PyData-Scattertext-Part-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Scattertext and AgeFromName to find gender-discriminating terms\n",
    "\n",
    "https://github.com/JasonKessler/scattertext\n",
    "\n",
    "https://github.com/JasonKessler/agefromname\n",
    "\n",
    "Cite as:\n",
    "Jason S. Kessler. Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations. 2017.\n",
    "\n",
    "Link to preprint: https://arxiv.org/abs/1703.00565\n",
    "\n",
    "`\n",
    "@article{kessler2017scattertext,\n",
    "  author    = {Kessler, Jason S.},\n",
    "  title     = {Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ},\n",
    "  booktitle = {ACL System Demonstrations},\n",
    "  year      = {2017},\n",
    "}\n",
    "`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:98% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import scattertext as st\n",
    "import re, io, itertools\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import spacy\n",
    "import os, pkgutil, json, urllib\n",
    "from urllib.request import urlopen\n",
    "from IPython.display import IFrame\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:98% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')\n",
    "# If this doesn't work, please uncomment the following line and use a regex-based parser instead\n",
    "#nlp = st.whitespace_nlp_with_sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "convention_df = st.SampleCorpora.ConventionData2012.get_data()\n",
    "convention_df['parsed'] = convention_df.text.apply(nlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `agefromname` package takes \n",
    "### - a first name,\n",
    "### - optional: a minimum age,\n",
    "### - optional: current year\n",
    "## and returns\n",
    "### - the probablity someone is male or female\n",
    "### `pip install agefromname`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from agefromname import AgeFromName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gender_imputer = AgeFromName()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.031370941932688919"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_imputer.prob_male('kelsey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83377422744681196"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_imputer.prob_male('kelsey', minimum_age=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x130027e80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Ojp+tfsECfy/4kCGa8EEk3pS8pdTM4NRT/bgoX33lh5jt0sW3j4tIfKjZRGLigw98Dbxv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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ebf8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.DataFrame([{'Year you encounter a Kelsey': year, \n",
    "                'P(Male|Kelsey)': gender_imputer.prob_male('kelsey', current_year=year)}\n",
    "               for year in range(1930, 2015)])\n",
    " .set_index('Year you encounter a Kelsey')\n",
    " .plot())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's assign speakers a gender if, assuming they're at least 35, \n",
    "## there's a 90% someone with their name is male or female.\n",
    "## Otherwise, drop their speech from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "convention_df['speaker_gender'] = (convention_df.speaker\n",
    " .apply(lambda x: (gender_imputer.prob_male(x.split()[0], minimum_age = 35) if x else 0.5))\n",
    " .apply(lambda x: 'Male' if x > 0.9 else 'Female' if x < 0.1 else '?')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>speaker</th>\n",
       "      <th>text</th>\n",
       "      <th>parsed</th>\n",
       "      <th>speaker_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>democrat</td>\n",
       "      <td>BARACK OBAMA</td>\n",
       "      <td>Thank you. Thank you. Thank you. Thank you so ...</td>\n",
       "      <td>(Thank, you, ., Thank, you, ., Thank, you, ., ...</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>democrat</td>\n",
       "      <td>MICHELLE OBAMA</td>\n",
       "      <td>Thank you so much. Tonight, I am so thrilled a...</td>\n",
       "      <td>(Thank, you, so, much, ., Tonight, ,, I, am, s...</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>democrat</td>\n",
       "      <td>RICHARD DURBIN</td>\n",
       "      <td>Thank you. It is a singular honor to be here t...</td>\n",
       "      <td>(Thank, you, ., It, is, a, singular, honor, to...</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      party         speaker  \\\n",
       "0  democrat    BARACK OBAMA   \n",
       "1  democrat  MICHELLE OBAMA   \n",
       "2  democrat  RICHARD DURBIN   \n",
       "\n",
       "                                                text  \\\n",
       "0  Thank you. Thank you. Thank you. Thank you so ...   \n",
       "1  Thank you so much. Tonight, I am so thrilled a...   \n",
       "2  Thank you. It is a singular honor to be here t...   \n",
       "\n",
       "                                              parsed speaker_gender  \n",
       "0  (Thank, you, ., Thank, you, ., Thank, you, ., ...              ?  \n",
       "1  (Thank, you, so, much, ., Tonight, ,, I, am, s...         Female  \n",
       "2  (Thank, you, ., It, is, a, singular, honor, to...           Male  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convention_df.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      105\n",
       "Female     65\n",
       "?          19\n",
       "Name: speaker_gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convention_df.speaker_gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "convention_df_gender = convention_df[convention_df.speaker_gender.isin(['Male', 'Female'])]\n",
    "convention_df_gender['speaker_party'] = (convention_df_gender['speaker'] \n",
    "                                         + ' (' + convention_df_gender['party'].apply(lambda x: x.upper()[0])  + ')')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's plot the differences in language use by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200\"\n",
       "            height=\"700\"\n",
       "            src=\"output/Conventions2012Gender.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x11f48aeb8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_gender = st.CorpusFromParsedDocuments(convention_df_gender, \n",
    "                                             category_col='speaker_gender', \n",
    "                                             parsed_col='parsed').build()\n",
    "html = st.produce_scattertext_explorer(corpus_gender,\n",
    "                                       category='Female',\n",
    "                                       category_name='Female',\n",
    "                                       not_category_name='Male',\n",
    "                                       minimum_term_frequency=5,\n",
    "                                       width_in_pixels=1000,\n",
    "                                       metadata=convention_df_gender['speaker_party'])\n",
    "file_name = 'output/Conventions2012Gender.html'\n",
    "open(file_name, 'wb').write(html.encode('utf-8'))\n",
    "IFrame(src=file_name, width = 1200, height=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's see how gender and party-associated terms differ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute gender and party scaled f-scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "female_scores = corpus_gender.get_scaled_f_scores('Female')\n",
    "democratic_scores = (st.CorpusFromParsedDocuments(convention_df_gender, \n",
    "                                                  category_col='party', \n",
    "                                                  parsed_col='parsed')\n",
    "                     .build()\n",
    "                     .get_scaled_f_scores('democrat'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use custom coordinates to plot the gender scaled f-score vs. the party scaled f-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200\"\n",
       "            height=\"700\"\n",
       "            src=\"output/Conventions2012GenderAndParty.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x107730f60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html = st.produce_scattertext_explorer(corpus_gender,\n",
    "                                       category='Female',\n",
    "                                       category_name='Female',\n",
    "                                       not_category_name='Male',\n",
    "                                       minimum_term_frequency=5,\n",
    "                                       pmi_filter_thresold=4,\n",
    "                                       width_in_pixels=1000,\n",
    "                                       scores=female_scores,\n",
    "                                       sort_by_dist=False,\n",
    "                                       x_coords=democratic_scores,\n",
    "                                       y_coords=female_scores,\n",
    "                                       show_characteristic=False,\n",
    "                                       metadata=(convention_df_gender['speaker'] \n",
    "                                                 + ' (' \n",
    "                                                 + convention_df_gender['party'].apply(lambda x: x.upper()[0]) \n",
    "                                                 + ')'),\n",
    "                                       x_label='More Democratic',\n",
    "                                       y_label='More Female')\n",
    "file_name = 'output/Conventions2012GenderAndParty.html'\n",
    "open(file_name, 'wb').write(html.encode('utf-8'))\n",
    "IFrame(src=file_name, width = 1200, height=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Women and men appear to discuss different topics \n",
    "## The package Empath uses a crowd-sourced topic model to categorize a given document\n",
    "\n",
    "Fast, Ethan, Binbin Chen, and Michael S. Bernstein. \"Empath: Understanding topic signals in large-scale text.\" Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200\"\n",
       "            height=\"700\"\n",
       "            src=\"output/EmpathGender.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14f1a9400>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empath_corpus = st.CorpusFromParsedDocuments(convention_df_gender,\n",
    "                                             category_col='speaker_gender',\n",
    "                                             feats_from_spacy_doc=st.FeatsFromOnlyEmpath(),\n",
    "                                             parsed_col='text').build()\n",
    "html = st.produce_scattertext_explorer(empath_corpus,\n",
    "                                       category='Female',\n",
    "                                       category_name='Female',\n",
    "                                       not_category_name='Male',\n",
    "                                       width_in_pixels=1000,\n",
    "                                       metadata=convention_df_gender['speaker_party'],\n",
    "                                       use_non_text_features=True,\n",
    "                                       use_full_doc=True)\n",
    "file_name = 'output/EmpathGender.html'\n",
    "open(file_name, 'wb').write(html.encode('utf-8'))\n",
    "IFrame(src=file_name, width = 1200, height=700)\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
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   "name": "Python [Root]"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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