nlp_ideas_sentiment_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:42.667444Z",
          "iopub.status.busy": "2020-10-12T12:26:42.666465Z",
          "iopub.status.idle": "2020-10-12T12:26:49.462818Z",
          "shell.execute_reply": "2020-10-12T12:26:49.461925Z"
        },
        "papermill": {
          "duration": 6.850687,
          "end_time": "2020-10-12T12:26:49.462948",
          "exception": false,
          "start_time": "2020-10-12T12:26:42.612261",
          "status": "completed"
        },
        "tags": [],
        "id": "muAP3q-BLsrI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "tqdm.pandas(desc=\"progress-bar\")\n",
        "from gensim.models import Doc2Vec\n",
        "from sklearn import utils\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "import gensim\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from gensim.models.doc2vec import TaggedDocument\n",
        "import re\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.046251,
          "end_time": "2020-10-12T12:26:49.556802",
          "exception": false,
          "start_time": "2020-10-12T12:26:49.510551",
          "status": "completed"
        },
        "tags": [],
        "id": "u9KN9HB_LsrK"
      },
      "source": [
        "Read Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:49.655670Z",
          "iopub.status.busy": "2020-10-12T12:26:49.654895Z",
          "iopub.status.idle": "2020-10-12T12:26:49.709755Z",
          "shell.execute_reply": "2020-10-12T12:26:49.709002Z"
        },
        "papermill": {
          "duration": 0.106491,
          "end_time": "2020-10-12T12:26:49.709876",
          "exception": false,
          "start_time": "2020-10-12T12:26:49.603385",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P_hrOhT0LsrK",
        "outputId": "95359d08-43a9-4fa9-f999-05e9f06b9807"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Positive Apple announced plans to achieve carbon neutrality by 2030.\n",
              "0  Positive  New solar panel technology could revolutionize...         \n",
              "1  Negative  Tech giants need to prioritize e-waste reduction.         \n",
              "2  Negative  Google's AI ethics committee faces criticism o...         \n",
              "3  Positive  Tesla's electric vehicles are driving the shif...         \n",
              "4  Negative  Amazon's data centers consume enormous amounts...         "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cbf62413-47f7-4b2a-b623-a1e8cd0dcdeb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Positive</th>\n",
              "      <th>Apple announced plans to achieve carbon neutrality by 2030.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Positive</td>\n",
              "      <td>New solar panel technology could revolutionize...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Negative</td>\n",
              "      <td>Tech giants need to prioritize e-waste reduction.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Negative</td>\n",
              "      <td>Google's AI ethics committee faces criticism o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Positive</td>\n",
              "      <td>Tesla's electric vehicles are driving the shif...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Negative</td>\n",
              "      <td>Amazon's data centers consume enormous amounts...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbf62413-47f7-4b2a-b623-a1e8cd0dcdeb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cbf62413-47f7-4b2a-b623-a1e8cd0dcdeb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cbf62413-47f7-4b2a-b623-a1e8cd0dcdeb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8bcd43a6-72a0-4e62-9fee-3083411ae1d1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8bcd43a6-72a0-4e62-9fee-3083411ae1d1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8bcd43a6-72a0-4e62-9fee-3083411ae1d1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 181,\n  \"fields\": [\n    {\n      \"column\": \"Positive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Negative\",\n          \"Neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Apple announced plans to achieve carbon neutrality by 2030.\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 176,\n        \"samples\": [\n          \"Data centers are a significant contributor to global energy consumption.\",\n          \"Companies are under pressure to disclose their carbon footprint.\",\n          \"Carbon offsetting schemes are under scrutiny.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df = pd.read_csv('all_data.csv',delimiter=',',encoding='latin-1')\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "y1uJLvlUR5qG",
        "outputId": "c31f4ad6-29e0-464e-c441-4103b5e8baf1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Positive Apple announced plans to achieve carbon neutrality by 2030.\n",
              "176  Neutral  Public-private partnerships drive sustainable ...         \n",
              "177  Neutral  Debates over the ethical use of facial recogni...         \n",
              "178  Neutral  Tech's role in disaster response and recovery ...         \n",
              "179  Neutral  Sustainable fashion initiatives focus on consu...         \n",
              "180  Neutral  Tech companies collaborate on open-source sust...         "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bec03b6d-5356-4838-b070-92539784570a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Positive</th>\n",
              "      <th>Apple announced plans to achieve carbon neutrality by 2030.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Public-private partnerships drive sustainable ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Debates over the ethical use of facial recogni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Tech's role in disaster response and recovery ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Sustainable fashion initiatives focus on consu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>Neutral</td>\n",
              "      <td>Tech companies collaborate on open-source sust...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bec03b6d-5356-4838-b070-92539784570a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bec03b6d-5356-4838-b070-92539784570a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bec03b6d-5356-4838-b070-92539784570a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5680370c-3d94-4e7f-8eb2-bdb6a0efbe72\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5680370c-3d94-4e7f-8eb2-bdb6a0efbe72')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5680370c-3d94-4e7f-8eb2-bdb6a0efbe72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Positive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Apple announced plans to achieve carbon neutrality by 2030.\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Debates over the ethical use of facial recognition technology continue.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:49.811562Z",
          "iopub.status.busy": "2020-10-12T12:26:49.810579Z",
          "iopub.status.idle": "2020-10-12T12:26:49.813757Z",
          "shell.execute_reply": "2020-10-12T12:26:49.813059Z"
        },
        "papermill": {
          "duration": 0.056875,
          "end_time": "2020-10-12T12:26:49.813890",
          "exception": false,
          "start_time": "2020-10-12T12:26:49.757015",
          "status": "completed"
        },
        "tags": [],
        "id": "695YZh7oLsrN"
      },
      "outputs": [],
      "source": [
        "df = df.rename(columns={'Positive':'sentiment', 'Apple announced plans to achieve carbon neutrality by 2030.':'Message'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:49.916465Z",
          "iopub.status.busy": "2020-10-12T12:26:49.915342Z",
          "iopub.status.idle": "2020-10-12T12:26:49.919670Z",
          "shell.execute_reply": "2020-10-12T12:26:49.918929Z"
        },
        "papermill": {
          "duration": 0.057305,
          "end_time": "2020-10-12T12:26:49.919790",
          "exception": false,
          "start_time": "2020-10-12T12:26:49.862485",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfLafnJfLsrO",
        "outputId": "d896e205-143d-4a24-f3ec-bac460d36d71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(181, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:50.035463Z",
          "iopub.status.busy": "2020-10-12T12:26:50.026822Z",
          "iopub.status.idle": "2020-10-12T12:26:50.039928Z",
          "shell.execute_reply": "2020-10-12T12:26:50.039206Z"
        },
        "papermill": {
          "duration": 0.072332,
          "end_time": "2020-10-12T12:26:50.040050",
          "exception": false,
          "start_time": "2020-10-12T12:26:49.967718",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLRuTRgMLsrO",
        "outputId": "8900335d-5c6a-4a0d-a903-cfee6922a6df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1405"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.index = range(181)\n",
        "df['Message'].apply(lambda x: len(x.split(' '))).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.048672,
          "end_time": "2020-10-12T12:26:50.137220",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.088548",
          "status": "completed"
        },
        "tags": [],
        "id": "5W1zag5zLsrP"
      },
      "source": [
        "VISUALIZING THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:50.245448Z",
          "iopub.status.busy": "2020-10-12T12:26:50.242774Z",
          "iopub.status.idle": "2020-10-12T12:26:50.442947Z",
          "shell.execute_reply": "2020-10-12T12:26:50.442238Z"
        },
        "papermill": {
          "duration": 0.257927,
          "end_time": "2020-10-12T12:26:50.443105",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.185178",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "BQxzOeJdLsrP",
        "outputId": "ee88b1df-b649-44f4-fb06-64b8f24c4528"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cnt_pro = df['sentiment'].value_counts()\n",
        "plt.figure(figsize=(12,4))\n",
        "sns.barplot(cnt_pro, alpha=0.8)\n",
        "plt.ylabel('Number of Occurrences', fontsize=12)\n",
        "plt.xlabel('sentiment', fontsize=12)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:50.556412Z",
          "iopub.status.busy": "2020-10-12T12:26:50.555556Z",
          "iopub.status.idle": "2020-10-12T12:26:50.559713Z",
          "shell.execute_reply": "2020-10-12T12:26:50.559124Z"
        },
        "papermill": {
          "duration": 0.067554,
          "end_time": "2020-10-12T12:26:50.559841",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.492287",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQ1Ct4OLLsrQ",
        "outputId": "fc426c65-34c3-412c-c698-b990661a0604"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     sentiment                                            Message\n",
            "0            0  New solar panel technology could revolutionize...\n",
            "1            2  Tech giants need to prioritize e-waste reduction.\n",
            "2            2  Google's AI ethics committee faces criticism o...\n",
            "3            0  Tesla's electric vehicles are driving the shif...\n",
            "4            2  Amazon's data centers consume enormous amounts...\n",
            "..         ...                                                ...\n",
            "176          1  Public-private partnerships drive sustainable ...\n",
            "177          1  Debates over the ethical use of facial recogni...\n",
            "178          1  Tech's role in disaster response and recovery ...\n",
            "179          1  Sustainable fashion initiatives focus on consu...\n",
            "180          1  Tech companies collaborate on open-source sust...\n",
            "\n",
            "[181 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "#Convert sting to numeric\n",
        "sentiment  = {'Positive': 0,'Neutral': 1,'Negative':2}\n",
        "\n",
        "df.sentiment = [sentiment[item] for item in df.sentiment]\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:50.666951Z",
          "iopub.status.busy": "2020-10-12T12:26:50.666213Z",
          "iopub.status.idle": "2020-10-12T12:26:50.688240Z",
          "shell.execute_reply": "2020-10-12T12:26:50.687469Z"
        },
        "papermill": {
          "duration": 0.0786,
          "end_time": "2020-10-12T12:26:50.688365",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.609765",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0UfnOefLsrQ",
        "outputId": "aac91265-fe7a-4296-8738-c731eb00db5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IBM is investing in quantum computing for climate research.\n",
            "Message: 0\n"
          ]
        }
      ],
      "source": [
        "def print_message(index):\n",
        "    example = df[df.index == index][['Message', 'sentiment']].values[0]\n",
        "    if len(example) > 0:\n",
        "        print(example[0])\n",
        "        print('Message:', example[1])\n",
        "print_message(12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:50.797821Z",
          "iopub.status.busy": "2020-10-12T12:26:50.796782Z",
          "iopub.status.idle": "2020-10-12T12:26:50.800382Z",
          "shell.execute_reply": "2020-10-12T12:26:50.801184Z"
        },
        "papermill": {
          "duration": 0.062132,
          "end_time": "2020-10-12T12:26:50.801381",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.739249",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6PRD8kJLsrR",
        "outputId": "df0c72a6-03d2-4fa0-b3b5-28ad9477efe4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New solar panel technology could revolutionize renewable energy.\n",
            "Message: 0\n"
          ]
        }
      ],
      "source": [
        "print_message(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.05082,
          "end_time": "2020-10-12T12:26:50.904555",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.853735",
          "status": "completed"
        },
        "tags": [],
        "id": "WGHRWBSRLsrR"
      },
      "source": [
        "Text Preprocessing Below we define a function to convert text to lower-case and strip punctuation/symbols from words and so on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:51.014576Z",
          "iopub.status.busy": "2020-10-12T12:26:51.013707Z",
          "iopub.status.idle": "2020-10-12T12:26:52.465841Z",
          "shell.execute_reply": "2020-10-12T12:26:52.465013Z"
        },
        "papermill": {
          "duration": 1.51066,
          "end_time": "2020-10-12T12:26:52.465985",
          "exception": false,
          "start_time": "2020-10-12T12:26:50.955325",
          "status": "completed"
        },
        "tags": [],
        "id": "AsB7G7vZLsrS"
      },
      "outputs": [],
      "source": [
        "from bs4 import BeautifulSoup\n",
        "def cleanText(text):\n",
        "    text = BeautifulSoup(text, \"lxml\").text\n",
        "    text = re.sub(r'\\|\\|\\|', r' ', text)\n",
        "    text = re.sub(r'http\\S+', r'<URL>', text)\n",
        "    text = text.lower()\n",
        "    text = text.replace('x', '')\n",
        "    return text\n",
        "df['Message'] = df['Message'].apply(cleanText)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1Rvy2F9MYnJ",
        "outputId": "bbd8d110-7e15-42f4-cbf3-e4a852ebbc8e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:52.590236Z",
          "iopub.status.busy": "2020-10-12T12:26:52.589006Z",
          "iopub.status.idle": "2020-10-12T12:26:56.862817Z",
          "shell.execute_reply": "2020-10-12T12:26:56.861767Z"
        },
        "papermill": {
          "duration": 4.34599,
          "end_time": "2020-10-12T12:26:56.862994",
          "exception": false,
          "start_time": "2020-10-12T12:26:52.517004",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7AmA-XMNLsrS",
        "outputId": "ae0e7c71-fdb1-44b0-9407-ad1a7ab4f9d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 181 unique tokens.\n"
          ]
        }
      ],
      "source": [
        "df['Message'] = df['Message'].apply(cleanText)\n",
        "train, test = train_test_split(df, test_size=0.000001 , random_state=42)\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "def tokenize_text(text):\n",
        "    tokens = []\n",
        "    for sent in nltk.sent_tokenize(text):\n",
        "        for word in nltk.word_tokenize(sent):\n",
        "            #if len(word) < 0:\n",
        "            if len(word) <= 0:\n",
        "                continue\n",
        "            tokens.append(word.lower())\n",
        "    return tokens\n",
        "train_tagged = train.apply(\n",
        "    lambda r: TaggedDocument(words=tokenize_text(r['Message']), tags=[r.sentiment]), axis=1)\n",
        "test_tagged = test.apply(\n",
        "    lambda r: TaggedDocument(words=tokenize_text(r['Message']), tags=[r.sentiment]), axis=1)\n",
        "\n",
        "# The maximum number of words to be used. (most frequent)\n",
        "max_fatures = 500000\n",
        "\n",
        "# Max number of words in each complaint.\n",
        "MAX_SEQUENCE_LENGTH = 50\n",
        "\n",
        "#tokenizer = Tokenizer(num_words=max_fatures, split=' ')\n",
        "tokenizer = Tokenizer(num_words=max_fatures, split=' ', filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~', lower=True)\n",
        "tokenizer.fit_on_texts(df['Message'].values)\n",
        "X = tokenizer.texts_to_sequences(df['Message'].values)\n",
        "X = pad_sequences(X)\n",
        "print('Found %s unique tokens.' % len(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:57.004735Z",
          "iopub.status.busy": "2020-10-12T12:26:56.989207Z",
          "iopub.status.idle": "2020-10-12T12:26:57.168898Z",
          "shell.execute_reply": "2020-10-12T12:26:57.168096Z"
        },
        "papermill": {
          "duration": 0.254585,
          "end_time": "2020-10-12T12:26:57.169086",
          "exception": false,
          "start_time": "2020-10-12T12:26:56.914501",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLBqLta0LsrU",
        "outputId": "2ba25287-54bb-4327-8115-b402d88dfbc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data tensor: (181, 50)\n"
          ]
        }
      ],
      "source": [
        "X = tokenizer.texts_to_sequences(df['Message'].values)\n",
        "X = pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "print('Shape of data tensor:', X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:57.279508Z",
          "iopub.status.busy": "2020-10-12T12:26:57.278622Z",
          "iopub.status.idle": "2020-10-12T12:26:57.283858Z",
          "shell.execute_reply": "2020-10-12T12:26:57.283268Z"
        },
        "papermill": {
          "duration": 0.062472,
          "end_time": "2020-10-12T12:26:57.283990",
          "exception": false,
          "start_time": "2020-10-12T12:26:57.221518",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbrrlxtqLsrV",
        "outputId": "5746ffb7-3e73-4959-947a-18a7a089daac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([TaggedDocument(words=['google', \"'s\", 'commitment', 'to', 'zero', 'waste', 'initiatives', 'is', 'inspiring', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai', \"'s\", 'role', 'in', 'combating', 'climate', 'change', 'is', 'evolving', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'carbon', 'footprint', 'of', 'cloud', 'gaming', 'platforms', 'is', 'a', 'concern', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['the', 'impact', 'of', '5g', 'on', 'sustainability', 'is', 'debated', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['climate', 'change', 'poses', 'risks', 'to', 'tech', 'infrastructure', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['green', 'computing', 'initiatives', 'are', 'essential', 'for', 'a', 'sustainable', 'future', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'tech', 'sector', 'must', 'address', 'the', 'environmental', 'impact', 'of', 'hardware', 'production', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'invest', 'in', 'renewable', 'energy', 'projects', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['eectricity', 'production', 'without', 'causing', 'pollution', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'startups', 'attract', 'venture', 'capital', 'investments', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['public', 'perception', 'of', 'green', 'tech', 'is', 'influenced', 'by', 'media', 'coverage', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['ai-driven', 'precision', 'agriculture', 'can', 'improve', 'resource', 'efficiency', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'debate', 'on', 'ai', 'ethics', 'continues', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['companies', 'are', 'under', 'pressure', 'to', 'disclose', 'their', 'carbon', 'footprint', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['google', 'is', 'committed', 'to', 'reducing', 'its', 'carbon', 'footprint', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['apple', \"'s\", 'supply', 'chain', 'transparency', 'efforts', 'are', 'commendable', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'role', 'of', 'blockchain', 'in', 'healthcare', 'is', 'still', 'evolving', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['ai-driven', 'traffic', 'management', 'systems', 'reduce', 'emissions', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'collaborate', 'on', 'open-source', 'sustainability', 'projects', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'commit', 'to', 'using', 'recycled', 'materials', 'in', 'product', 'packaging', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'policies', 'vary', 'across', 'tech', 'hubs', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['carbon', 'offsetting', 'schemes', 'are', 'under', 'scrutiny', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['electric', 'vehicles', 'are', 'becoming', 'more', 'affordable', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['microsoft', 'aims', 'to', 'use', 'ai', 'for', 'sustainable', 'agriculture', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['sustainable', 'building', 'practices', 'are', 'gaining', 'traction', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['blockchain', 'can', 'enhance', 'traceability', 'in', 'food', 'supply', 'chains', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['artificial', 'intelligence', 'can', 'help', 'optimize', 'energy', 'consumption', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['blockchain', \"'s\", 'impact', 'on', 'financial', 'systems', 'is', 'uncertain', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'projects', 'face', 'funding', 'challenges', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['google', \"'s\", 'commitment', 'to', 'sustainability', 'influences', 'industry', 'trends', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'future', 'of', 'transportation', 'lies', 'in', 'electric', 'and', 'autonomous', 'vehicles', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'eplore', 'alternatives', 'to', 'rare', 'earth', 'metals', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['consumer', 'preferences', 'for', 'eco-friendly', 'products', 'are', 'growing', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', 'giants', 'should', 'prioritize', 'renewable', 'energy', 'investments', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai-powered', 'energy', 'management', 'systems', 'can', 'optimize', 'resource', 'usage', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'without', 'causing', 'carbon', 'emmission', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['concerns', 'over', 'electronic', 'waste', 'management', 'persist', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['smart', 'grid', 'technology', 'optimizes', 'energy', 'distribution', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['apple', \"'s\", 'commitment', 'to', 'reducing', 'carbon', 'emissions', 'sets', 'a', 'precedent', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ibm', 'is', 'investing', 'in', 'quantum', 'computing', 'for', 'climate', 'research', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'investments', 'are', 'on', 'the', 'rise', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai-driven', 'solutions', 'are', 'transforming', 'supply', 'chain', 'management', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['blockchain', 'technology', 'can', 'revolutionize', 'carbon', 'trading', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'invest', 'in', 'wildlife', 'conservation', 'efforts', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['challenges', 'in', 'sustainable', 'packaging', 'remain', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'collaborate', 'on', 'open-source', 'sustainability', 'initiatives', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'startups', 'are', 'focusing', 'on', 'sustainability', 'from', 'inception', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai', \"'s\", 'impact', 'on', 'job', 'displacement', 'is', 'a', 'topic', 'of', 'discussion', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'tech', 'industry', 'is', 'moving', 'towards', 'zero-waste', 'practices', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['public', 'opinion', 'on', 'tech', 'regulation', 'is', 'divided', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['concerns', 'over', 'tech', \"'s\", 'carbon', 'footprint', 'are', 'widespread', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['google', \"'s\", 'ai', 'ethics', 'committee', 'faces', 'criticism', 'over', 'lack', 'of', 'transparency', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['public-private', 'partnerships', 'drive', 'sustainable', 'infrastructure', 'projects', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['amazon', 'faces', 'criticism', 'over', 'ecessive', 'packaging', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['electric', 'vehicle', 'charging', 'infrastructure', 'needs', 'epansion', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['public-private', 'partnerships', 'drive', 'green', 'tech', 'initiatives', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['blockchain', 'technology', 'improves', 'transparency', 'in', 'carbon', 'credit', 'markets', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['data', 'privacy', 'concerns', 'affect', 'cloud', 'adoption', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['companies', 'need', 'to', 'invest', 'in', 'green', 'data', 'centers', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'intersection', 'of', 'tech', 'and', 'environmental', 'policy', 'is', 'comple', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'adoption', 'of', 'electric', 'vehicles', 'is', 'accelerating', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['solar-powered', 'chargers', 'reduce', 'reliance', 'on', 'traditional', 'power', 'sources', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['data', 'centers', 'powered', 'by', 'renewable', 'energy', 'sources', 'are', 'more', 'sustainable', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['cloud', 'computing', 'can', 'reduce', 'hardware', 'waste', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ethical', 'sourcing', 'of', 'minerals', 'is', 'a', 'priority', 'for', 'tech', 'companies', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'must', 'address', 'the', 'ethical', 'implications', 'of', 'facial', 'recognition', 'technology', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['solar', 'energy', 'solutions', 'are', 'becoming', 'more', 'accessible', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'are', 'under', 'scrutiny', 'for', 'labor', 'practices', 'in', 'supply', 'chains', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['environmental', 'concerns', 'shape', 'tech', 'investment', 'strategies', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['concerns', 'rise', 'over', 'the', 'environmental', 'impact', 'of', 'fast', 'fashion', 'e-commerce', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['smart', 'home', 'devices', 'contribute', 'to', 'energy', 'efficiency', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['climate', 'tech', 'startups', 'are', 'attracting', 'investment', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['blockchain', 'technology', 'improves', 'transparency', 'in', 'supply', 'chain', 'management', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['amazon', \"'s\", 'data', 'centers', 'consume', 'enormous', 'amounts', 'of', 'energy', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['the', 'tech', 'industry', 'needs', 'to', 'do', 'more', 'to', 'address', 'diversity', 'and', 'inclusion', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['smart', 'home', 'devices', 'improve', 'energy', 'efficiency', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['advancements', 'in', 'renewable', 'energy', 'technology', 'are', 'promising', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['companies', 'need', 'to', 'prioritize', 'sustainable', 'supply', 'chain', 'management', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['5g', 'technology', 'could', 'support', 'more', 'efficient', 'energy', 'usage', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['data', 'security', 'is', 'a', 'major', 'concern', 'for', 'iot', 'adoption', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['smart', 'city', 'initiatives', 'face', 'infrastructure', 'challenges', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['new', 'solar', 'panel', 'technology', 'could', 'revolutionize', 'renewable', 'energy', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['efforts', 'to', 'reduce', 'electronic', 'waste', 'are', 'gaining', 'momentum', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['companies', 'should', 'integrate', 'sustainability', 'into', 'product', 'design', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'adoption', 'varies', 'by', 'region', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['virtual', 'reality', 'can', 'be', 'used', 'for', 'eco-friendly', 'tourism', 'eperiences', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'innovations', 'are', 'reshaping', 'the', 'future', 'of', 'agriculture', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'environmental', 'impact', 'of', 'smartphone', 'production', 'is', 'substantial', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['the', 'environmental', 'impact', 'of', 'streaming', 'services', 'needs', 'evaluation', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'incentives', 'vary', 'by', 'country', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['smart', 'grid', 'technology', 'is', 'advancing', 'slowly', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'impact', 'of', 'iot', 'on', 'sustainability', 'is', 'still', 'being', 'debated', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['sustainable', 'practices', 'are', 'becoming', 'a', 'key', 'differentiator', 'in', 'tech', 'competition', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['smart', 'home', 'technologies', 'require', 'consumer', 'education', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['smartphone', 'manufacturers', 'face', 'challenges', 'in', 'sourcing', 'ethical', 'materials', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['companies', 'invest', 'in', 'green', 'bonds', 'to', 'fund', 'sustainable', 'initiatives', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['tech', 'innovations', 'support', 'wildlife', 'conservation', 'efforts', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['data', 'security', 'and', 'sustainability', 'are', 'top', 'priorities', 'for', 'it', 'leaders', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'initiatives', 'create', 'jobs', 'in', 'tech', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai-driven', 'predictive', 'maintenance', 'reduces', 'energy', 'consumption', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'debate', 'over', 'carbon', 'pricing', 'continues', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['challenges', 'in', 'wind', 'energy', 'production', 'persist', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'evolution', 'of', 'green', 'transportation', 'technology', 'is', 'rapid', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['sustainable', 'fintech', 'solutions', 'are', 'reshaping', 'the', 'banking', 'industry', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'carbon', 'footprint', 'of', 'cryptocurrency', 'mining', 'is', 'a', 'concern', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['ethical', 'considerations', 'in', 'ai', 'development', 'are', 'comple', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', 'innovations', 'in', 'waste', 'management', 'show', 'promise', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['companies', 'face', 'pressure', 'to', 'disclose', 'environmental', 'impact', 'data', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'storage', 'solutions', 'are', 'key', 'to', 'grid', 'stability', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'policies', 'can', 'drive', 'innovation', 'in', 'the', 'tech', 'sector', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai', 'algorithms', 'can', 'optimize', 'renewable', 'energy', 'distribution', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'influence', 'of', 'tech', 'on', 'climate', 'policies', 'is', 'significant', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['public', 'awareness', 'of', 'e-waste', 'issues', 'is', 'increasing', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'ethical', 'use', 'of', 'ai', 'in', 'healthcare', 'is', 'a', 'topic', 'of', 'debate', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', \"'s\", 'role', 'in', 'disaster', 'response', 'and', 'recovery', 'is', 'evolving', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'impact', 'of', 'iot', 'on', 'sustainability', 'is', 'still', 'being', 'debated', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'adoption', 'varies', 'by', 'region', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['companies', 'face', 'pressure', 'to', 'disclose', 'their', 'carbon', 'footprint', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['tech', 'innovations', 'are', 'essential', 'for', 'combating', 'climate', 'change', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['facebook', \"'s\", 'commitment', 'to', 'renewable', 'energy', 'is', 'commendable', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['challenges', 'in', 'renewable', 'energy', 'storage', 'persist', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tesla', \"'s\", 'electric', 'vehicles', 'are', 'driving', 'the', 'shift', 'towards', 'sustainable', 'transportation', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['debates', 'on', 'data', 'privacy', 'shape', 'ai', 'development', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['ethical', 'considerations', 'in', 'ai', 'development', 'are', 'crucial', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['blockchain', 'technology', 'offers', 'transparency', 'in', 'supply', 'chain', 'management', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'role', 'of', 'ai', 'in', 'climate', 'modeling', 'is', 'epanding', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai', 'can', 'be', 'leveraged', 'to', 'monitor', 'and', 'manage', 'water', 'resources', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['concerns', 'over', 'data', 'privacy', 'continue', 'to', 'affect', 'public', 'trust', 'in', 'tech', 'companies', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['blockchain', 'can', 'enhance', 'transparency', 'in', 'supply', 'chains', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'ethical', 'implications', 'of', 'facial', 'recognition', 'technology', 'are', 'significant', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['ai', 'algorithms', 'optimize', 'energy', 'efficiency', 'in', 'manufacturing', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'certificates', 'help', 'companies', 'meet', 'sustainability', 'goals', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['technological', 'advancements', 'in', 'water', 'purification', 'are', 'promising', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['public', 'trust', 'in', 'ai', 'governance', 'is', 'crucial', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['concerns', 'over', 'data', 'security', 'hinder', 'adoption', 'of', 'iot', 'devices', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['the', 'environmental', 'impact', 'of', 'cryptocurrency', 'mining', 'is', 'concerning', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'startups', 'are', 'gaining', 'traction', 'in', 'the', 'tech', 'industry', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['carbon', 'offsetting', 'initiatives', 'are', 'gaining', 'traction', 'in', 'tech', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['green', 'tech', 'startups', 'face', 'funding', 'challenges', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['data', 'privacy', 'laws', 'impact', 'tech', 'companies', 'globally', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['the', 'circular', 'economy', 'is', 'gaining', 'momentum', 'in', 'tech', 'manufacturing', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['sustainable', 'tech', 'solutions', 'are', 'gaining', 'traction', 'in', 'emerging', 'markets', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'environmental', 'impact', 'of', 'cloud', 'storage', 'is', 'debated', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['energy', 'efficiency', 'standards', 'are', 'under', 'review', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['energy-efficient', 'appliances', 'are', 'gaining', 'popularity', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['electric', 'vehicle', 'infrastructure', 'needs', 'government', 'support', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['smart', 'thermostats', 'contribute', 'to', 'energy', 'efficiency', 'in', 'homes', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'carbon', 'footprint', 'of', 'cloud', 'computing', 'is', 'a', 'concern', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['the', 'tech', 'industry', 'should', 'focus', 'on', 'reducing', 'electronic', 'waste', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['renewable', 'energy', 'policies', 'vary', 'across', 'tech', 'hubs', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['debates', 'over', 'the', 'ethical', 'use', 'of', 'facial', 'recognition', 'technology', 'continue', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['data', 'privacy', 'regulations', 'impact', 'tech', 'companies', 'globally', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['data', 'centers', 'powered', 'by', 'renewable', 'energy', 'are', 'the', 'future', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['solar-powered', 'gadgets', 'reduce', 'reliance', 'on', 'traditional', 'energy', 'sources', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['e-waste', 'recycling', 'rates', 'need', 'to', 'improve', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['ai', 'can', 'assist', 'in', 'wildlife', 'conservation', 'efforts', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'future', 'of', 'ai', 'in', 'climate', 'modeling', 'is', 'promising', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['government', 'regulations', 'play', 'a', 'key', 'role', 'in', 'sustainable', 'tech', 'adoption', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['solar-powered', 'gadgets', 'are', 'gaining', 'popularity', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['sustainable', 'agriculture', 'practices', 'vary', 'by', 'region', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['blockchain', \"'s\", 'potential', 'in', 'healthcare', 'data', 'management', 'is', 'eplored', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', 'giants', 'need', 'to', 'prioritize', 'e-waste', 'reduction', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['smart', 'cities', 'leverage', 'technology', 'for', 'sustainable', 'urban', 'development', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'role', 'of', 'tech', 'in', 'wildlife', 'conservation', 'is', 'epanding', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['public', 'opinion', 'on', 'green', 'tech', 'is', 'divided', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['corporate', 'sustainability', 'initiatives', 'face', 'skepticism', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['concerns', 'over', 'e-waste', 'recycling', 'persist', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['data', 'centers', 'should', 'invest', 'in', 'renewable', 'energy', 'sources', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['ai', 'can', 'optimize', 'energy', 'consumption', 'in', 'smart', 'cities', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'tech', 'industry', 'must', 'address', 'the', 'environmental', 'impact', 'of', 'data', 'centers', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['green', 'it', 'practices', 'are', 'essential', 'for', 'reducing', 'carbon', 'emissions', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['the', 'ethical', 'use', 'of', 'ai', 'in', 'healthcare', 'is', 'a', 'topic', 'of', 'debate', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['tech', 'companies', 'collaborate', 'on', 'open-source', 'sustainability', 'projects', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['facebook', \"'s\", 'renewable', 'energy', 'initiatives', 'are', 'a', 'step', 'in', 'the', 'right', 'direction', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['consumer', 'electronics', 'companies', 'are', 'urged', 'to', 'reduce', 'plastic', 'packaging', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['ai', 'enhances', 'precision', 'farming', 'techniques', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['apple', 'faces', 'accusations', 'of', 'planned', 'obsolescence', '.'], tags=[2]),\n",
              "       TaggedDocument(words=['blockchain', 'enhances', 'transparency', 'in', 'renewable', 'energy', 'transactions', '.'], tags=[0]),\n",
              "       TaggedDocument(words=['sustainable', 'fashion', 'initiatives', 'focus', 'on', 'consumer', 'education', '.'], tags=[1]),\n",
              "       TaggedDocument(words=['ai-powered', 'drones', 'aid', 'in', 'environmental', 'monitoring', '.'], tags=[0])],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#train_tagged.values[2173]\n",
        "train_tagged.values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.052552,
          "end_time": "2020-10-12T12:26:57.388708",
          "exception": false,
          "start_time": "2020-10-12T12:26:57.336156",
          "status": "completed"
        },
        "tags": [],
        "id": "_nXvHjt9LsrV"
      },
      "source": [
        "Model DM = 1\n",
        "\n",
        "This work use DM=1 (it preserve word order)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:26:57.500829Z",
          "iopub.status.busy": "2020-10-12T12:26:57.499982Z",
          "iopub.status.idle": "2020-10-12T12:27:02.496799Z",
          "shell.execute_reply": "2020-10-12T12:27:02.496132Z"
        },
        "papermill": {
          "duration": 5.055169,
          "end_time": "2020-10-12T12:27:02.496925",
          "exception": false,
          "start_time": "2020-10-12T12:26:57.441756",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhhqPXutLsrW",
        "outputId": "9f74a1a1-6807-4dfb-a735-cbd9cd2a0894"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 180/180 [00:00<00:00, 652527.85it/s]\n"
          ]
        }
      ],
      "source": [
        "# d2v_model = Doc2Vec(dm=1, dm_mean=1, size=20, window=8, min_count=1, workers=1, alpha=0.065, min_alpha=0.065)\n",
        "d2v_model = Doc2Vec(dm=1, dm_mean=1, vector_size=20, window=8, min_count=1, workers=1, alpha=0.065, min_alpha=0.065)\n",
        "d2v_model.build_vocab([x for x in tqdm(train_tagged.values)])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:02.612438Z",
          "iopub.status.busy": "2020-10-12T12:27:02.611409Z",
          "iopub.status.idle": "2020-10-12T12:27:16.819216Z",
          "shell.execute_reply": "2020-10-12T12:27:16.818556Z"
        },
        "papermill": {
          "duration": 14.268056,
          "end_time": "2020-10-12T12:27:16.819361",
          "exception": false,
          "start_time": "2020-10-12T12:27:02.551305",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2LT7s5PLsrW",
        "outputId": "3f10f4d1-c021-40c2-839c-56d1710094b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 180/180 [00:00<00:00, 213631.78it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 443060.28it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 772747.92it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 585251.72it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 693273.39it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 836073.89it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 817957.44it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 322638.77it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 699050.67it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 748982.86it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 699698.54it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 867787.03it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 796386.84it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 803164.60it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 765694.44it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 761063.23it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 736560.70it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 792208.52it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 699698.54it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 583442.60it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 136129.59it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 798915.05it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 830555.25it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 805736.09it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 483029.25it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 804877.10it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 879923.92it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 811800.77it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 833305.43it/s]\n",
            "100%|██████████| 180/180 [00:00<00:00, 861843.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 615 ms, sys: 37.6 ms, total: 652 ms\n",
            "Wall time: 828 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "for epoch in range(30):\n",
        "    d2v_model.train(utils.shuffle([x for x in tqdm(train_tagged.values)]), total_examples=len(train_tagged.values), epochs=1)\n",
        "    d2v_model.alpha -= 0.002\n",
        "    d2v_model.min_alpha = d2v_model.alpha"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:17.010712Z",
          "iopub.status.busy": "2020-10-12T12:27:17.009541Z",
          "iopub.status.idle": "2020-10-12T12:27:17.013494Z",
          "shell.execute_reply": "2020-10-12T12:27:17.014661Z"
        },
        "papermill": {
          "duration": 0.102522,
          "end_time": "2020-10-12T12:27:17.014909",
          "exception": false,
          "start_time": "2020-10-12T12:27:16.912387",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5V8tj2l2LsrW",
        "outputId": "faa4e75a-ecb9-43bc-a21f-afa7d2a8b27d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Doc2Vec<dm/m,d20,n5,w8,s0.001>\n"
          ]
        }
      ],
      "source": [
        "print(d2v_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:17.201967Z",
          "iopub.status.busy": "2020-10-12T12:27:17.200766Z",
          "iopub.status.idle": "2020-10-12T12:27:17.205291Z",
          "shell.execute_reply": "2020-10-12T12:27:17.204596Z"
        },
        "papermill": {
          "duration": 0.099901,
          "end_time": "2020-10-12T12:27:17.205421",
          "exception": false,
          "start_time": "2020-10-12T12:27:17.105520",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRVPkn4iLsrW",
        "outputId": "47d854f3-090e-4315-b5cd-7c1dee92f254",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.',\n",
              " 'in',\n",
              " 'tech',\n",
              " 'the',\n",
              " 'energy',\n",
              " 'are',\n",
              " 'of',\n",
              " 'is',\n",
              " 'renewable',\n",
              " 'to',\n",
              " 'companies',\n",
              " 'ai',\n",
              " 'on',\n",
              " 'data',\n",
              " 'sustainable',\n",
              " \"'s\",\n",
              " 'impact',\n",
              " 'carbon',\n",
              " 'technology',\n",
              " 'can',\n",
              " 'a',\n",
              " 'sustainability',\n",
              " 'for',\n",
              " 'environmental',\n",
              " 'initiatives',\n",
              " 'blockchain',\n",
              " 'over',\n",
              " 'green',\n",
              " 'management',\n",
              " 'ethical',\n",
              " 'smart',\n",
              " 'supply',\n",
              " 'climate',\n",
              " 'gaining',\n",
              " 'concerns',\n",
              " 'by',\n",
              " 'footprint',\n",
              " 'industry',\n",
              " 'challenges',\n",
              " 'adoption',\n",
              " 'face',\n",
              " 'transparency',\n",
              " 'role',\n",
              " 'efficiency',\n",
              " 'reduce',\n",
              " 'public',\n",
              " 'electric',\n",
              " 'waste',\n",
              " 'practices',\n",
              " 'and',\n",
              " 'centers',\n",
              " 'infrastructure',\n",
              " 'startups',\n",
              " 'optimize',\n",
              " 'invest',\n",
              " 'solutions',\n",
              " 'chain',\n",
              " 'privacy',\n",
              " 'cloud',\n",
              " 'more',\n",
              " 'efforts',\n",
              " 'future',\n",
              " 'projects',\n",
              " 'development',\n",
              " 'use',\n",
              " 'persist',\n",
              " 'packaging',\n",
              " 'policies',\n",
              " 'vary',\n",
              " 'innovations',\n",
              " 'vehicles',\n",
              " 'should',\n",
              " 'traction',\n",
              " 'consumer',\n",
              " 'healthcare',\n",
              " 'google',\n",
              " 'wildlife',\n",
              " 'debate',\n",
              " 'commitment',\n",
              " 'sources',\n",
              " 'concern',\n",
              " 'debated',\n",
              " 'computing',\n",
              " 'need',\n",
              " 'address',\n",
              " 'production',\n",
              " 'ai-driven',\n",
              " 'e-waste',\n",
              " 'agriculture',\n",
              " 'needs',\n",
              " 'iot',\n",
              " 'conservation',\n",
              " 'reducing',\n",
              " 'under',\n",
              " 'faces',\n",
              " 'facial',\n",
              " 'transportation',\n",
              " 'recognition',\n",
              " 'topic',\n",
              " 'solar-powered',\n",
              " 'region',\n",
              " 'grid',\n",
              " 'drive',\n",
              " 'security',\n",
              " 'home',\n",
              " 'devices',\n",
              " 'prioritize',\n",
              " 'support',\n",
              " 'promising',\n",
              " 'electronic',\n",
              " 'key',\n",
              " 'collaborate',\n",
              " 'pressure',\n",
              " 'must',\n",
              " 'improve',\n",
              " 'consumption',\n",
              " 'disclose',\n",
              " 'apple',\n",
              " 'investments',\n",
              " 'still',\n",
              " 'systems',\n",
              " 'storage',\n",
              " 'open-source',\n",
              " 'emissions',\n",
              " 'evolving',\n",
              " 'chains',\n",
              " 'change',\n",
              " 'becoming',\n",
              " 'essential',\n",
              " 'markets',\n",
              " 'combating',\n",
              " 'amazon',\n",
              " 'vehicle',\n",
              " 'precision',\n",
              " 'traditional',\n",
              " 'improves',\n",
              " 'sector',\n",
              " 'causing',\n",
              " 'without',\n",
              " 'reliance',\n",
              " '5g',\n",
              " 'public-private',\n",
              " 'comple',\n",
              " 'hardware',\n",
              " 'affect',\n",
              " 'partnerships',\n",
              " 'funding',\n",
              " 'resource',\n",
              " 'ethics',\n",
              " 'help',\n",
              " 'enhance',\n",
              " 'eco-friendly',\n",
              " 'giants',\n",
              " 'scrutiny',\n",
              " 'ai-powered',\n",
              " 'usage',\n",
              " 'offsetting',\n",
              " 'hubs',\n",
              " 'across',\n",
              " 'product',\n",
              " 'materials',\n",
              " 'distribution',\n",
              " 'rise',\n",
              " 'revolutionize',\n",
              " 'commendable',\n",
              " 'their',\n",
              " 'towards',\n",
              " 'opinion',\n",
              " 'continues',\n",
              " 'divided',\n",
              " 'powered',\n",
              " 'criticism',\n",
              " 'sourcing',\n",
              " 'cities',\n",
              " 'education',\n",
              " 'could',\n",
              " 'smartphone',\n",
              " 'facebook',\n",
              " 'recycling',\n",
              " 'gadgets',\n",
              " 'regulations',\n",
              " 'focus',\n",
              " 'being',\n",
              " 'government',\n",
              " 'popularity',\n",
              " 'debates',\n",
              " 'crucial',\n",
              " 'momentum',\n",
              " 'modeling',\n",
              " 'epanding',\n",
              " 'globally',\n",
              " 'water',\n",
              " 'continue',\n",
              " 'varies',\n",
              " 'trust',\n",
              " 'be',\n",
              " 'manufacturing',\n",
              " 'advancements',\n",
              " 'reshaping',\n",
              " 'shape',\n",
              " 'implications',\n",
              " 'considerations',\n",
              " 'investment',\n",
              " 'enhances',\n",
              " 'fashion',\n",
              " 'contribute',\n",
              " 'cryptocurrency',\n",
              " 'it',\n",
              " 'solar',\n",
              " 'mining',\n",
              " 'algorithms',\n",
              " 'significant',\n",
              " 'influenced',\n",
              " 'leveraged',\n",
              " 'coverage',\n",
              " 'banking',\n",
              " 'perception',\n",
              " 'resources',\n",
              " 'recycled',\n",
              " 'fintech',\n",
              " 'manage',\n",
              " 'certificates',\n",
              " 'using',\n",
              " 'monitor',\n",
              " 'capital',\n",
              " 'media',\n",
              " 'issues',\n",
              " 'commit',\n",
              " 'offers',\n",
              " 'increasing',\n",
              " 'disaster',\n",
              " 'response',\n",
              " 'influence',\n",
              " 'recovery',\n",
              " 'traffic',\n",
              " 'innovation',\n",
              " 'committed',\n",
              " 'tesla',\n",
              " 'driving',\n",
              " 'stability',\n",
              " 'promise',\n",
              " 'shift',\n",
              " 'show',\n",
              " 'awareness',\n",
              " 'its',\n",
              " 'purification',\n",
              " 'venture',\n",
              " 'electronics',\n",
              " 'reduction',\n",
              " 'platforms',\n",
              " 'gaming',\n",
              " 'leverage',\n",
              " 'urban',\n",
              " 'corporate',\n",
              " 'skepticism',\n",
              " 'step',\n",
              " 'right',\n",
              " 'direction',\n",
              " 'urged',\n",
              " 'potential',\n",
              " 'plastic',\n",
              " 'farming',\n",
              " 'inspiring',\n",
              " 'techniques',\n",
              " 'accusations',\n",
              " 'planned',\n",
              " 'zero',\n",
              " 'obsolescence',\n",
              " 'transactions',\n",
              " 'drones',\n",
              " 'eplored',\n",
              " 'play',\n",
              " 'attract',\n",
              " 'circular',\n",
              " 'meet',\n",
              " 'pollution',\n",
              " 'goals',\n",
              " 'technological',\n",
              " 'eectricity',\n",
              " 'evolution',\n",
              " 'governance',\n",
              " 'hinder',\n",
              " 'concerning',\n",
              " 'laws',\n",
              " 'economy',\n",
              " 'assist',\n",
              " 'emerging',\n",
              " 'standards',\n",
              " 'review',\n",
              " 'energy-efficient',\n",
              " 'appliances',\n",
              " 'thermostats',\n",
              " 'homes',\n",
              " 'risks',\n",
              " 'poses',\n",
              " 'rates',\n",
              " 'rapid',\n",
              " 'create',\n",
              " 'wind',\n",
              " 'remain',\n",
              " 'efficient',\n",
              " 'zero-waste',\n",
              " 'major',\n",
              " 'moving',\n",
              " 'discussion',\n",
              " 'city',\n",
              " 'displacement',\n",
              " 'job',\n",
              " 'inception',\n",
              " 'from',\n",
              " 'focusing',\n",
              " 'new',\n",
              " 'pricing',\n",
              " 'panel',\n",
              " 'trading',\n",
              " 'transforming',\n",
              " 'integrate',\n",
              " 'into',\n",
              " 'research',\n",
              " 'quantum',\n",
              " 'investing',\n",
              " 'ibm',\n",
              " 'precedent',\n",
              " 'sets',\n",
              " 'regulation',\n",
              " 'widespread',\n",
              " 'committee',\n",
              " 'aid',\n",
              " 'priority',\n",
              " 'power',\n",
              " 'chargers',\n",
              " 'accessible',\n",
              " 'accelerating',\n",
              " 'labor',\n",
              " 'policy',\n",
              " 'intersection',\n",
              " 'strategies',\n",
              " 'fast',\n",
              " 'e-commerce',\n",
              " 'credit',\n",
              " 'attracting',\n",
              " 'epansion',\n",
              " 'consume',\n",
              " 'charging',\n",
              " 'enormous',\n",
              " 'ecessive',\n",
              " 'amounts',\n",
              " 'do',\n",
              " 'diversity',\n",
              " 'inclusion',\n",
              " 'lack',\n",
              " 'design',\n",
              " 'optimizes',\n",
              " 'virtual',\n",
              " 'competition',\n",
              " 'financial',\n",
              " 'technologies',\n",
              " 'require',\n",
              " 'intelligence',\n",
              " 'artificial',\n",
              " 'manufacturers',\n",
              " 'food',\n",
              " 'traceability',\n",
              " 'bonds',\n",
              " 'fund',\n",
              " 'top',\n",
              " 'priorities',\n",
              " 'building',\n",
              " 'aims',\n",
              " 'microsoft',\n",
              " 'affordable',\n",
              " 'leaders',\n",
              " 'minerals',\n",
              " 'jobs',\n",
              " 'predictive',\n",
              " 'maintenance',\n",
              " 'schemes',\n",
              " 'reduces',\n",
              " 'uncertain',\n",
              " 'differentiator',\n",
              " 'reality',\n",
              " 'influences',\n",
              " 'used',\n",
              " 'tourism',\n",
              " 'eperiences',\n",
              " 'emmission',\n",
              " 'substantial',\n",
              " 'streaming',\n",
              " 'services',\n",
              " 'growing',\n",
              " 'products',\n",
              " 'evaluation',\n",
              " 'preferences',\n",
              " 'incentives',\n",
              " 'metals',\n",
              " 'earth',\n",
              " 'rare',\n",
              " 'alternatives',\n",
              " 'eplore',\n",
              " 'autonomous',\n",
              " 'country',\n",
              " 'lies',\n",
              " 'advancing',\n",
              " 'trends',\n",
              " 'slowly',\n",
              " 'monitoring']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "d2v_model.wv.index_to_key"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d2v_model.dv.vectors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufRq00mnNq7_",
        "outputId": "3bdf8d3d-00ce-4b59-e3ee-f9192eddd330"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.6512418 , -2.727615  , -1.7408246 ,  4.5851345 ,  0.2782448 ,\n",
              "        -0.44055864, -6.0780153 , -0.89151525, -5.489114  ,  1.5215937 ,\n",
              "         0.4477373 ,  1.4796101 ,  0.38598093,  0.32335195, -0.94549066,\n",
              "        -6.717244  ,  1.0260522 ,  4.867299  , -4.499227  , -4.162951  ],\n",
              "       [-2.2649493 ,  0.46333018, -3.6143796 ,  4.650686  ,  5.427221  ,\n",
              "        -2.771485  , -2.147992  , -3.027275  , -0.6554688 , -4.35361   ,\n",
              "         4.597008  ,  2.677166  , -3.3273218 , -2.8552802 , -0.30698785,\n",
              "         1.795212  ,  2.7042572 , -4.0318623 , -2.193751  ,  2.0892408 ],\n",
              "       [-2.5446095 , -6.029582  ,  2.2255228 , -1.5291418 ,  0.48612046,\n",
              "        -2.8429675 , -0.8229828 , -0.68818945, -3.322086  , -1.966732  ,\n",
              "         3.6353512 ,  0.527922  , -4.809867  , -5.983405  , -1.3346839 ,\n",
              "         5.8058667 ,  1.4945606 ,  4.412099  , -6.5590405 ,  0.98942935]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:17.394356Z",
          "iopub.status.busy": "2020-10-12T12:27:17.393493Z",
          "iopub.status.idle": "2020-10-12T12:27:17.397508Z",
          "shell.execute_reply": "2020-10-12T12:27:17.396883Z"
        },
        "papermill": {
          "duration": 0.101069,
          "end_time": "2020-10-12T12:27:17.397647",
          "exception": false,
          "start_time": "2020-10-12T12:27:17.296578",
          "status": "completed"
        },
        "tags": [],
        "id": "OXbauv8CLsrX"
      },
      "outputs": [],
      "source": [
        "# save the vectors in a new matrix\n",
        "embedding_matrix = np.zeros((len(d2v_model.wv.index_to_key)+ 1, 20))\n",
        "\n",
        "for i, vec in enumerate(d2v_model.dv.vectors):\n",
        "    while i in vec <= 100:\n",
        "    #print(i)\n",
        "    #print(model.docvecs)\n",
        "          embedding_matrix[i]=vec\n",
        "    #print(vec)\n",
        "    #print(vec[i])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.089396,
          "end_time": "2020-10-12T12:27:17.576795",
          "exception": false,
          "start_time": "2020-10-12T12:27:17.487399",
          "status": "completed"
        },
        "tags": [],
        "id": "r4Kcr3g3LsrX"
      },
      "source": [
        "# Measuring distance between two vectors (related to cosine similarity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:17.761221Z",
          "iopub.status.busy": "2020-10-12T12:27:17.760471Z",
          "iopub.status.idle": "2020-10-12T12:27:17.771803Z",
          "shell.execute_reply": "2020-10-12T12:27:17.772439Z"
        },
        "papermill": {
          "duration": 0.106359,
          "end_time": "2020-10-12T12:27:17.772620",
          "exception": false,
          "start_time": "2020-10-12T12:27:17.666261",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpLT1UA9LsrY",
        "outputId": "642e81e5-c3c3-40e5-9bee-9e72c2c9f260"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('appliances', 0.87519371509552),\n",
              " ('facebook', 0.8738319873809814),\n",
              " ('practices', 0.865057110786438),\n",
              " ('solutions', 0.8635282516479492),\n",
              " ('gadgets', 0.860425591468811),\n",
              " ('reshaping', 0.8567269444465637),\n",
              " ('financial', 0.8553938865661621),\n",
              " ('gaining', 0.8449129462242126),\n",
              " ('consumption', 0.8404158353805542),\n",
              " ('promising', 0.837714433670044)]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "d2v_model.wv.most_similar(positive=['energy-efficient'], topn=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:17.965398Z",
          "iopub.status.busy": "2020-10-12T12:27:17.964632Z",
          "iopub.status.idle": "2020-10-12T12:27:17.971853Z",
          "shell.execute_reply": "2020-10-12T12:27:17.971196Z"
        },
        "papermill": {
          "duration": 0.10422,
          "end_time": "2020-10-12T12:27:17.971999",
          "exception": false,
          "start_time": "2020-10-12T12:27:17.867779",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdS6H-QSLsrY",
        "outputId": "0a2cd048-81d9-4051-87ec-07c50400558a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('awareness', 0.8959972858428955),\n",
              " ('issues', 0.8846880197525024),\n",
              " ('smartphone', 0.8846154808998108),\n",
              " ('use', 0.882526695728302),\n",
              " ('implications', 0.8818418979644775),\n",
              " ('affect', 0.8747193217277527),\n",
              " ('concerning', 0.874009370803833),\n",
              " ('continue', 0.8736481070518494),\n",
              " ('epansion', 0.8708701133728027),\n",
              " ('charging', 0.8692067861557007)]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "d2v_model.wv.most_similar(positive=['investment'], topn=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:18.168986Z",
          "iopub.status.busy": "2020-10-12T12:27:18.168217Z",
          "iopub.status.idle": "2020-10-12T12:27:18.175299Z",
          "shell.execute_reply": "2020-10-12T12:27:18.174527Z"
        },
        "papermill": {
          "duration": 0.108079,
          "end_time": "2020-10-12T12:27:18.175427",
          "exception": false,
          "start_time": "2020-10-12T12:27:18.067348",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WakLt9NWLsrZ",
        "outputId": "10a7bd5a-ca50-4c00-951a-f53f198896bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('priority', 0.7781924605369568),\n",
              " ('cloud', 0.7655738592147827),\n",
              " ('moving', 0.7560240626335144),\n",
              " ('mining', 0.7499487996101379),\n",
              " ('cryptocurrency', 0.7331640720367432),\n",
              " ('its', 0.6996006369590759),\n",
              " ('pressure', 0.6954761147499084),\n",
              " ('evaluation', 0.672150194644928),\n",
              " ('environmental', 0.6626198291778564),\n",
              " ('footprint', 0.6621782183647156)]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "d2v_model.wv.most_similar(positive=['zero-waste'], topn=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.091436,
          "end_time": "2020-10-12T12:27:18.361351",
          "exception": false,
          "start_time": "2020-10-12T12:27:18.269915",
          "status": "completed"
        },
        "tags": [],
        "id": "JvDv5ZfvLsrZ"
      },
      "source": [
        "Plot Similarity word in Doc2vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:18.566535Z",
          "iopub.status.busy": "2020-10-12T12:27:18.563808Z",
          "iopub.status.idle": "2020-10-12T12:27:18.682597Z",
          "shell.execute_reply": "2020-10-12T12:27:18.681749Z"
        },
        "papermill": {
          "duration": 0.229098,
          "end_time": "2020-10-12T12:27:18.682727",
          "exception": false,
          "start_time": "2020-10-12T12:27:18.453629",
          "status": "completed"
        },
        "tags": [],
        "id": "OP4qmD6sLsrZ"
      },
      "outputs": [],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "def tsne_plot(model):\n",
        "    \"Creates and TSNE model and plots it\"\n",
        "    labels = []\n",
        "    tokens = []\n",
        "\n",
        "    for word in d2v_model.wv.key_to_index:\n",
        "        tokens.append(model[word])\n",
        "        labels.append(word)\n",
        "\n",
        "    tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=250, random_state=23)\n",
        "    new_values = tsne_model.fit_transform(tokens)\n",
        "\n",
        "    x = []\n",
        "    y = []\n",
        "    for value in new_values:\n",
        "        x.append(value[0])\n",
        "        y.append(value[1])\n",
        "\n",
        "    plt.figure(figsize=(16, 16))\n",
        "    for i in range(len(x)):\n",
        "        plt.scatter(x[i],y[i])\n",
        "        plt.annotate(labels[i],\n",
        "                     xy=(x[i], y[i]),\n",
        "                     xytext=(5, 2),\n",
        "                     textcoords='offset points',\n",
        "                     ha='right',\n",
        "                     va='bottom')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:27:18.906477Z",
          "iopub.status.busy": "2020-10-12T12:27:18.890965Z",
          "iopub.status.idle": "2020-10-12T12:41:07.304510Z",
          "shell.execute_reply": "2020-10-12T12:41:07.305281Z"
        },
        "papermill": {
          "duration": 828.530585,
          "end_time": "2020-10-12T12:41:07.305476",
          "exception": false,
          "start_time": "2020-10-12T12:27:18.774891",
          "status": "completed"
        },
        "tags": [],
        "id": "tMr4qoshLsra"
      },
      "outputs": [],
      "source": [
        "# tsne_plot(d2v_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.121792,
          "end_time": "2020-10-12T12:41:07.554437",
          "exception": false,
          "start_time": "2020-10-12T12:41:07.432645",
          "status": "completed"
        },
        "tags": [],
        "id": "uZKnm0F1Lsrb"
      },
      "source": [
        "# Create the LSTM Model\n",
        "\n",
        "The parameter used here inspired by [3].\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:41:07.812571Z",
          "iopub.status.busy": "2020-10-12T12:41:07.810122Z",
          "iopub.status.idle": "2020-10-12T12:41:09.303718Z",
          "shell.execute_reply": "2020-10-12T12:41:09.304591Z"
        },
        "papermill": {
          "duration": 1.627566,
          "end_time": "2020-10-12T12:41:09.304839",
          "exception": false,
          "start_time": "2020-10-12T12:41:07.677273",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MTOd7jZjLsrb",
        "outputId": "3a1c60be-003f-4a0e-a94a-36458c4ba3dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 50, 20)            8140      \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 50)                14200     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 3)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 22493 (87.86 KB)\n",
            "Trainable params: 22493 (87.86 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Embedding\n",
        "\n",
        "\n",
        "# init layer\n",
        "model = Sequential()\n",
        "\n",
        "# emmbed word vectors\n",
        "model.add(Embedding(len(d2v_model.wv.index_to_key)+1,20,input_length=X.shape[1],weights=[embedding_matrix],trainable=True))\n",
        "\n",
        "# learn the correlations\n",
        "def split_input(sequence):\n",
        "     return sequence[:-1], tf.reshape(sequence[1:], (-1,1))\n",
        "model.add(LSTM(50,return_sequences=False))\n",
        "model.add(Dense(3,activation=\"softmax\"))\n",
        "\n",
        "# output model skeleton\n",
        "model.summary()\n",
        "model.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=['acc'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:41:09.560246Z",
          "iopub.status.busy": "2020-10-12T12:41:09.559286Z",
          "iopub.status.idle": "2020-10-12T12:41:10.085335Z",
          "shell.execute_reply": "2020-10-12T12:41:10.084534Z"
        },
        "papermill": {
          "duration": 0.655789,
          "end_time": "2020-10-12T12:41:10.085483",
          "exception": false,
          "start_time": "2020-10-12T12:41:09.429694",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "gXL0-Rk3Lsrb",
        "outputId": "531664b1-0c8c-4dd8-e622-20ebf3859088"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "from keras.utils import plot_model\n",
        "plot_model(model, to_file='model.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:41:10.344650Z",
          "iopub.status.busy": "2020-10-12T12:41:10.343769Z",
          "iopub.status.idle": "2020-10-12T12:41:10.352385Z",
          "shell.execute_reply": "2020-10-12T12:41:10.351730Z"
        },
        "papermill": {
          "duration": 0.141525,
          "end_time": "2020-10-12T12:41:10.352514",
          "exception": false,
          "start_time": "2020-10-12T12:41:10.210989",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jcacpZmLsrd",
        "outputId": "b1bc8d25-e479-4a69-e22f-7f891631fcdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(153, 50) (153, 3)\n",
            "(28, 50) (28, 3)\n"
          ]
        }
      ],
      "source": [
        "Y = pd.get_dummies(df['sentiment']).values\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.15, random_state = 42)\n",
        "print(X_train.shape,Y_train.shape)\n",
        "print(X_test.shape,Y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = len(d2v_model.wv.index_to_key) + 1\n",
        "print(vocab_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdPQqx7SbPNR",
        "outputId": "1a323b60-daff-4f58-cc64-362c1d343298"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:43:48.986718Z",
          "iopub.status.busy": "2020-10-12T12:43:48.985884Z",
          "iopub.status.idle": "2020-10-12T12:43:49.764517Z",
          "shell.execute_reply": "2020-10-12T12:43:49.763845Z"
        },
        "papermill": {
          "duration": 0.950111,
          "end_time": "2020-10-12T12:43:49.764671",
          "exception": false,
          "start_time": "2020-10-12T12:43:48.814560",
          "status": "completed"
        },
        "tags": [],
        "id": "FaZaaDNcLsrf"
      },
      "outputs": [],
      "source": [
        "# predict probabilities for test set\n",
        "yhat_probs = model.predict(X_test, verbose=0)\n",
        "\n",
        "# predict crisp classes for test set\n",
        "yhat_classes = model.predict(X_test, verbose=0).argmax(axis=1)\n",
        "\n",
        "# reduce to 1d array\n",
        "#yhat_probs = yhat_probs[:, 0]\n",
        "#yhat_classes = yhat_classes[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:43:50.104353Z",
          "iopub.status.busy": "2020-10-12T12:43:50.103137Z",
          "iopub.status.idle": "2020-10-12T12:43:50.107313Z",
          "shell.execute_reply": "2020-10-12T12:43:50.107971Z"
        },
        "papermill": {
          "duration": 0.178295,
          "end_time": "2020-10-12T12:43:50.108187",
          "exception": false,
          "start_time": "2020-10-12T12:43:49.929892",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2Pbn2PLLsrf",
        "outputId": "6d8cf25b-299e-4c9c-b36e-2c5266cc3e2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 1, 2, 1, 2, 0, 2, 0, 0, 0, 1, 0, 1, 2, 0, 0, 1, 0, 0, 0, 1,\n",
              "       1, 0, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "import numpy as np\n",
        "rounded_labels=np.argmax(Y_test, axis=1)\n",
        "rounded_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:43:50.462368Z",
          "iopub.status.busy": "2020-10-12T12:43:50.461233Z",
          "iopub.status.idle": "2020-10-12T12:43:50.472789Z",
          "shell.execute_reply": "2020-10-12T12:43:50.473661Z"
        },
        "papermill": {
          "duration": 0.186503,
          "end_time": "2020-10-12T12:43:50.473880",
          "exception": false,
          "start_time": "2020-10-12T12:43:50.287377",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8rypXQQLsrg",
        "outputId": "baf3bc91-9494-4e75-9165-946223597664"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15,  0,  0],\n",
              "       [ 8,  0,  0],\n",
              "       [ 5,  0,  0]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(rounded_labels, yhat_classes)\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yhat_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umjyyuP7QIZX",
        "outputId": "dd1ed9ac-5993-4b08-f45f-298b3d4401a5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD8v_iVtQGIx",
        "outputId": "1528e2df-0bf9-4775-8e54-4a8d42a81013"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 1, 2, 1, 2, 0, 2, 0, 0, 0, 1, 0, 1, 2, 0, 0, 1, 0, 0, 0, 1,\n",
              "       1, 0, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:43:50.837934Z",
          "iopub.status.busy": "2020-10-12T12:43:50.837130Z",
          "iopub.status.idle": "2020-10-12T12:43:51.045528Z",
          "shell.execute_reply": "2020-10-12T12:43:51.046112Z"
        },
        "papermill": {
          "duration": 0.38367,
          "end_time": "2020-10-12T12:43:51.046279",
          "exception": false,
          "start_time": "2020-10-12T12:43:50.662609",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "nS-NO_c2Lsrg",
        "outputId": "10216b19-6142-4326-919f-77e0bb6ea3eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# The confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "lstm_val = confusion_matrix(rounded_labels, yhat_classes)\n",
        "f, ax = plt.subplots(figsize=(5,5))\n",
        "sns.heatmap(lstm_val, annot=True, linewidth=0.7, linecolor='cyan', fmt='g', ax=ax, cmap=\"BuPu\")\n",
        "plt.title('LSTM Classification Confusion Matrix')\n",
        "plt.xlabel('Y predict')\n",
        "plt.ylabel('Y test')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.170884,
          "end_time": "2020-10-12T12:43:51.385992",
          "exception": false,
          "start_time": "2020-10-12T12:43:51.215108",
          "status": "completed"
        },
        "tags": [],
        "id": "v1vwK9HNLsrh"
      },
      "source": [
        "# Validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.16783,
          "end_time": "2020-10-12T12:43:52.141630",
          "exception": false,
          "start_time": "2020-10-12T12:43:51.973800",
          "status": "completed"
        },
        "tags": [],
        "id": "JyQSDozDLsri"
      },
      "source": [
        "**Save Model**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:43:52.493269Z",
          "iopub.status.busy": "2020-10-12T12:43:52.492488Z",
          "iopub.status.idle": "2020-10-12T12:43:52.516965Z",
          "shell.execute_reply": "2020-10-12T12:43:52.516112Z"
        },
        "papermill": {
          "duration": 0.206645,
          "end_time": "2020-10-12T12:43:52.517130",
          "exception": false,
          "start_time": "2020-10-12T12:43:52.310485",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uw8wwQwsLsri",
        "outputId": "9b6e6040-a8a0-4153-fdf3-b98deeebaf44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "model.save('Mymodel.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.167501,
          "end_time": "2020-10-12T12:43:52.853834",
          "exception": false,
          "start_time": "2020-10-12T12:43:52.686333",
          "status": "completed"
        },
        "tags": [],
        "id": "T4-exOvlLsrk"
      },
      "source": [
        "**Test with new and different data set aside from the data to build the model.**\n",
        "\n",
        "note : sentiment  = {'positive': 0,'neutral': 1,'negative':2}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T12:43:54.775597Z",
          "iopub.status.busy": "2020-10-12T12:43:54.774486Z",
          "iopub.status.idle": "2020-10-12T12:43:54.818737Z",
          "shell.execute_reply": "2020-10-12T12:43:54.817952Z"
        },
        "papermill": {
          "duration": 0.222813,
          "end_time": "2020-10-12T12:43:54.818863",
          "exception": false,
          "start_time": "2020-10-12T12:43:54.596050",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4Do0IFNLsrm",
        "outputId": "c5388bca-3e0a-4406-b787-794f5bf6d976"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 25ms/step\n",
            "[[0.33333334 0.33333334 0.33333334]] 0\n"
          ]
        }
      ],
      "source": [
        "message = ['Data centers powered by renewable energy sources are more sustainable']\n",
        "seq = tokenizer.texts_to_sequences(message)\n",
        "\n",
        "padded = pad_sequences(seq, maxlen=X.shape[1], dtype='int32', value=0)\n",
        "\n",
        "pred = model.predict(padded)\n",
        "\n",
        "labels = ['0','1','2']\n",
        "print(pred, labels[np.argmax(pred)])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The rating of the 'IDEA' is :\",pred*100)"
      ],
      "metadata": {
        "id": "XOQrKyx2eAh9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efbd5c77-8e50-4ea0-98fe-a5aebfff6fb5"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rating of the 'IDEA' is : [[33.333336 33.333336 33.333336]]\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "papermill": {
      "duration": 1038.73911,
      "end_time": "2020-10-12T12:43:55.843833",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2020-10-12T12:26:37.104723",
      "version": "2.1.0"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}