']   \n",
        "...                                                 ...   \n",
        "656                                          ['dances']   \n",
        "630                                   ['Dance', 'prom']   \n",
        "456                 ['husband\\xe2\\x80\\xa6\\xe2\\x80\\x9d']   \n",
        "235   ['Werk', 'girl.', 'Dancing', 'privacy', 'harem...   \n",
        "902                                     ['girls', 'go']   \n",
        "199                                         ['dancing']   \n",
        "198                                         ['dancing']   \n",
        "11                                                   []   \n",
        "10                                            ['links']   \n",
        "9                                              ['Buzz']   \n",
        "8                                              ['link']   \n",
        "901             ['Round', 'Taylor', 'shoulder', 'pops']   \n",
        "903            ['Taylor', 'didn\\xe2\\x80\\x99t', 'count']   \n",
        "1796                                                 []   \n",
        "1789  ['Wait', 'dance', 'move', 'Hips', 'don\\xe2\\x80...   \n",
        "1795                              ['Spirit', 'fingers']   \n",
        "1794                ['Shake', 'reel', '\\xe2\\x80\\x98em']   \n",
        "1793                ['Shake', 'reel', '\\xe2\\x80\\x98em']   \n",
        "1792                      ['She\\xe2\\x80\\x99s', 'limbs']   \n",
        "1791  ['Wait', 'dance', 'move', 'Hips', 'don\\xe2\\x80...   \n",
        "1790  ['Wait', 'dance', 'move', 'Hips', 'don\\xe2\\x80...   \n",
        "1788             ['grace.', 'Much', 'elegance.', 'Wow']   \n",
        "904            ['Taylor', 'didn\\xe2\\x80\\x99t', 'count']   \n",
        "1787             ['grace.', 'Much', 'elegance.', 'Wow']   \n",
        "1786                              ['Check', 'footwork']   \n",
        "1546                             ['Sleeping', 'Beauty']   \n",
        "907                         ['Winner', 'Tegan', 'Sara']   \n",
        "906                         ['Winner', 'Tegan', 'Sara']   \n",
        "905                        ['Round', 'Taylor', 'looks']   \n",
        "3662  ['Cute', 'Enough', 'To', 'Distract', 'From', '...   \n",
        "\n",
        "                                          all text tags  \\\n",
        "6     [('And', 'CC'), ('Now', 'RB'), ('For', 'NNP'),...   \n",
        "7     [('Your', 'PRP$'), ('link', 'NN'), ('was', 'VB...   \n",
        "3140  [('And', 'CC'), ('dancing', 'VBG'), ('!', '.')...   \n",
        "3139  [('And', 'CC'), ('dancing', 'VBG'), ('!', '.')...   \n",
        "2571  [('16.', 'CD'), ('More', 'JJR'), ('dancing:', ...   \n",
        "2568  [('6.', 'CD'), ('Arguing', 'NNP'), ('because',...   \n",
        "2218  [('We', 'PRP'), ('Could', 'MD'), ('All', 'VB')...   \n",
        "2217  [('14.', 'CD'), ('Spot.', 'NNP'), ('On', 'NNP'...   \n",
        "2216  [('13.', 'CD'), ('\\xe2\\x80\\xa6and', 'JJ'), ('i...   \n",
        "2215  [('12.', 'CD'), ('And', 'CC'), ('finally', 'RB...   \n",
        "2214  [('9.', 'CD'), ('In', 'IN'), ('this', 'DT'), (...   \n",
        "2213  [('5.', 'CD'), ('\\xe2\\x80\\xa6all', 'JJ'), ('th...   \n",
        "2204  [('5.', 'CD'), ('When', 'WRB'), ('you', 'PRP')...   \n",
        "2090  [('17.', 'CD'), ('Voldemort', 'NNP'), ('can', ...   \n",
        "1823  [('2.', 'CD'), ('\\xe2\\x80\\xa6and', 'JJ'), ('yo...   \n",
        "1822  [('2.', 'CD'), ('\\xe2\\x80\\xa6and', 'JJ'), ('yo...   \n",
        "1800  [('Watch', 'NN'), ('the', 'DT'), ('music', 'NN...   \n",
        "3150  [('18.', 'CD'), ('We', 'PRP'), ('learned', 'VB...   \n",
        "3159  [('Press', 'NN'), ('play', 'NN'), ('and', 'CC'...   \n",
        "3160  [('Press', 'NN'), ('play', 'NN'), ('and', 'CC'...   \n",
        "3486  [('8.', 'CD'), ('Fred', 'VBN'), ('Astaire\\xe2\\...   \n",
        "3652  [('102.', 'CD'), ('The', 'DT'), ('\\xe2\\x80\\x9c...   \n",
        "3498  [('22.', 'CD'), ('22.', 'CD'), ('Veronica', 'N...   \n",
        "3490  [('11.', 'CD'), ('The', 'DT'), ('Limb', 'NNP')...   \n",
        "3489  [('10.', 'CD'), ('Beyonce\\xe2\\x80\\x99s', 'NNS'...   \n",
        "3488  [('8.', 'CD'), ('Fred', 'VBN'), ('Astaire\\xe2\\...   \n",
        "3487  [('8.', 'CD'), ('Fred', 'VBN'), ('Astaire\\xe2\\...   \n",
        "3485  [('2.', 'CD'), ('The', 'DT'), ('Secret', 'NNP'...   \n",
        "3161  [('7.', 'CD'), ('Let', 'NNP'), ('me', 'PRP'), ...   \n",
        "3484  [('2.', 'CD'), ('The', 'DT'), ('Secret', 'NNP'...   \n",
        "...                                                 ...   \n",
        "656   [('6.', 'CD'), ('She', 'PRP'), ('sings', 'VBZ'...   \n",
        "630   [('6.', 'CD'), ('6.', 'CD'), ('Dance', 'NNP'),...   \n",
        "456   [('6.', 'CD'), ('\\xe2\\x80\\x9c\\xe2\\x80\\xa6\\xe2\\...   \n",
        "235   [('2.', 'CD'), ('Werk', 'NNP'), ('it', 'PRP'),...   \n",
        "902   [('2.', 'CD'), ('But', 'CC'), ('the', 'DT'), (...   \n",
        "199   [('28.', 'CD'), ('When', 'WRB'), ('he', 'PRP')...   \n",
        "198   [('28.', 'CD'), ('When', 'WRB'), ('he', 'PRP')...   \n",
        "11                                                   []   \n",
        "10    [('Note', 'VB'), (':', ':'), ('once', 'RB'), (...   \n",
        "9                    [('More', 'JJR'), ('Buzz', 'NNP')]   \n",
        "8     [('Your', 'PRP$'), ('link', 'NN'), ('was', 'VB...   \n",
        "901   [('1.', 'CD'), ('Round', 'NNP'), ('1', 'CD'), ...   \n",
        "903   [('4.', 'CD'), ('But', 'CC'), ('Taylor', 'NNP'...   \n",
        "1796  [('8.', 'CD'), ('She', 'PRP'), ('can', 'MD'), ...   \n",
        "1789  [('19.', 'CD'), ('Wait', 'NNP'), (',', ','), (...   \n",
        "1795  [('10.', 'CD'), ('Spirit', 'NN'), ('fingers', ...   \n",
        "1794  [('11.', 'CD'), ('Shake', 'NNP'), ('it', 'PRP'...   \n",
        "1793  [('11.', 'CD'), ('Shake', 'NNP'), ('it', 'PRP'...   \n",
        "1792  [('16.', 'CD'), ('She\\xe2\\x80\\x99s', 'NNS'), (...   \n",
        "1791  [('19.', 'CD'), ('Wait', 'NNP'), (',', ','), (...   \n",
        "1790  [('19.', 'CD'), ('Wait', 'NNP'), (',', ','), (...   \n",
        "1788  [('21.', 'CD'), ('So', 'RB'), ('grace.', 'NNP'...   \n",
        "904   [('4.', 'CD'), ('But', 'CC'), ('Taylor', 'NNP'...   \n",
        "1787  [('21.', 'CD'), ('So', 'RB'), ('grace.', 'NNP'...   \n",
        "1786  [('23.', 'CD'), ('Check', 'NNP'), ('out', 'IN'...   \n",
        "1546  [('3.', 'CD'), ('Sleeping', 'NNP'), ('Beauty',...   \n",
        "907   [('9.', 'CD'), ('Winner', 'NNP'), (':', ':'), ...   \n",
        "906   [('9.', 'CD'), ('Winner', 'NNP'), (':', ':'), ...   \n",
        "905   [('5.', 'CD'), ('Round', 'NNP'), ('3', 'CD'), ...   \n",
        "3662  [('75.', 'CD'), ('The', 'DT'), ('\\xe2\\x80\\x9cC...   \n",
        "\n",
        "                            ...                         Unnamed: 0_y    id  \\\n",
        "6                           ...                                 2818  2538   \n",
        "7                           ...                                 3637  3549   \n",
        "3140                        ...                                  227  1349   \n",
        "3139                        ...                                  226   272   \n",
        "2571                        ...                                 2312  2030   \n",
        "2568                        ...                                  221   267   \n",
        "2218                        ...                                 3580  3458   \n",
        "2217                        ...                                 3852  3891   \n",
        "2216                        ...                                 2269  1988   \n",
        "2215                        ...                                 3021  2775   \n",
        "2214                        ...                                 2061  1778   \n",
        "2213                        ...                                 2224  1942   \n",
        "2204                        ...                                 3342  3155   \n",
        "2090                        ...                                 2225  1943   \n",
        "1823                        ...                                 3322  3471   \n",
        "1822                        ...                                 3321  3130   \n",
        "1800                        ...                                  858  3349   \n",
        "3150                        ...                                 2949  2692   \n",
        "3159                        ...                                  169   233   \n",
        "3160                        ...                                  170  3574   \n",
        "3486                        ...                                  851   727   \n",
        "3652                        ...                                 3273  3070   \n",
        "3498                        ...                                 2565  2263   \n",
        "3490                        ...                                 3235  3024   \n",
        "3489                        ...                                 1820  1565   \n",
        "3488                        ...                                  853  2847   \n",
        "3487                        ...                                  852  1350   \n",
        "3485                        ...                                  653  3052   \n",
        "3161                        ...                                 2469  2172   \n",
        "3484                        ...                                  652   576   \n",
        "...                         ...                                  ...   ...   \n",
        "656                         ...                                 1576  1328   \n",
        "630                         ...                                 2369  2083   \n",
        "456                         ...                                 3258  3053   \n",
        "235                         ...                                  938   802   \n",
        "902                         ...                                 2143  1860   \n",
        "199                         ...                                 1602  1583   \n",
        "198                         ...                                 1601  1351   \n",
        "11                          ...                                 1961  1686   \n",
        "10                          ...                                 2136  1848   \n",
        "9                           ...                                 2670  2378   \n",
        "8                           ...                                 3638  3887   \n",
        "901                         ...                                 2397  2112   \n",
        "903                         ...                                 3198  2983   \n",
        "1796                        ...                                 1643  1391   \n",
        "1789                        ...                                  395   390   \n",
        "1795                        ...                                 1034   885   \n",
        "1794                        ...                                 1568  2129   \n",
        "1793                        ...                                 1567  1322   \n",
        "1792                        ...                                 1958  1683   \n",
        "1791                        ...                                  397  2670   \n",
        "1790                        ...                                  396  2669   \n",
        "1788                        ...                                 3212  3367   \n",
        "904                         ...                                 3199  3710   \n",
        "1787                        ...                                 3211  2993   \n",
        "1786                        ...                                 1720  1463   \n",
        "1546                        ...                                 3366  3184   \n",
        "907                         ...                                 2479  2184   \n",
        "906                         ...                                 2478  2182   \n",
        "905                         ...                                 2485  2188   \n",
        "3662                        ...                                 3805  3811   \n",
        "\n",
        "     gif_id         x         y  \\\n",
        "6        21 -0.368056 -0.035156   \n",
        "7        22  0.318000  0.075188   \n",
        "3140   6471  0.378667  0.304511   \n",
        "3139   6471 -0.002667  0.112782   \n",
        "2571   5282  0.407639  0.035156   \n",
        "2568   5272 -0.004000 -0.003759   \n",
        "2218   4570  0.372464 -0.327869   \n",
        "2217   4569 -0.440000 -0.093985   \n",
        "2216   4568  0.460000  0.041353   \n",
        "2215   4567  0.388000  0.131579   \n",
        "2214   4566  0.002667 -0.157895   \n",
        "2213   4564  0.453333  0.157895   \n",
        "2204   4541  0.454667  0.041353   \n",
        "2090   4336  0.436000 -0.135338   \n",
        "1823   3777  0.447826  0.188525   \n",
        "1822   3777  0.298667  0.304511   \n",
        "1800   3713 -0.304700  0.376712   \n",
        "3150   6492 -0.210000 -0.406015   \n",
        "3159   6508  0.080667  0.345865   \n",
        "3160   6508  0.244667  0.251880   \n",
        "3486   7167  0.282000  0.398496   \n",
        "3652   7509 -0.394667 -0.097744   \n",
        "3498   7194 -0.014583  0.472656   \n",
        "3490   7170  0.293333  0.368421   \n",
        "3489   7169  0.303571  0.298165   \n",
        "3488   7167  0.292667  0.349624   \n",
        "3487   7167  0.217532  0.284404   \n",
        "3485   7161 -0.001621  0.376712   \n",
        "3161   6515  0.479333  0.481203   \n",
        "3484   7161 -0.359722  0.288235   \n",
        "...     ...       ...       ...   \n",
        "656    1335  0.356000  0.052632   \n",
        "630    1289  0.295333  0.161654   \n",
        "456     948  0.275527  0.331050   \n",
        "235     503 -0.149109 -0.431507   \n",
        "902    1809  0.000000  0.018797   \n",
        "199     431  0.134740 -0.399083   \n",
        "198     431  0.250000  0.183486   \n",
        "11       25  0.406250 -0.148438   \n",
        "10       24  0.029333  0.206767   \n",
        "9        23  0.060000  0.018797   \n",
        "8        22  0.297333  0.233083   \n",
        "901    1808  0.439222  0.207763   \n",
        "903    1811  0.169368  0.230594   \n",
        "1796   3705  0.081169  0.114679   \n",
        "1789   3694 -0.180556 -0.054688   \n",
        "1795   3703  0.301333  0.251880   \n",
        "1794   3702  0.471333  0.477444   \n",
        "1793   3702 -0.130667 -0.402256   \n",
        "1792   3697  0.000000 -0.015038   \n",
        "1791   3694  0.317241  0.208738   \n",
        "1790   3694  0.313793  0.383495   \n",
        "1788   3692  0.110345 -0.257282   \n",
        "904    1811  0.264000  0.146617   \n",
        "1787   3692 -0.065640 -0.075342   \n",
        "1786   3690  0.269481  0.229358   \n",
        "1546   3185  0.457333  0.154135   \n",
        "907    1816  0.479333  0.481203   \n",
        "906    1816  0.479333  0.481203   \n",
        "905    1812 -0.436667  0.308271   \n",
        "3662   7536 -0.001333 -0.462406   \n",
        "\n",
        "                                                    url  strength     emotion  \\\n",
        "6     http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.369731  unpleasant   \n",
        "7     http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.326768       happy   \n",
        "3140  http://s3-ec.buzzfed.com/static/2013-11/enhanc...  0.485917      elated   \n",
        "3139  http://s3-ec.buzzfed.com/static/2013-11/enhanc...  0.112814      active   \n",
        "2571  http://s3-ec.buzzfed.com/static/2014-07/29/21/...  0.409152    pleasant   \n",
        "2568  http://s3-ec.buzzfed.com/static/2014-07/29/21/...  0.005489     unhappy   \n",
        "2218  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.496213      serene   \n",
        "2217  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.449926         sad   \n",
        "2216  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.461855    pleasant   \n",
        "2215  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.409704       happy   \n",
        "2214  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.157918     subdued   \n",
        "2213  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.480043       happy   \n",
        "2204  http://s3-ec.buzzfed.com/static/2013-12/enhanc...  0.456544    pleasant   \n",
        "2090  http://s3-ec.buzzfed.com/static/2014-06/29/23/...  0.456522   contented   \n",
        "1823  http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.485891       happy   \n",
        "1822  http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.426531     excited   \n",
        "1800  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.484514     nervous   \n",
        "3150  http://s3-ec.buzzfed.com/static/2014-03/enhanc...  0.457108   depressed   \n",
        "3159  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.355148       alert   \n",
        "3160  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.351149     excited   \n",
        "3486  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.488183     excited   \n",
        "3652  http://s3-ec.buzzfed.com/static/2014-07/16/21/...  0.406591         sad   \n",
        "3498  http://s3-ec.buzzfed.com/static/2014-01/enhanc...  0.472881      active   \n",
        "3490  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.470933     excited   \n",
        "3489  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.425509      elated   \n",
        "3488  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.455951     excited   \n",
        "3487  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.358058     excited   \n",
        "3485  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.376715      active   \n",
        "3161  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.679203     excited   \n",
        "3484  http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.460955    stressed   \n",
        "...                                                 ...       ...         ...   \n",
        "656   http://s3-ec.buzzfed.com/static/2014-09/25/0/e...  0.359870    pleasant   \n",
        "630   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.336680      elated   \n",
        "456   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.430708     excited   \n",
        "235   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.456543       bored   \n",
        "902   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.018797      active   \n",
        "199   http://s3-ec.buzzfed.com/static/2014-02/enhanc...  0.421215        calm   \n",
        "198   http://s3-ec.buzzfed.com/static/2014-02/enhanc...  0.310108      elated   \n",
        "11    http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.432519   contented   \n",
        "10    http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.208837      active   \n",
        "9     http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.062875       happy   \n",
        "8     http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.377802      elated   \n",
        "901   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.485882       happy   \n",
        "903   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.286110     excited   \n",
        "1796  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.140498     excited   \n",
        "1789  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.188656         sad   \n",
        "1795  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.392741      elated   \n",
        "1794  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.670900     excited   \n",
        "1793  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.422947       bored   \n",
        "1792  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.015038     subdued   \n",
        "1791  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.379754      elated   \n",
        "1790  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.495514     excited   \n",
        "1788  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.279947        calm   \n",
        "904   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.301981      elated   \n",
        "1787  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.099925   depressed   \n",
        "1786  http://s3-ec.buzzfed.com/static/2014-08/18/18/...  0.353872      elated   \n",
        "1546  http://s3-ec.buzzfed.com/static/2013-10/enhanc...  0.482609       happy   \n",
        "907   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.679203     excited   \n",
        "906   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.679203     excited   \n",
        "905   http://s3-ec.buzzfed.com/static/enhanced/webdr...  0.534517    stressed   \n",
        "3662  http://s3-ec.buzzfed.com/static/2014-07/16/20/...  0.462408     subdued   \n",
        "\n",
        "      Match  N Matches  \n",
        "6      True          1  \n",
        "7      True          1  \n",
        "3140   True          1  \n",
        "3139   True          1  \n",
        "2571   True          1  \n",
        "2568   True          1  \n",
        "2218   True          1  \n",
        "2217   True          1  \n",
        "2216   True          1  \n",
        "2215   True          1  \n",
        "2214   True          1  \n",
        "2213   True          1  \n",
        "2204   True          1  \n",
        "2090   True          1  \n",
        "1823   True          1  \n",
        "1822   True          1  \n",
        "1800   True          1  \n",
        "3150   True          1  \n",
        "3159   True          1  \n",
        "3160   True          1  \n",
        "3486   True          1  \n",
        "3652   True          1  \n",
        "3498   True          1  \n",
        "3490   True          1  \n",
        "3489   True          1  \n",
        "3488   True          1  \n",
        "3487   True          1  \n",
        "3485   True          1  \n",
        "3161   True          1  \n",
        "3484   True          1  \n",
        "...     ...        ...  \n",
        "656    True          1  \n",
        "630    True          1  \n",
        "456    True          1  \n",
        "235    True          1  \n",
        "902    True          1  \n",
        "199    True          1  \n",
        "198    True          1  \n",
        "11     True          1  \n",
        "10     True          1  \n",
        "9      True          1  \n",
        "8      True          1  \n",
        "901    True          1  \n",
        "903    True          1  \n",
        "1796   True          1  \n",
        "1789   True          1  \n",
        "1795   True          1  \n",
        "1794   True          1  \n",
        "1793   True          1  \n",
        "1792   True          1  \n",
        "1791   True          1  \n",
        "1790   True          1  \n",
        "1788   True          1  \n",
        "904    True          1  \n",
        "1787   True          1  \n",
        "1786   True          1  \n",
        "1546   True          1  \n",
        "907    True          1  \n",
        "906    True          1  \n",
        "905    True          1  \n",
        "3662   True          1  \n",
        "\n",
        "[72 rows x 25 columns]"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}