tweet_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "tweet_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "fIpLJWV6qdb4"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSLNmfUrwLiC"
      },
      "source": [
        "**importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16XRYJ6ljYDC"
      },
      "source": [
        "#for handling emoticons\n",
        "!pip install emoji"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlT7cqZVuLtB"
      },
      "source": [
        "pip install num2words"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LS5tmcvrLNaa"
      },
      "source": [
        "pip install vaderSentiment"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptKjt4GM5lGb"
      },
      "source": [
        "pip install pyLDAvis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3pRVsL9wJ0l"
      },
      "source": [
        "import json\n",
        "import csv\n",
        "from collections import defaultdict\n",
        "from collections import Counter\n",
        "import numpy as np\n",
        "import re\n",
        "from num2words import num2words\n",
        "import nltk\n",
        "import pandas as pd\n",
        "\n",
        "#for topic modelling\n",
        "import gensim\n",
        "from gensim import models\n",
        "from gensim import corpora\n",
        "from gensim.models import CoherenceModel\n",
        "\n",
        "#for plotting\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim  \n",
        "%matplotlib inline\n",
        "\n",
        "#for handling emoticons\n",
        "import emoji\n",
        "\n",
        "#for sentiment analysis\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blmbpKRmwQhX"
      },
      "source": [
        "**Loading data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3A1FNx9kgef"
      },
      "source": [
        "with open('/content/drive/My Drive/Yuvi_RIP.json') as tweets:\n",
        "  tweet_data = json.load(tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fayaUVZh2eV"
      },
      "source": [
        "tweet_data_frame = pd.DataFrame.from_dict(tweet_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7hcbUS3iUus",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ef51b5a9-a663-4432-823e-c9e3ccf9d56f"
      },
      "source": [
        "tweet_data_frame.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9189, 27)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFBAgFv5a_KT"
      },
      "source": [
        "**Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PC54kQRBkEf_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "7fbe1c61-0607-4613-fb07-b75e60133610"
      },
      "source": [
        "tweet_data_frame.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['_id', 'created_at', 'id', 'id_str', 'full_text', 'truncated',\n",
              "       'display_text_range', 'entities', 'metadata', 'source',\n",
              "       'in_reply_to_status_id', 'in_reply_to_status_id_str',\n",
              "       'in_reply_to_user_id', 'in_reply_to_user_id_str',\n",
              "       'in_reply_to_screen_name', 'user', 'geo', 'coordinates', 'place',\n",
              "       'contributors', 'retweeted_status', 'is_quote_status', 'retweet_count',\n",
              "       'favorite_count', 'favorited', 'retweeted', 'lang'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a782jq1diBNf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "7c6e81e2-c60d-4021-ed86-4161bdcbc728"
      },
      "source": [
        "tweet_data_frame.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>_id</th>\n",
              "      <th>created_at</th>\n",
              "      <th>id</th>\n",
              "      <th>id_str</th>\n",
              "      <th>full_text</th>\n",
              "      <th>truncated</th>\n",
              "      <th>display_text_range</th>\n",
              "      <th>entities</th>\n",
              "      <th>metadata</th>\n",
              "      <th>source</th>\n",
              "      <th>in_reply_to_status_id</th>\n",
              "      <th>in_reply_to_status_id_str</th>\n",
              "      <th>in_reply_to_user_id</th>\n",
              "      <th>in_reply_to_user_id_str</th>\n",
              "      <th>in_reply_to_screen_name</th>\n",
              "      <th>user</th>\n",
              "      <th>geo</th>\n",
              "      <th>coordinates</th>\n",
              "      <th>place</th>\n",
              "      <th>contributors</th>\n",
              "      <th>retweeted_status</th>\n",
              "      <th>is_quote_status</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>favorite_count</th>\n",
              "      <th>favorited</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>lang</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570717'}</td>\n",
              "      <td>Wed Jun 10 18:19:05 +0000 2020</td>\n",
              "      <td>1270782555357212700</td>\n",
              "      <td>1270782555357212672</td>\n",
              "      <td>RT @PTTVOnlineNews: திமுக எம்எல்ஏ ஜெ.அன்பழகன் ...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 140]</td>\n",
              "      <td>{'hashtags': [{'text': 'JAnbazhagan', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'ta', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1146440916527243300, 'id_str': '1146440...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 06:32:16 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>ta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570718'}</td>\n",
              "      <td>Wed Jun 10 18:18:55 +0000 2020</td>\n",
              "      <td>1270782513577963500</td>\n",
              "      <td>1270782513577963525</td>\n",
              "      <td>RT @ShikharDhawan85: @WasimJaffer14 @virenders...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 107]</td>\n",
              "      <td>{'hashtags': [{'text': 'TeamIndia', 'indices':...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 2939301529, 'id_str': '2939301529', 'na...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 12:10:56 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570719'}</td>\n",
              "      <td>Wed Jun 10 18:18:43 +0000 2020</td>\n",
              "      <td>1270782461304164400</td>\n",
              "      <td>1270782461304164352</td>\n",
              "      <td>RT @TheCricketWire: #OnThisDay last year, Yuvr...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 140]</td>\n",
              "      <td>{'hashtags': [{'text': 'OnThisDay', 'indices':...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1256605147355988000, 'id_str': '1256605...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 10:26:06 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb57071a'}</td>\n",
              "      <td>Wed Jun 10 18:18:41 +0000 2020</td>\n",
              "      <td>1270782454454837200</td>\n",
              "      <td>1270782454454837248</td>\n",
              "      <td>RT @Sra1karne: We are at 5th place in Trending...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 140]</td>\n",
              "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 464766558, 'id_str': '464766558', 'name...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 06:25:26 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb57071b'}</td>\n",
              "      <td>Wed Jun 10 18:18:34 +0000 2020</td>\n",
              "      <td>1270782423538647000</td>\n",
              "      <td>1270782423538647040</td>\n",
              "      <td>RT @fadnavis_amruta: He was out on streets of ...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 144]</td>\n",
              "      <td>{'hashtags': [{'text': 'Dharavi', 'indices': [...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1850478770, 'id_str': '1850478770', 'na...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 08:00:34 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>269</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    _id  ... lang\n",
              "0  {'$oid': '5ee1244dc51e2d8beb570717'}  ...   ta\n",
              "1  {'$oid': '5ee1244dc51e2d8beb570718'}  ...   en\n",
              "2  {'$oid': '5ee1244dc51e2d8beb570719'}  ...   en\n",
              "3  {'$oid': '5ee1244dc51e2d8beb57071a'}  ...   en\n",
              "4  {'$oid': '5ee1244dc51e2d8beb57071b'}  ...   en\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2HAtI_kbZgi"
      },
      "source": [
        "There were a total 9189 tweets and each tweet had 27 attributes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjQXkUJdmhfl"
      },
      "source": [
        "**Separating the tweets into different classes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYaWPzV-bruF"
      },
      "source": [
        "The tweets were separated based on the hashtags.\n",
        "\n",
        "* Class A ⇒ #MissYouYuvi only\n",
        "* Class B ⇒ #RestInPeace only\n",
        "* Class C ⇒ both #MissYouYuvi and #RestInPeace"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_S5Ziq9g2p2E"
      },
      "source": [
        "df_A = pd.DataFrame()                       #dataframe that stores tweets of class A\n",
        "df_B = pd.DataFrame()                       #dataframe that stores tweets of class B\n",
        "df_C = pd.DataFrame()                       #dataframe that stores tweets of class C\n",
        "\n",
        "\n",
        "for i in range(len(tweet_data_frame)):\n",
        "  text = tweet_data_frame.loc[i,'full_text']\n",
        "\n",
        "  if(\"#MissYouYuvi\" in text and \"#RestInPeace\" not in text):\n",
        "    df_A = df_A.append(tweet_data_frame.loc[[i]])\n",
        "  elif(\"#RestInPeace\" in text and \"#MissYouYuvi\" not in text):\n",
        "    df_B = df_B.append(tweet_data_frame.loc[[i]])\n",
        "  elif(\"#RestInPeace\" in text and \"#MissYouYuvi\" in text):\n",
        "    df_C = df_C.append(tweet_data_frame.loc[[i]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIpLJWV6qdb4"
      },
      "source": [
        "####**Analysing the data fetched**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpfNQnVzoZ4P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "800338ef-6fc7-4daf-f760-676412fde43f"
      },
      "source": [
        "print(\"Size of class A = \",len(df_A.index))\n",
        "print(\"Size of class B = \",len(df_B.index))\n",
        "print(\"Size of class C = \",len(df_C.index))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of class A =  5665\n",
            "Size of class B =  416\n",
            "Size of class C =  269\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nENRjMpItyQk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46314aab-7371-4531-ed61-1c6c8abdf7b4"
      },
      "source": [
        "class_name = ['A','B','C']\n",
        "count = [len(df_A.index),len(df_B.index),len(df_C.index)]\n",
        "\n",
        "index = np.arange(len(class_name))\n",
        "\n",
        "sns.barplot(x=class_name, y=count, linewidth=2.5, errcolor=\".2\", edgecolor=\".2\")\n",
        "plt.xlabel('Class', fontsize=15)\n",
        "plt.ylabel('Number of data', fontsize=15)\n",
        "plt.xticks(index, class_name, fontsize=15)\n",
        "plt.title('Class distribution')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dcvnr8v78XD+m4lwn5/J5kg6o532YmVn9VyRcC7w/InYF3gZMkrQ38J/AuRGxI7ACOCYffwywIpefm49D0s7AUcAuwCTgQkkb1vVOzMzaXF0TSCSr8+7g/Ang/cAvcvmlwKF5e3LeJ9dPkKRcfkVErI2IBcB8YM863IKZmWV1XxNd0oaS7gWWAbOBR4GVFfNpPQlsk7e3ARYC5PpVpDffXynv5jeV15oqaY6kOR0dnmnFzKyW1ptAcj/EqZJ2rcUFI+LliHgbMJr01NBvS+VGxPSIGB8R40eOHNlflzEza0vrTSARsRY4FRhWywtHxErSuyX7AMMkdY4IGw0sytuLgG0Bcv1Q4OnK8m5+Y2ZmdVC0CetOYPe+XkzSSEnD8vbGpPdJHiYlkg/nw6YA1+btmXmfXH9TREQuPyo/HY0lvaPixa7MzOqo6HsgXwIul7QOuI70JnpUHhARawqcZyvg0jxiagPgqoj4taSHgCsknUlai/3ifPzFwE8kzSetP3JUvtaDkq4CHgJeAo6LiJcL3ouZmdVA0QRyZ/4+HzivyjHrHUYbEfcDu3VT/hjdjKKKiBeAI6qc6+vA19d3TTMz6x9FE8hn6PLEYWZm7a3okraX9HMcZmbWYgrPhQWvvAG+B2kE1IyIWCJpR2BpRFRbL8TMzAagomuibwLMII2EWpd/dz2wBPgG8Ffgi/0Uo5mZNaHerIn+DmACsClpDZBO15HmozIzszZStAnrMOCEiLi5m0kLnwC2r21YZmbW7Io+gWxMegO8O5sCfgfDzKzNFE0gdwGfrFL3YeD22oRjZmatomgT1leA2ZL+C/g56Z2QgyR9npRA3tNP8ZmZWZMquib6raQO9NcB3yd1on8V2AHYLyLu6rcIzcysKfVmTfTbgHfnSRA3J63hUWT+KzMzG4DKLCj1AuldkOdrHIuZmbWQwglE0kGSbiclkCXAC5Jul3Rwv0VnZmZNq1ACkfRZYBawGjiBNEPuCXl/Zq43M7M2UrQP5BTgooj41y7lP5T0Q9KKhRfVNDIzM2tqRZuwtgCuqVL3S2B4bcIxM7NWUTSB3Ay8t0rde4E/1CYcMzNrFVWbsPLU7Z3OB34saQvgV8AyYEvgQ8CBwLH9GaSZmTWfnvpA/syrVyEU8Nn8CV49I+/1FFjS1szMBo6eEsj76haFmZm1nKoJJCJ+X89AzMystfRqSVsASYOAIV3LPa2JmVl7Kfoi4VBJF0paTHoT/dluPmZm1kaKPoFcQhqu+yNgPvBifwVkZmatoWgCmQB8NiJ+1p/BmJlZ6yj6IuFfAfdxmJnZK4omkC8BX5a0XX8GY2ZmraNQE1ZEXCdpP2C+pMeBld0cs2eNYzMzsyZWdBTWWcDngHuAu4AHu/kUOc+2km6W9JCkByWdkMuHS5ot6ZH8vXkul6TzJc2XdL+k3SvONSUf/4ikKb25aTMz67uinejHAqdGxDf7eL2XgGkRMVfSpsDdkmYDnwJujIhvSToJOAk4kTTP1rj82Qv4AbCXpOHAacB40rQqd0uaGREr+hifmZkVVLQPZA1wd18vFhGLI2Ju3n4WeBjYBpgMXJoPuxQ4NG9PBi6L5A5gmKStgAOA2RGxPCeN2cCkvsZnZmbFFU0g5wFTJWm9RxYkaQywG3AnMCoiFueqJcCovL0NsLDiZ0/msmrlXa8xVdIcSXM6OjpqFbqZmVG8CWsEqQlpnqRbeG0nekTEiUUvKmkT0kJUn4uIZyrzUkSEpKj6416IiOnAdIDx48fX5JxmZpYUTSAfJvVfDAb276Y+SH0W6yVpMCl5/DQirs7FSyVtFRGLcxPVsly+CNi24uejc9kiYN8u5bcUuhMzM6uJQk1YETF2PZ8dipwnN4FdDDwcEedUVM0EOkdSTQGurSj/ZB6NtTewKjd1/Q6YKGnzPGJrYi4zM7M66fVsvH30TuATwAOS7s1lpwDfAq6SdAzwBHBkrrsOOIg0/9Ya4NMAEbFc0tdIQ4oBzoiI5fW5BTMzg4IJRNK/ru+YiLiwwDF/5NUrGVaa0M3xARxX5VwzgBnru6aZmfWPok8g3++hrrNzer0JxMzMBo6ifSAbdP0Aw4GjgfuAnfszSDMzaz6l+0AiYiVwpaShwEW8elSUmZkNcEVfJOzJAtKUImZm1kb6lEDyOxvTSEnEzMzaSNFRWB38o7O80xBgU9Ia6YfVOC4zM2tyRftALuC1CeQF0hxU10fE0zWNyszMml7RBaVO7+c4zMysxdSiE93MzNpQ1ScQSTf14jwREa95k9zMzAaunpqwivRrbAW8g9f2j5iZ2QBXNYFExBHV6iRtR5q+/QPAU8C5tQ/NzMyaWa/eRJe0I3Ay8HHSmh0nAxdFxPP9EJuZmTWxou+B7AKcChxBWkr2BGBGRLzYj7GZmVkT63EUlqQ9JF0N3A/sDhwLjIuIHzp5mJm1t55GYf2WtNLfA8BREfHzukVlZmZNr6cmrAPy92jgAkkX9HSiiNiyZlGZmVnT6ymBfLVuUZiZWcvpaRivE4iZmVXlqUzMzKwUJxAzMyvFCcTMzEpxAjEzs1KcQMzMrBQnEDMzK8UJxMzMSnECMTOzUuqaQCTNkLRM0p8ryoZLmi3pkfy9eS6XpPMlzZd0v6TdK34zJR//iKQp9bwHMzNL6v0EcgkwqUvZScCNETEOuDHvAxwIjMufqcAPICUc4DRgL2BP4LTOpGNmZvVT1wQSEX8AlncpngxcmrcvBQ6tKL8skjuAYZK2Ik3yODsilkfECmA2r01KZmbWz5qhD2RURCzO20uAUXl7G9LiVZ2ezGXVyl9D0lRJcyTN6ejoqG3UZmZtrhkSyCsiIoCo4fmmR8T4iBg/cuTIWp3WzMxojgSyNDdNkb+X5fJFwLYVx43OZdXKzcysjpohgcwEOkdSTQGurSj/ZB6NtTewKjd1/Q6YKGnz3Hk+MZeZmVkd9bSgVM1J+hmwLzBC0pOk0VTfAq6SdAzwBHBkPvw64CBgPrAG+DRARCyX9DXgrnzcGRHRtWPezMz6WV0TSEQcXaVqQjfHBnBclfPMAGbUMDQzM+ulZmjCMjOzFuQEYmZmpTiBmJlZKU4gZmZWihOImZmV4gRiZmalOIGYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZleIEYmZmpTiBmJlZKXVdD8Ssv5199tnMmzev0WG0hZ122olp06Y1OgxrICcQG1DmzZvH3LlzGx2GWVtwArEB6fWDgrFDo9FhDEgLVok1L6nRYVgTcAKxAWns0OBr71zX6DAGpK/cNpgHn3YCMXeim5lZSX4CMbOm4UEQ9VOLQRBOIGbWNDwIorU4gZhZ8xkMGu5+lv4QywNq1D3oBGJmTUfDxZBJQxodxoD04vUvEktrM0LRnehmZlaKE4iZmZXiBGJmZqU4gZiZWSktnUAkTZI0T9J8SSc1Oh4zs3bSsglE0obABcCBwM7A0ZJ2bmxUZmbto5WH8e4JzI+IxwAkXQFMBh6qydlXd8A9v6zJqawbqzv69fQLVomv3Da4X6/Rrhas6v/3M2J58OL1L/b7ddpRLK/dJKOKaM0ZSyV9GJgUEcfm/U8Ae0XE8RXHTAWm5t2dgIE8R8II4KlGB2Gl+c+vdQ30P7vtI2JkdxWt/ASyXhExHZje6DjqQdKciBjf6DisHP/5ta52/rNr2T4QYBGwbcX+6FxmZmZ10MoJ5C5gnKSxkoYARwEzGxyTmVnbaNkmrIh4SdLxwO+ADYEZEfFgg8NqpLZoqhvA/OfXutr2z65lO9HNzKyxWrkJy8zMGsgJxMzMSnECGQAkLZAUknZsdCxWjKTT859Z52eNpAfyu0vWIiQdLukmSSslrZX035LOkbR1o2OrByeQFidpH2BM3j26gaFY760C9smfQ4AbgYskfbShUVkhks4GrgIeAz4BTATOBSaQplka8NyJ3uIknQ98BvgzsFlEeD6wFiDpdOD4iBjRpfxPwOMRcWRDArNCJB1Cem3gmIiY0aVuQ2BiRPy2IcHVkZ9AWlj+F/VI0r/IM4A3S9q1sVFZHz0LeBKv5vd5YG7X5AEQES+3Q/IAJ5BW9z5gFHAF8AtgHW7GaimSBuXPZpI+DrwXuKbRcVl1kgYD7wCub3QsjeYE0tqOBlYC10fEcuAG4ChJ/T9dqtXCFqSkv47UH/IT4IKIuKyhUdn6bAG8DvhrowNpNCeQFpWnbzkMuCYiOue9vgLYntQpa81vFfD2/HkXcAIwRdJpDY3Kimr7DuSWncrEOBAYBlwnaVguuwVYS3oyub1BcVlxL0XEnIr92yQNAr4p6Xv5qdKaz9Ok/862a3QgjeYnkNbV2dfxc2BF/iwkPVofkTvYrfU8DAwB/rnRgVj3ImIdcBtwQKNjaTQnkBYk6Q2k9wZ+RupIr/x8gdSx/v6GBWh98Zb8vbChUdj6fBcYL2lK1wpJG0ia1ICY6s5NWK1pMvB64LyIuLOyQtJtwKmkJ5TZDYjNihskae+8PQTYA/gycG1ELGlcWLY+ETFL0jnAxZLeCVwLrAbeBPwL8DhtMErLLxK2IEmzgJ0i4o1V6i8EPgqMioi1dQ3OCskvElZ2lq8DngCuBs6MiGcbEZf1jqTDgeOB3YGNSYljJnBWO/xPgBOImZmV4j4QMzMrxQnEzMxKcQIxM7NSnEDMzKwUJxAzMyvFCcTMzEpxAjGrgZ6WNpU0Ji9b+4FGx2lWS04gZn3kpU2tXXkqE7M+yEubfoHXLm36e0nTScnEbEDyE4hZ35Ra2lTSJyX9UdJySSsk3SxpfJdjdpF0fT7mOUkPSzquov5dkm6V9Ez+3CvpiJrfoVkVfgIxK6liadOzS/x8DHAZ8ChpIsWjgVsl7RIRj+VjZpGmd/84af2JnYDN8rU3A35NmsTvDEDA/yKtEWNWF04gZuWVXto0Is7o3Ja0AWnm5D1JyeIMSSOAscDkiHggH3pjxSneCAwFjq+YePGGXt+BWR+4Ccus73o9I6mkN0u6RtJS4GXSbLw7kRIDwHLSmiA/lPQRSVt2OcWjpOnDL5c0uWJVSrO6cQIxK6/U0qaSNiU9LWxL6oB/N2ld9PuAjQAi4u+kDvglwAxgSe7v2C3XrwD2BwaTRoB1SPqNpB1qcF9mhTiBmJXUh6VN9wFGAx+PiJ9GxB/z2uhDu5z/LxFxOKlfYz9ScvlNbvIiIu6IiEm5/jDS08vlfbkns95wAjHrmzJLm26cv9dWHPsOUsf6a0TEuoi4CTgH2IouHeUR8XxEzCI9qexc5ibMynAnulkfFFza9PNdfnZHPuZHkr5Neho5HVjUeYCktwJnAVeSXlDcHDgRuC8ilks6GPgM8CtSJ/42wGeBm/rlRs264QRi1kcRMU3S7aSlTS+ny9Km5H6NiuOX5vc1ziIlnEdIyeZLFYctAZaS1rffGlgJ3ExKIgDzSZ333wC2BDpIw3pPqfkNmlXhJW3NzKwU94GYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZleIEYmZmpfwPkeDNS27C/OUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDB_9r6e5sgr"
      },
      "source": [
        "**Number of users in each class**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wY83MRtEcHqf"
      },
      "source": [
        "Information about the number of unique users was taken from the ‘user’ attribute of the tweet. This attribute consists of a dictionary, where ‘id_str’ gives the twitter user’s\n",
        "unique id. Following are the results for each class:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZqPQSo28h8T"
      },
      "source": [
        "*Class A*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdRwm6W95Hvd"
      },
      "source": [
        "users_A = []\n",
        "\n",
        "for i in df_A.index:\n",
        "  users_A.append(df_A.loc[i,'user']['id_str'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Bpq74px7aNS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ba9c90a-a873-4080-d725-b4d119d3237f"
      },
      "source": [
        "print(\"Tweets for class A: \", len(users_A))\n",
        "print(\"Number of users tweeting for class A :\", len(set(users_A)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tweets for class A:  5665\n",
            "Number of users tweeting for class A : 1617\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOXizDyk8_SG"
      },
      "source": [
        "*Class B*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Cw0WIG18_Er"
      },
      "source": [
        "users_B = []\n",
        "\n",
        "for i in df_B.index:\n",
        "  users_B.append(df_B.loc[i,'user']['id_str'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enAmAZ6r9GXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6996034-ca84-452a-a660-43d633700eee"
      },
      "source": [
        "print(\"Tweets for class B: \", len(users_B))\n",
        "print(\"Number of users tweeting for class B :\", len(set(users_B)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tweets for class B:  416\n",
            "Number of users tweeting for class B : 381\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcT8zMhK9KkW"
      },
      "source": [
        "*Class C*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfhhTq5K9Ond"
      },
      "source": [
        "users_C = []\n",
        "\n",
        "for i in df_C.index:\n",
        "  users_C.append(df_C.loc[i,'user']['id_str'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vGrrYkW9KKo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df3b05fa-721e-4b62-8c17-a77b917df613"
      },
      "source": [
        "print(\"Tweets for class C: \", len(users_C))\n",
        "print(\"Number of users tweeting for class C :\", len(set(users_C)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tweets for class C:  269\n",
            "Number of users tweeting for class C : 197\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aIHE42y9WL-"
      },
      "source": [
        "**Number of verified users in each class**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1d2TIyxbcQXY"
      },
      "source": [
        "Information about the number of verified users was taken from the ‘user’ attribute of the tweet. This attribute consists of a dictionary, where ‘verified’ gives the twitter user’s verification status. Following are the results for each class:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwoQyfwj9hpf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9558b520-7612-4340-9389-068b110e5d7d"
      },
      "source": [
        "count_A = 0\n",
        "\n",
        "for i in df_A.index:\n",
        "  if(df_A.loc[i,'user']['verified'] == True):\n",
        "    count_A +=1\n",
        "\n",
        "print(\"Number of verified users in class A = \", count_A)\n",
        "print(\"Number of non-verified users in class A = \",len(set(users_A))-count_A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of verified users in class A =  34\n",
            "Number of non-verified users in class A =  1583\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-rYxM7X9zq0"
      },
      "source": [
        "*Class B*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaAgYPIf9yQq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39806134-5cdd-47c6-9c56-9f886896fe1e"
      },
      "source": [
        "count_B = 0\n",
        "\n",
        "for i in df_B.index:\n",
        "  if(df_B.loc[i,'user']['verified'] == True):\n",
        "    count_B +=1\n",
        "\n",
        "print(\"Number of verified users in class B = \", count_B)\n",
        "print(\"Number of non-verified users in class B = \",len(set(users_B))-count_B)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of verified users in class B =  6\n",
            "Number of non-verified users in class B =  375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5d8KKdoW9-ep"
      },
      "source": [
        "*Class C*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_hFYbZA9zBr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "359e6832-8502-4f16-ad77-38df98ff55cc"
      },
      "source": [
        "count_C = 0\n",
        "\n",
        "for i in df_C.index:\n",
        "  if(df_C.loc[i,'user']['verified'] == True):\n",
        "    count_C +=1\n",
        "\n",
        "print(\"Number of verified users in class C = \", count_C)\n",
        "print(\"Number of non-verified users in class C = \",len(set(users_C))-count_C)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of verified users in class C =  0\n",
            "Number of non-verified users in class C =  197\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSKCc74U_Q2y"
      },
      "source": [
        "**Number of hashtags vs timeline**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lO-IfBqTcZX5"
      },
      "source": [
        "Since all the tweets belonged to the same date, so the time of the day was fetched and plotted against the number of hashtags.\n",
        "\n",
        "_One can also plot a time-series of the hash-tag usage with x-axis as the date and y-axis as count of hashtag use on that day for the 3 classes_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yxk56WsGXMGt"
      },
      "source": [
        "time_A = defaultdict(lambda:0)\n",
        "\n",
        "for i in df_A.index:\n",
        "  date_time = df_A.loc[i,'created_at']\n",
        "  time = date_time.split('Wed Jun 10')[1].strip()\n",
        "  hour = time.split(\":\")[0]\n",
        "  time_A[hour] +=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "br-LUUqkZa_E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db61289c-33e3-407e-8177-466a9af154bf"
      },
      "source": [
        "plt.plot(list(time_A.keys()), list(time_A.values()))\n",
        "plt.xlabel('Time of the day on Wed Jun 10 ') \n",
        "plt.ylabel('Number of hashtags')\n",
        "plt.title(\"Usage of hash-tags of class A\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_adyfQn_VpF"
      },
      "source": [
        "time_B = defaultdict(lambda:0)\n",
        "\n",
        "for i in df_B.index:\n",
        "  date_time = df_B.loc[i,'created_at']\n",
        "  time = date_time.split('Wed Jun 10')[1].strip()\n",
        "  hour = time.split(\":\")[0]\n",
        "  time_B[hour] +=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgRpnqBxZZA0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70af9e0c-7521-45d1-92a6-a47dd8d313e7"
      },
      "source": [
        "plt.plot(list(time_B.keys()), list(time_B.values()))\n",
        "plt.xlabel('Time of the day on Wed Jun 10 ') \n",
        "plt.ylabel('Number of hashtags')\n",
        "plt.title(\"Usage of hash-tags of class B\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VIluFvtYZDg"
      },
      "source": [
        "time_C = defaultdict(lambda:0)\n",
        "\n",
        "for i in df_C.index:\n",
        "  date_time = df_C.loc[i,'created_at']\n",
        "  time = date_time.split('Wed Jun 10')[1].strip()\n",
        "  hour = time.split(\":\")[0]\n",
        "  time_C[hour] +=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7X8HBj9ZDto",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4519202a-f0e8-4a83-8f19-bb17d5b1fac6"
      },
      "source": [
        "plt.plot(list(time_C.keys()), list(time_C.values()))\n",
        "plt.xlabel('Time of the day on Wed Jun 10 ') \n",
        "plt.ylabel('Number of hashtags')\n",
        "plt.title(\"Usage of hash-tags of class C\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTTTacGhlZvt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e458f05-6515-4003-bcda-a6960462e4aa"
      },
      "source": [
        "f, axs = plt.subplots(3,2,figsize=(10,10))\n",
        "\n",
        "plt.subplot(311)\n",
        "plt.plot(list(time_A.keys()), list(time_A.values()))\n",
        "plt.ylabel(\"No of #MissYouYuvi only\")\n",
        "plt.xlabel(\"Time of the day on Wed Jun 10 \")\n",
        "\n",
        "plt.subplot(312)\n",
        "plt.plot(list(time_B.keys()), list(time_B.values()))\n",
        "plt.ylabel(\"No of #RestInPeace only\")\n",
        "plt.xlabel(\"Time of the day on Wed Jun 10\")\n",
        "\n",
        "plt.subplot(313)\n",
        "plt.plot(list(time_C.keys()), list(time_C.values()))\n",
        "plt.ylabel(\"No of #RestInPeace and #MissYouYuvi\")\n",
        "plt.xlabel(\"Time of the day on Wed Jun 10\")\n",
        "\n",
        "f.subplots_adjust(hspace=0.4)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4vMeyRvaYAa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04aeaef2-7481-48ce-d12b-3e41018dd519"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "plt.plot(list(time_A.keys()), list(time_A.values()), label = \"#MissYouYuvi only\")  \n",
        "plt.plot(list(time_B.keys()), list(time_B.values()), label = \"#RestInPeace only\")  \n",
        "plt.plot(list(time_C.keys()), list(time_C.values()), label = \"#RestInPeace and #MissYouYuvi\")  \n",
        "  \n",
        "plt.xlabel('Time of the day on Wed Jun 10 ') \n",
        "\n",
        "plt.ylabel('Number of hashtags') \n",
        "\n",
        "plt.title('Hash-tag Usage')\n",
        "plt.legend()\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8CPcBRQeEIt"
      },
      "source": [
        "####**Preparing the data for processing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_84DPoVuadC"
      },
      "source": [
        "**Data pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zuid08-dwU2"
      },
      "source": [
        "def remove_hashtag(sent):\n",
        "  sent = re.sub(r'(@[A-Za-z0-9]+)|(#[A-Za-z0-9]+)','',sent)\n",
        "  return sent"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPh1n5FkqMRL"
      },
      "source": [
        "def remove_links(text):\n",
        "  text = re.sub(r'(\\w+:\\/\\/\\S+)','',text)\n",
        "  \n",
        "  return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0-4p7CPszgH"
      },
      "source": [
        "def remove_specialChars(text):\n",
        "  text = (re.sub(r'[\\.•,;:?!#=+$%^&*_~><\\-\\(\\)\\{\\}\\[\\]\\\"\\'\\|\\/]','',text)).replace('RT','')\n",
        "  \n",
        "  # changing to lower case\n",
        "  text = text.lower()\n",
        "  return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38b8BrFSlkkr"
      },
      "source": [
        "def replace_emoji(text):\n",
        "  text = emoji.demojize(text)\n",
        "  text = text.replace(\":\",\" \")\n",
        "  tweet = ' '.join(text.split())\n",
        "\n",
        "  return tweet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kY4ljyqTuCpI"
      },
      "source": [
        "def numberTowords(tweet):\n",
        "\n",
        "  final_tweet = []\n",
        "\n",
        "  for word in tweet:\n",
        "    if(word.isnumeric()):\n",
        "      final_tweet.append(num2words(word))\n",
        "    else:\n",
        "      final_tweet.append(word)\n",
        "  \n",
        "  return final_tweet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RK1juVUNC4wg"
      },
      "source": [
        "# function to handle negative contractions\n",
        "\n",
        "def handle_negation(text):\n",
        "  text = re.sub(r'(\\w+)n\\'t', '\\g<1> not',text)\n",
        "  return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PwTnwoPpEE8C"
      },
      "source": [
        "**Tokenise and Lemmatise**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5l2d-r5-EMUD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c8baa6a4-a26f-4728-a6d8-3cb0347620c2"
      },
      "source": [
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "lm = WordNetLemmatizer()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQvPTq4TDYRp"
      },
      "source": [
        "def tokenise(line):\n",
        "  lem_words = []\n",
        "\n",
        "  tokens = line.split()\n",
        "\n",
        "  for j in tokens:\n",
        "    lem_words.append(lm.lemmatize(j))\n",
        "    \n",
        "  return lem_words"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Y1dfP_SEXFV"
      },
      "source": [
        "**Preparing data for analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfOfCFlxEWl6"
      },
      "source": [
        "def get_tweets(df):\n",
        "  cleaned_tweets = []\n",
        "  vocab = set()\n",
        "\n",
        "  tokens_dict = defaultdict(list)                           # dictionary that stores tokens of each tweet\n",
        "  wd_count = defaultdict(lambda:defaultdict(lambda:0))      # dictionary that stores count(term,tweet) i.e. tf\n",
        "  w_count = defaultdict(lambda:0)                           # dictionary that stores df(term)\n",
        "\n",
        "  for i in df.index:\n",
        "    text = df.loc[i,'full_text']\n",
        "\n",
        "    #cleaning the tweets\n",
        "    h_tweet = remove_hashtag(text)\n",
        "    l_tweet = remove_links(h_tweet)\n",
        "    demoji = replace_emoji(l_tweet)\n",
        "    n_tweet = handle_negation(demoji)\n",
        "    clean_tweet = remove_specialChars(n_tweet)\n",
        "\n",
        "    #generate tokens\n",
        "    tokens = tokenise(clean_tweet)\n",
        "\n",
        "    # final = numberTowords(tokens)\n",
        "    # print(final)\n",
        "    \n",
        "    cleaned_tweets.append(tokens)\n",
        "\n",
        "  return cleaned_tweets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sGK8dNtgboL"
      },
      "source": [
        "tweets_A = get_tweets(df_A)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hY6zw4LXE4sc"
      },
      "source": [
        "tweets_B = get_tweets(df_B)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUjHVt-xE8V5"
      },
      "source": [
        "tweets_C = get_tweets(df_C)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qm--acui36uB"
      },
      "source": [
        "####**Topic Modelling using LDA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2SWQc4WsfJU"
      },
      "source": [
        "def build_corpus(final_tweet):\n",
        "  dictionary = corpora.Dictionary()\n",
        "\n",
        "  BoW_corpus = [dictionary.doc2bow(sent, allow_update=True) for sent in final_tweet]\n",
        "  dictionary = gensim.corpora.Dictionary(final_tweet)\n",
        "\n",
        "  tfidf = models.TfidfModel(BoW_corpus)\n",
        "  corpus_tfidf = tfidf[BoW_corpus]\n",
        "\n",
        "  return corpus_tfidf,dictionary"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeOUbPg-Fb6n"
      },
      "source": [
        "BoW_A,dict_A = build_corpus(tweets_A)\n",
        "BoW_B,dict_B = build_corpus(tweets_B)\n",
        "BoW_C,dict_C = build_corpus(tweets_C)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lU77BtZ7G2mz"
      },
      "source": [
        "def find_topics(corpus_tfidf,dictionary,num):\n",
        "\n",
        "  lda_model_tfidf = gensim.models.LdaMulticore(corpus_tfidf, num_topics=num, id2word=dictionary, passes=2, workers=4)\n",
        "\n",
        "  return lda_model_tfidf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8v2xfLfInOt"
      },
      "source": [
        "def show_topics(model,corpus,dictionary):\n",
        "  for idx, topic in model.print_topics(-1):\n",
        "    print('Topic: {} Word: {}'.format(idx, topic))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ng3GiiSDOYKI"
      },
      "source": [
        "**For Class A (#MissYouYuvi only)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNrxGyUJHzaV"
      },
      "source": [
        "#perform topic modelling\n",
        "topics = 15\n",
        "model_A = find_topics(BoW_A,dict_A,topics)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVQVe3w6KSCO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "79412e48-d97b-4e58-b89c-e01cfb26bed6"
      },
      "source": [
        "show_topics(model_A,BoW_A,dict_A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic: 0 Word: 0.009*\"a\" + 0.007*\"jersey\" + 0.007*\"you\" + 0.007*\"no\" + 0.006*\"12\" + 0.006*\"fire\" + 0.006*\"this\" + 0.006*\"to\" + 0.006*\"cricketgame\" + 0.005*\"of\"\n",
            "Topic: 1 Word: 0.023*\"heartsuit\" + 0.015*\"foldedhands\" + 0.012*\"a\" + 0.011*\"we\" + 0.011*\"you\" + 0.010*\"sir\" + 0.010*\"miss\" + 0.008*\"u\" + 0.008*\"yuvian\" + 0.008*\"the\"\n",
            "Topic: 2 Word: 0.048*\"redheart\" + 0.013*\"yuvi\" + 0.012*\"no\" + 0.010*\"yuvian\" + 0.010*\"love\" + 0.009*\"you\" + 0.008*\"like\" + 0.007*\"longer\" + 0.007*\"nd\" + 0.007*\"we\"\n",
            "Topic: 3 Word: 0.017*\"fire\" + 0.012*\"you\" + 0.012*\"the\" + 0.010*\"yuvi\" + 0.010*\"pensiveface\" + 0.010*\"miss\" + 0.010*\"is\" + 0.009*\"love\" + 0.009*\"of\" + 0.008*\"when\"\n",
            "Topic: 4 Word: 0.014*\"in\" + 0.013*\"you\" + 0.009*\"will\" + 0.009*\"year\" + 0.009*\"heart\" + 0.009*\"our\" + 0.008*\"completed\" + 0.008*\"of\" + 0.008*\"never\" + 0.008*\"speed\"\n",
            "Topic: 5 Word: 0.022*\"are\" + 0.019*\"always\" + 0.015*\"you\" + 0.014*\"yuvi\" + 0.012*\"phase\" + 0.012*\"with\" + 0.011*\"a\" + 0.011*\"the\" + 0.010*\"we\" + 0.009*\"in\"\n",
            "Topic: 6 Word: 0.017*\"stalwart\" + 0.011*\"immense\" + 0.010*\"is\" + 0.010*\"contribution\" + 0.009*\"redheart\" + 0.009*\"to\" + 0.008*\"your\" + 0.008*\"yuvi\" + 0.007*\"cricket\" + 0.006*\"fire\"\n",
            "Topic: 7 Word: 0.012*\"blueheart\" + 0.010*\"you\" + 0.009*\"miss\" + 0.009*\"of\" + 0.008*\"man\" + 0.008*\"one\" + 0.008*\"we\" + 0.008*\"the\" + 0.008*\"million\" + 0.007*\"u\"\n",
            "Topic: 8 Word: 0.010*\"…\" + 0.010*\"loudlycryingface\" + 0.009*\"heartsuit\" + 0.009*\"redheart\" + 0.008*\"my\" + 0.008*\"for\" + 0.007*\"i\" + 0.007*\"the\" + 0.007*\"you\" + 0.007*\"in\"\n",
            "Topic: 9 Word: 0.018*\"foldedhands\" + 0.014*\"emotion\" + 0.012*\"million\" + 0.011*\"one\" + 0.011*\"man\" + 0.010*\"of\" + 0.010*\"you\" + 0.008*\"fire\" + 0.008*\"redheart\" + 0.008*\"stalwart\"\n",
            "Topic: 10 Word: 0.024*\"collision\" + 0.020*\"tag\" + 0.017*\"bhavi\" + 0.016*\"champ\" + 0.012*\"you\" + 0.010*\"hash\" + 0.010*\"spread\" + 0.010*\"your\" + 0.010*\"use\" + 0.010*\"share\"\n",
            "Topic: 11 Word: 0.030*\"brokenheart\" + 0.021*\"love\" + 0.014*\"you\" + 0.010*\"hero\" + 0.010*\"enjoy\" + 0.010*\"faceblowingakiss\" + 0.010*\"blackheart\" + 0.010*\"thalaivaaaaa\" + 0.009*\"happy\" + 0.009*\"my\"\n",
            "Topic: 12 Word: 0.023*\"fire\" + 0.018*\"rt\" + 0.013*\"yuvian\" + 0.011*\"singh\" + 0.011*\"yuvraj\" + 0.010*\"the\" + 0.010*\"of\" + 0.009*\"redheart\" + 0.009*\"you\" + 0.009*\"u\"\n",
            "Topic: 13 Word: 0.011*\"yuvi\" + 0.010*\"the\" + 0.010*\"he\" + 0.009*\"you\" + 0.008*\"is\" + 0.008*\"to\" + 0.007*\"top\" + 0.007*\"yuvian\" + 0.007*\"it\" + 0.007*\"trending\"\n",
            "Topic: 14 Word: 0.011*\"six\" + 0.011*\"who\" + 0.010*\"tweet\" + 0.010*\"you\" + 0.010*\"completed\" + 0.010*\"minute\" + 0.010*\"hour\" + 0.010*\"speed\" + 0.010*\"and\" + 0.009*\"miss\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NO6VoZA-fPcH"
      },
      "source": [
        "**_Analysis_**\n",
        "\n",
        "From the words obtained in the topics,\n",
        "* Topic 1: People remembering what a legend Yuvraj Singh was.\n",
        "* Topic 2: People are praising Yuvraj Singh\n",
        "\n",
        "The results obtained are not very diverse. All the topics express that people are missing Yujvraj Singh in the Indian Cricket team.\n",
        "The sentiment of all the topics is positive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BZe9wuCfkIZ"
      },
      "source": [
        "Most dominant topic of class A is shown below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TocD_hAgJ5CP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "e0a2f751-d31f-48e6-90ce-fd925fb156a6"
      },
      "source": [
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(model_A, BoW_A, dict_A)\n",
        "vis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1221401145124728482390335418\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1221401145124728482390335418_data = {\"mdsDat\": {\"x\": [-0.03956225074085251, -0.026747844814435037, -0.0162992065523486, -0.009026759122246736, 0.07517168386602176, 0.050092555968762636, 0.011003221912327735, -0.034950760650567375, -0.0743103789996383, -0.02416660757297641, -0.03657443821335929, 0.1277140492961591, -0.020782406858664913, -0.0008859808958357188, 0.01932512337765362], \"y\": [0.02513335575063955, 0.019971797267287133, 0.03726625068126367, -0.11341811811738674, 0.05781601743941495, 0.061636367773950644, 0.021329826464789807, -0.0327188515727187, 0.027150400596868723, 0.017056494919384785, 0.004295619808489628, -0.04080770584137318, -0.014404354809296985, -0.05730357008828542, -0.013003530273028209], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [9.903667449951172, 8.657322883605957, 8.575479507446289, 6.8541646003723145, 6.731418609619141, 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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
              "topic                                               \n",
              "5     -0.039562  0.025133       1        1  9.903667\n",
              "14    -0.026748  0.019972       2        1  8.657323\n",
              "9     -0.016299  0.037266       3        1  8.575480\n",
              "11    -0.009027 -0.113418       4        1  6.854165\n",
              "3      0.075172  0.057816       5        1  6.731419\n",
              "12     0.050093  0.061636       6        1  6.716328\n",
              "13     0.011003  0.021330       7        1  6.715363\n",
              "8     -0.034951 -0.032719       8        1  6.461961\n",
              "10    -0.074310  0.027150       9        1  6.376820\n",
              "1     -0.024167  0.017056      10        1  6.168150\n",
              "4     -0.036574  0.004296      11        1  5.890129\n",
              "2      0.127714 -0.040808      12        1  5.841338\n",
              "7     -0.020782 -0.014404      13        1  5.786773\n",
              "0     -0.000886 -0.057304      14        1  4.901534\n",
              "6      0.019325 -0.013004      15        1  4.419557, topic_info=            Term        Freq       Total Category  logprob  loglift\n",
              "86      redheart  118.000000  118.000000  Default  30.0000  30.0000\n",
              "287  brokenheart   51.000000   51.000000  Default  29.0000  29.0000\n",
              "429    collision   43.000000   43.000000  Default  28.0000  28.0000\n",
              "237         fire   74.000000   74.000000  Default  27.0000  27.0000\n",
              "281          tag   26.000000   26.000000  Default  26.0000  26.0000\n",
              "..           ...         ...         ...      ...      ...      ...\n",
              "394        speed    2.352716   37.581905  Topic15  -5.5718   0.3482\n",
              "119       always    2.472739   78.709114  Topic15  -5.5221  -0.3413\n",
              "9            are    2.432671   87.970978  Topic15  -5.5384  -0.4689\n",
              "662            1    2.327497   36.199780  Topic15  -5.5826   0.3749\n",
              "200         this    2.314790   55.551205  Topic15  -5.5881  -0.0589\n",
              "\n",
              "[1020 rows x 6 columns], token_table=      Topic      Freq Term\n",
              "term                      \n",
              "1103      2  0.120974   0f\n",
              "1103      3  0.120974   0f\n",
              "1103      6  0.120974   0f\n",
              "1103     13  0.483895   0f\n",
              "662       1  0.082873    1\n",
              "...     ...       ...  ...\n",
              "138      13  0.103814    …\n",
              "138      14  0.025954    …\n",
              "138      15  0.051907    …\n",
              "3238      8  0.217009    ♡\n",
              "3238     15  0.217009    ♡\n",
              "\n",
              "[2908 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 15, 10, 12, 4, 13, 14, 9, 11, 2, 5, 3, 8, 1, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9_R05xqOjl5"
      },
      "source": [
        "**For Class B (#RestInPeace only)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6IXInRINc_X"
      },
      "source": [
        "topics = 8\n",
        "model_B = find_topics(BoW_B,dict_B,topics)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8Lt0g6PNkKq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "68140ec5-9896-4f7a-b082-1adce4ab7c70"
      },
      "source": [
        "show_topics(model_B,BoW_B,dict_B)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic: 0 Word: 0.006*\"campaign\" + 0.006*\"govt\" + 0.006*\"school\" + 0.006*\"covid\" + 0.006*\"class\" + 0.006*\"19\" + 0.006*\"10th\" + 0.005*\"…\" + 0.005*\"ஜெஅன்பழகன்\" + 0.004*\"in\"\n",
            "Topic: 1 Word: 0.007*\"…\" + 0.006*\"அடக்கம்\" + 0.006*\"செய்யப்பட்டது\" + 0.005*\"உடல்\" + 0.005*\"முதல்வர்\" + 0.005*\"எமஎல்ஏ\" + 0.005*\"பினராயி\" + 0.005*\"கேரள\" + 0.005*\"grinningfacewithsweat\" + 0.005*\"dhak\"\n",
            "Topic: 2 Word: 0.006*\"to\" + 0.006*\"and\" + 0.005*\"பொருளாளர்\" + 0.005*\"துரைமுருகன்\" + 0.005*\"the\" + 0.005*\"loudlycryingface\" + 0.005*\"such\" + 0.004*\"ஜெஅன்பழகன்\" + 0.004*\"a\" + 0.004*\"their\"\n",
            "Topic: 3 Word: 0.049*\"ht…\" + 0.048*\"சீமான்\" + 0.046*\"எம்எல்ஏ\" + 0.045*\"மறைவுக்கு\" + 0.045*\"இரங்கல்\" + 0.042*\"திமுக\" + 0.038*\"ஜெஅன்பழகன்\" + 0.003*\"yes\" + 0.003*\"his\" + 0.003*\"are\"\n",
            "Topic: 4 Word: 0.016*\"rip\" + 0.016*\"foldedhands\" + 0.015*\"bouquet\" + 0.014*\"may\" + 0.012*\"he\" + 0.007*\"இரங்கல்\" + 0.007*\"திமுக\" + 0.007*\"மறைவுக்கு\" + 0.007*\"எம்எல்ஏ\" + 0.007*\"ஜெஅன்பழகன்\"\n",
            "Topic: 5 Word: 0.005*\"and\" + 0.005*\"the\" + 0.004*\"whitemediumstar\" + 0.004*\"sparkle\" + 0.004*\"rest\" + 0.004*\"glowingstar\" + 0.004*\"blackheart\" + 0.004*\"illuminardi\" + 0.004*\"respectblackpink\" + 0.004*\"me\"\n",
            "Topic: 6 Word: 0.009*\"இறுதி\" + 0.009*\"மறைந்த\" + 0.009*\"அஞ்சலி\" + 0.009*\"அவரது\" + 0.009*\"புதல்வர்\" + 0.009*\"திருராஜாஅன்பழகன்\" + 0.009*\"அவர்களுக்கு\" + 0.009*\"செலுத்தும்\" + 0.008*\"இன்று\" + 0.007*\"what\"\n",
            "Topic: 7 Word: 0.007*\"வீரவணக்கம்\" + 0.005*\"foldedhands\" + 0.005*\"i\" + 0.004*\"memory\" + 0.004*\"with\" + 0.003*\"yuvi\" + 0.003*\"my\" + 0.003*\"a\" + 0.003*\"the\" + 0.003*\"के\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nikNlKHft4P"
      },
      "source": [
        "**_Analysis_**\n",
        "\n",
        "Most of the tweets containing #RestInPeace contained words from some South Indian languages which were uninterpretable. Since, these words were quite frequent and occurred in most of the tweets, they were retained after selecting tf-idf features and thus appeared in topics as well.\n",
        "\n",
        "Of the words that could be read, my analysis is as follows:\n",
        "* Topic 2 : People express their dissatisfaction about the ruling political party.\n",
        "* Topic 4 : People expressing their tributes and sorrow towards “dubey” who possibly\n",
        "might’ve been infected by corona.\n",
        "* Topic 8 : Grief expressed by people."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_g1eWmrNvbh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "f22f8dbe-3c50-4716-b458-c2056b91f8d4"
      },
      "source": [
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(model_B, BoW_B, dict_B)\n",
        "vis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1221401145018895043479319187\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1221401145018895043479319187_data = {\"mdsDat\": {\"x\": [-0.13462240573105663, 0.032973338371387606, 0.014799468539503504, 0.01153444225247742, -0.018344219201564878, 0.028014589912676236, 0.03673937511625188, 0.028905410740324704], \"y\": [0.009175150944233233, 0.07548084008472387, -0.013819885821255321, 0.011198240200138357, -0.028254776827463746, -0.02140331524002027, -0.02057332516745319, -0.011802928172903031], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [21.216083526611328, 13.488091468811035, 12.48568344116211, 12.383230209350586, 10.850069999694824, 10.45981216430664, 10.275392532348633, 8.841635704040527]}, \"tinfo\": {\"Term\": [\"\\u0b9a\\u0bc0\\u0bae\\u0bbe\\u0ba9\\u0bcd\", \"ht\\u2026\", \"\\u0b8e\\u0bae\\u0bcd\\u0b8e\\u0bb2\\u0bcd\\u0b8f\", \"\\u0b87\\u0bb0\\u0b99\\u0bcd\\u0b95\\u0bb2\\u0bcd\", \"\\u0bae\\u0bb1\\u0bc8\\u0bb5\\u0bc1\\u0b95\\u0bcd\\u0b95\\u0bc1\", 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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "3     -0.134622  0.009175       1        1  21.216084\n",
              "6      0.032973  0.075481       2        1  13.488091\n",
              "2      0.014799 -0.013820       3        1  12.485683\n",
              "0      0.011534  0.011198       4        1  12.383230\n",
              "4     -0.018344 -0.028255       5        1  10.850070\n",
              "1      0.028015 -0.021403       6        1  10.459812\n",
              "7      0.036739 -0.020573       7        1  10.275393\n",
              "5      0.028905 -0.011803       8        1   8.841636, topic_info=          Term       Freq      Total Category  logprob  loglift\n",
              "3       சீமான்  14.000000  14.000000  Default  30.0000  30.0000\n",
              "0          ht…  14.000000  14.000000  Default  29.0000  29.0000\n",
              "2      எம்எல்ஏ  14.000000  14.000000  Default  28.0000  28.0000\n",
              "1      இரங்கல்  14.000000  14.000000  Default  27.0000  27.0000\n",
              "6    மறைவுக்கு  14.000000  14.000000  Default  26.0000  26.0000\n",
              "..         ...        ...        ...      ...      ...      ...\n",
              "31          in   0.341617   2.714607   Topic8  -5.7428   0.3530\n",
              "138         wa   0.307724   2.128980   Topic8  -5.8473   0.4915\n",
              "114         of   0.286410   2.446879   Topic8  -5.9191   0.2806\n",
              "470      after   0.273404   1.435385   Topic8  -5.9656   0.7675\n",
              "157        his   0.271327   1.890675   Topic8  -5.9732   0.4843\n",
              "\n",
              "[421 rows x 6 columns], token_table=      Topic      Freq        Term\n",
              "term                             \n",
              "848       4  0.605555        10th\n",
              "515       4  0.484526          19\n",
              "7         2  0.324174           a\n",
              "7         3  0.324174           a\n",
              "7         5  0.324174           a\n",
              "...     ...       ...         ...\n",
              "400       4  0.931431  வீட்டிற்கு\n",
              "726       7  0.844951  வீரவணக்கம்\n",
              "127       2  0.264245           …\n",
              "127       4  0.264245           …\n",
              "127       6  0.264245           …\n",
              "\n",
              "[131 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 7, 3, 1, 5, 2, 8, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rk01xepNOrMq"
      },
      "source": [
        "**For Class C (Both #RestInPeace and #MissYouYuvi)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piK1pFSIN13k"
      },
      "source": [
        "topics = 8\n",
        "model_C = find_topics(BoW_C,dict_C,topics)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waAbBEs5N_1z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "3c59f37d-4786-42db-c232-51a60acbfd44"
      },
      "source": [
        "show_topics(model_C,BoW_C,dict_C)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic: 0 Word: 0.018*\"be\" + 0.016*\"on\" + 0.014*\"like\" + 0.013*\"i\" + 0.012*\"fan\" + 0.012*\"and\" + 0.012*\"to\" + 0.011*\"twitter\" + 0.010*\"facewithrollingeyes\" + 0.009*\"top\"\n",
            "Topic: 1 Word: 0.018*\"heart\" + 0.017*\"…\" + 0.016*\"my\" + 0.013*\"stopped\" + 0.011*\"omg\" + 0.011*\"seenoevilmonkey\" + 0.011*\"ge\" + 0.011*\"bai\" + 0.010*\"a\" + 0.010*\"and\"\n",
            "Topic: 2 Word: 0.015*\"stuart\" + 0.015*\"broad\" + 0.015*\"i\" + 0.015*\"together\" + 0.014*\"they\" + 0.014*\"le\" + 0.013*\"watching\" + 0.013*\"in\" + 0.011*\"when\" + 0.011*\"and\"\n",
            "Topic: 3 Word: 0.012*\"the\" + 0.010*\"scared\" + 0.010*\"hell\" + 0.009*\"me\" + 0.009*\"are\" + 0.009*\"out\" + 0.009*\"time\" + 0.008*\"yuvraj\" + 0.008*\"at\" + 0.008*\"same\"\n",
            "Topic: 4 Word: 0.015*\"cacsexa…\" + 0.011*\"rollingonthefloorlaughing\" + 0.007*\"hashtag\" + 0.006*\"𝙎𝙞𝙣𝙜𝙝\" + 0.006*\"𝙡𝙞𝙠𝙚\" + 0.006*\"𝙗𝙚\" + 0.006*\"kawaii\" + 0.006*\"𝙛𝙖𝙣𝙨\" + 0.006*\"𝙔𝙪𝙫𝙧𝙖𝙟\" + 0.006*\"fuck\"\n",
            "Topic: 5 Word: 0.045*\"जननायकछात्रों…\" + 0.028*\"singh\" + 0.027*\"seeing\" + 0.027*\"yuvraj\" + 0.025*\"after\" + 0.023*\"together\" + 0.016*\"writing\" + 0.014*\"and\" + 0.011*\"people\" + 0.010*\"watching\"\n",
            "Topic: 6 Word: 0.045*\"indian\" + 0.045*\"every\" + 0.044*\"rn\" + 0.043*\"joking\" + 0.040*\"amp\" + 0.039*\"time\" + 0.038*\"at\" + 0.038*\"same\" + 0.035*\"when\" + 0.033*\"the\"\n",
            "Topic: 7 Word: 0.054*\"fuck\" + 0.049*\"moment\" + 0.049*\"scared\" + 0.046*\"for\" + 0.042*\"a\" + 0.017*\"people\" + 0.014*\"this\" + 0.014*\"bloody\" + 0.012*\"whats\" + 0.012*\"you\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXNxLtTUgBRJ"
      },
      "source": [
        "**_Analysis_**\n",
        "\n",
        "Topics in this class showed mixed emotions: some reported to be extremely negative and others extremely positive.\n",
        "\n",
        "* Topic 1 shows negative sentiments, with more weight given to the words like “fuck”, “scared”."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uzZGwciOFy8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "fa66aa58-01b4-4bf8-d25a-a8437d80f60d"
      },
      "source": [
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(model_C, BoW_C, dict_C)\n",
        "vis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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0.7899467945098877, 0.8088081479072571, 0.3525884747505188, 0.3525884747505188], \"Term\": [\"2020\", \"a\", \"a\", \"a\", \"a\", \"after\", \"after\", \"after\", \"all\", \"amp\", \"amp\", \"and\", \"and\", \"and\", \"and\", \"and\", \"and\", \"and\", \"are\", \"are\", \"are\", \"are\", \"are\", \"at\", \"at\", \"at\", \"at\", \"at\", \"attack\", \"aur\", \"backhandindexpointingdown\", \"bai\", \"bandittalro\", \"be\", \"be\", \"beat\", \"bloody\", \"bluebirdie\", \"broad\", \"but\", \"cacsexa\\u2026\", \"chahal\", \"coincidence\", \"condition\", \"dboss\", \"down\", \"ek\", \"every\", \"every\", \"facewithhandovermouth\", \"facewithmedicalmask\", \"facewithrollingeyes\", \"facewithrollingeyes\", \"facewithtearsofjoy\", \"facewithtearsofjoy\", \"facewiththermometer\", \"fan\", \"fan\", \"fans\\u2026\", \"far\", \"feel\", \"first\", \"foldedhandslightskintone\", \"for\", \"for\", \"for\", \"for\", \"friend\", \"fuck\", \"fuck\", \"fucked\", \"fun\", \"ge\", \"god\", \"hai\", \"have\", 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\"these\", \"these\", \"they\", \"this\", \"this\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"to\", \"to\", \"together\", \"together\", \"together\", \"too\", \"top\", \"trend\", \"trend\", \"trending\", \"trending\", \"trending\", \"trending\", \"trending\", \"trending\", \"trending\", \"tulip\", \"twitter\", \"twitter\", \"twitter\", \"twitteraties\", \"up\", \"upsidedownface\", \"vandana\", \"verma0\", \"wa\", \"watching\", \"watching\", \"whats\", \"when\", \"when\", \"when\", \"when\", \"winkingface\", \"writing\", \"you\", \"yuvi\", \"yuvi\", \"yuvraj\", \"yuvraj\", \"yuzvendra\", \"\\u091c\\u0928\\u0928\\u093e\\u092f\\u0915\\u091b\\u093e\\u0924\\u094d\\u0930\\u094b\\u0902\\u2026\", \"\\u0939\\u0948\", \"\\u2026\", \"\\u2026\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 8, 6, 3, 2, 1, 4, 5]};\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "6      0.159386 -0.015593       1        1  21.481840\n",
              "7     -0.044442 -0.131947       2        1  18.268526\n",
              "5     -0.093433  0.027051       3        1  12.654514\n",
              "2      0.003642  0.044027       4        1  11.269789\n",
              "1     -0.018690  0.042025       5        1  10.539447\n",
              "0     -0.006634  0.014585       6        1  10.428085\n",
              "3      0.003922 -0.007136       7        1   8.794520\n",
              "4     -0.003751  0.026988       8        1   6.563277, topic_info=               Term      Freq     Total Category  logprob  loglift\n",
              "186  जननायकछात्रों…  5.000000  5.000000  Default  30.0000  30.0000\n",
              "103            fuck  9.000000  9.000000  Default  29.0000  29.0000\n",
              "90           scared  9.000000  9.000000  Default  28.0000  28.0000\n",
              "77           moment  9.000000  9.000000  Default  27.0000  27.0000\n",
              "51            every  9.000000  9.000000  Default  26.0000  26.0000\n",
              "..              ...       ...       ...      ...      ...      ...\n",
              "53           joking  0.248968  9.552759   Topic8  -5.3357  -0.9236\n",
              "54               rn  0.248973  9.787193   Topic8  -5.3357  -0.9478\n",
              "51            every  0.246361  9.915074   Topic8  -5.3462  -0.9713\n",
              "49              amp  0.243555  9.433722   Topic8  -5.3577  -0.9330\n",
              "52           indian  0.241381  9.929102   Topic8  -5.3666  -0.9932\n",
              "\n",
              "[414 rows x 6 columns], token_table=      Topic      Freq            Term\n",
              "term                                 \n",
              "37        4  0.603975            2020\n",
              "74        1  0.104911               a\n",
              "74        2  0.629468               a\n",
              "74        3  0.104911               a\n",
              "74        5  0.104911               a\n",
              "...     ...       ...             ...\n",
              "582       2  0.682202       yuzvendra\n",
              "186       3  0.789947  जननायकछात्रों…\n",
              "35        2  0.808808              है\n",
              "73        1  0.352588               …\n",
              "73        5  0.352588               …\n",
              "\n",
              "[225 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 8, 6, 3, 2, 1, 4, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cklf5W51QDeq"
      },
      "source": [
        "**Analysing sentiments of each class**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIKYGLYtQCxq"
      },
      "source": [
        "def detect_sentiment(sentence): \n",
        "    senti = SentimentIntensityAnalyzer() \n",
        "    sentiment_score = senti.polarity_scores(sentence) \n",
        "      \n",
        "    print(\"Sentiment scores are: \", sentiment_score) \n",
        "\n",
        "    if sentiment_score['compound'] <= - 0.05 :\n",
        "      sentiment = \"Negative\"\n",
        "      return sentiment\n",
        "\n",
        "    elif sentiment_score['compound'] >= 0.05:\n",
        "      sentiment = \"Positive\"\n",
        "      return sentiment\n",
        "        \n",
        "    else : \n",
        "      sentiment = \"Neutral\"\n",
        "      return sentiment\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqrHjO9JP9S2"
      },
      "source": [
        "def class_sentiment(model,dictionary,num_topics):\n",
        "  # find the most significant words in each topic(here I've taken top 5 words)\n",
        "  topic_words = []\n",
        "\n",
        "  for i in range(num_topics):\n",
        "    #considering top 5 words from each topic for sentiment analysis\n",
        "    tt = model_A.get_topic_terms(i,5)\n",
        "    topic_words.append([dict_A[pair[0]] for pair in tt])\n",
        "\n",
        "\n",
        "  # Detect the sentiment of each topic\n",
        "  j = 0\n",
        "  for topic in topic_words:\n",
        "    #making sentence from the top 5 words for each topic\n",
        "    sentence = ' '.join(map(str, topic)) \n",
        "    print(\"For topic \",j,\":\")\n",
        "    j+=1\n",
        "    print(\"Topic Overall Rated As\",detect_sentiment(sentence))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "499e8V6TfVJ8"
      },
      "source": [
        "**for class A**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byCG02GxfL8-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "outputId": "4896245e-f630-44bc-9a00-1868cf5edf8a"
      },
      "source": [
        "class_sentiment(model_A,dict_A,15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For topic  0 :\n",
            "Sentiment scores are:  {'neg': 0.355, 'neu': 0.645, 'pos': 0.0, 'compound': -0.296}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  1 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  2 :\n",
            "Sentiment scores are:  {'neg': 0.65, 'neu': 0.35, 'pos': 0.0, 'compound': -0.6776}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  3 :\n",
            "Sentiment scores are:  {'neg': 0.375, 'neu': 0.625, 'pos': 0.0, 'compound': -0.34}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  4 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 0.488, 'pos': 0.512, 'compound': 0.6369}\n",
            "Topic Overall Rated As Positive\n",
            "For topic  5 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  6 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  7 :\n",
            "Sentiment scores are:  {'neg': 0.286, 'neu': 0.714, 'pos': 0.0, 'compound': -0.1531}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  8 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  9 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  10 :\n",
            "Sentiment scores are:  {'neg': 0.291, 'neu': 0.349, 'pos': 0.36, 'compound': 0.1531}\n",
            "Topic Overall Rated As Positive\n",
            "For topic  11 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 0.154, 'pos': 0.846, 'compound': 0.9001}\n",
            "Topic Overall Rated As Positive\n",
            "For topic  12 :\n",
            "Sentiment scores are:  {'neg': 0.375, 'neu': 0.625, 'pos': 0.0, 'compound': -0.34}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  13 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  14 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qs_qHUc5glXA"
      },
      "source": [
        "\n",
        "\n",
        "> The most dominant sentiment in the class is **Neutral**. So, class A has **Neutral sentiment**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mUwrb0pfjUp"
      },
      "source": [
        "**for class B**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW0W6tQZfiM3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "1c90e837-0ef0-406c-ddcb-ae3b7fe08c7a"
      },
      "source": [
        "class_sentiment(model_B,dict_B,8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For topic  0 :\n",
            "Sentiment scores are:  {'neg': 0.355, 'neu': 0.645, 'pos': 0.0, 'compound': -0.296}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  1 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  2 :\n",
            "Sentiment scores are:  {'neg': 0.65, 'neu': 0.35, 'pos': 0.0, 'compound': -0.6776}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  3 :\n",
            "Sentiment scores are:  {'neg': 0.375, 'neu': 0.625, 'pos': 0.0, 'compound': -0.34}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  4 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 0.488, 'pos': 0.512, 'compound': 0.6369}\n",
            "Topic Overall Rated As Positive\n",
            "For topic  5 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  6 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  7 :\n",
            "Sentiment scores are:  {'neg': 0.286, 'neu': 0.714, 'pos': 0.0, 'compound': -0.1531}\n",
            "Topic Overall Rated As Negative\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jI-slOjIfy4r"
      },
      "source": [
        "\n",
        "\n",
        "> The most dominant sentiment in the class is **Negative**. So, class B has **negative sentiment**.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0CZQ4aLfrTq"
      },
      "source": [
        "**for class C**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpQQC1cHfrHk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "1ee11040-8a1c-4ac5-ae71-ca2f9e83f957"
      },
      "source": [
        "class_sentiment(model_C,dict_C,8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For topic  0 :\n",
            "Sentiment scores are:  {'neg': 0.355, 'neu': 0.645, 'pos': 0.0, 'compound': -0.296}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  1 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  2 :\n",
            "Sentiment scores are:  {'neg': 0.65, 'neu': 0.35, 'pos': 0.0, 'compound': -0.6776}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  3 :\n",
            "Sentiment scores are:  {'neg': 0.375, 'neu': 0.625, 'pos': 0.0, 'compound': -0.34}\n",
            "Topic Overall Rated As Negative\n",
            "For topic  4 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 0.488, 'pos': 0.512, 'compound': 0.6369}\n",
            "Topic Overall Rated As Positive\n",
            "For topic  5 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  6 :\n",
            "Sentiment scores are:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
            "Topic Overall Rated As Neutral\n",
            "For topic  7 :\n",
            "Sentiment scores are:  {'neg': 0.286, 'neu': 0.714, 'pos': 0.0, 'compound': -0.1531}\n",
            "Topic Overall Rated As Negative\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBzAFj6Ega0V"
      },
      "source": [
        "\n",
        "\n",
        "> The most dominant sentiment in the class is **Negative**. So, class C has **negative sentiment**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7EJUssuXQwE"
      },
      "source": [
        "**Number of deaths (loaction-wise)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rV6WhlqgXO9o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "outputId": "043e6650-9cc0-4613-8021-7dfd6fc5fc48"
      },
      "source": [
        "df_B"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>_id</th>\n",
              "      <th>created_at</th>\n",
              "      <th>id</th>\n",
              "      <th>id_str</th>\n",
              "      <th>full_text</th>\n",
              "      <th>truncated</th>\n",
              "      <th>display_text_range</th>\n",
              "      <th>entities</th>\n",
              "      <th>metadata</th>\n",
              "      <th>source</th>\n",
              "      <th>in_reply_to_status_id</th>\n",
              "      <th>in_reply_to_status_id_str</th>\n",
              "      <th>in_reply_to_user_id</th>\n",
              "      <th>in_reply_to_user_id_str</th>\n",
              "      <th>in_reply_to_screen_name</th>\n",
              "      <th>user</th>\n",
              "      <th>geo</th>\n",
              "      <th>coordinates</th>\n",
              "      <th>place</th>\n",
              "      <th>contributors</th>\n",
              "      <th>retweeted_status</th>\n",
              "      <th>is_quote_status</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>favorite_count</th>\n",
              "      <th>favorited</th>\n",
              "      <th>retweeted</th>\n",
              "      <th>lang</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570717'}</td>\n",
              "      <td>Wed Jun 10 18:19:05 +0000 2020</td>\n",
              "      <td>1270782555357212700</td>\n",
              "      <td>1270782555357212672</td>\n",
              "      <td>RT @PTTVOnlineNews: திமுக எம்எல்ஏ ஜெ.அன்பழகன் ...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 140]</td>\n",
              "      <td>{'hashtags': [{'text': 'JAnbazhagan', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'ta', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1146440916527243300, 'id_str': '1146440...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 06:32:16 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>ta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570721'}</td>\n",
              "      <td>Wed Jun 10 18:17:40 +0000 2020</td>\n",
              "      <td>1270782198824788000</td>\n",
              "      <td>1270782198824787977</td>\n",
              "      <td>Virtual funerals are becoming more common, as ...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 270]</td>\n",
              "      <td>{'hashtags': [{'text': 'RestInPeace', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1209427513136562200, 'id_str': '1209427...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570729'}</td>\n",
              "      <td>Wed Jun 10 18:15:27 +0000 2020</td>\n",
              "      <td>1270781638583042000</td>\n",
              "      <td>1270781638583042048</td>\n",
              "      <td>😭😭\\n\\n#RestInPeace https://t.co/N3Qm59QSjz</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 16]</td>\n",
              "      <td>{'hashtags': [{'text': 'RestInPeace', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'und', 'result_type': 'r...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1112792532545466400, 'id_str': '1112792...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>und</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570734'}</td>\n",
              "      <td>Wed Jun 10 18:12:53 +0000 2020</td>\n",
              "      <td>1270780994518474800</td>\n",
              "      <td>1270780994518474753</td>\n",
              "      <td>Rest in peace, George Floyd\\nWe Will Not Forge...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 173]</td>\n",
              "      <td>{'hashtags': [{'text': 'RIPGeorgeFloyd', 'indi...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1254525094937985000, 'id_str': '1254525...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>{'$oid': '5ee1244dc51e2d8beb570739'}</td>\n",
              "      <td>Wed Jun 10 18:11:10 +0000 2020</td>\n",
              "      <td>1270780563331453000</td>\n",
              "      <td>1270780563331452929</td>\n",
              "      <td>RT @nehaa_sinha: And two firefighters have los...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 137]</td>\n",
              "      <td>{'hashtags': [{'text': 'RestInPeace', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 2784446966, 'id_str': '2784446966', 'na...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 07:37:39 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>{'$oid': '5ee125dcc51e2d8beb572a71'}</td>\n",
              "      <td>Wed Jun 10 09:05:47 +0000 2020</td>\n",
              "      <td>1270643310009278500</td>\n",
              "      <td>1270643310009278464</td>\n",
              "      <td>@ANI @rsprasad He's just an MP like any other ...</td>\n",
              "      <td>False</td>\n",
              "      <td>[5, 293]</td>\n",
              "      <td>{'hashtags': [{'text': 'karachi', 'indices': [...</td>\n",
              "      <td>{'iso_language_code': 'en', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>1.270628e+18</td>\n",
              "      <td>1270627935351762944</td>\n",
              "      <td>355989081.0</td>\n",
              "      <td>355989081</td>\n",
              "      <td>ANI</td>\n",
              "      <td>{'id': 1233066191389782000, 'id_str': '1233066...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9070</th>\n",
              "      <td>{'$oid': '5ee125dcc51e2d8beb572a85'}</td>\n",
              "      <td>Wed Jun 10 09:05:09 +0000 2020</td>\n",
              "      <td>1270643152593064000</td>\n",
              "      <td>1270643152593063936</td>\n",
              "      <td>RT @itsvjdmk: #JAnbazhagan\\n#RestInPeace\\n#RIP...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 90]</td>\n",
              "      <td>{'hashtags': [{'text': 'JAnbazhagan', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'und', 'result_type': 'r...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1007808431908982800, 'id_str': '1007808...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 08:39:15 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>und</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9117</th>\n",
              "      <td>{'$oid': '5ee125e0c51e2d8beb572ab4'}</td>\n",
              "      <td>Wed Jun 10 09:04:18 +0000 2020</td>\n",
              "      <td>1270642938494832600</td>\n",
              "      <td>1270642938494832642</td>\n",
              "      <td>#JAnbazhagan \\n#RestInPeace https://t.co/KP5UI...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 26]</td>\n",
              "      <td>{'hashtags': [{'text': 'JAnbazhagan', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'und', 'result_type': 'r...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1260019038538752000, 'id_str': '1260019...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>und</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9140</th>\n",
              "      <td>{'$oid': '5ee125e0c51e2d8beb572acb'}</td>\n",
              "      <td>Wed Jun 10 09:03:47 +0000 2020</td>\n",
              "      <td>1270642809276522500</td>\n",
              "      <td>1270642809276522496</td>\n",
              "      <td>RT @thatsTamil: சென்னை கண்ணம்மாபேட்டையில் ஜெ. ...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 140]</td>\n",
              "      <td>{'hashtags': [{'text': 'JAnbazhagan', 'indices...</td>\n",
              "      <td>{'iso_language_code': 'ta', 'result_type': 're...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1161653145375654000, 'id_str': '1161653...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 07:41:08 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>ta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9159</th>\n",
              "      <td>{'$oid': '5ee125e0c51e2d8beb572ade'}</td>\n",
              "      <td>Wed Jun 10 09:03:26 +0000 2020</td>\n",
              "      <td>1270642720604926000</td>\n",
              "      <td>1270642720604925954</td>\n",
              "      <td>RT @Iambhaarat1: #BiharKaCmGayabHai\\n#RestInPe...</td>\n",
              "      <td>False</td>\n",
              "      <td>[0, 72]</td>\n",
              "      <td>{'hashtags': [{'text': 'BiharKaCmGayabHai', 'i...</td>\n",
              "      <td>{'iso_language_code': 'und', 'result_type': 'r...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'id': 1253935697326559200, 'id_str': '1253935...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'created_at': 'Wed Jun 10 08:59:04 +0000 2020...</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>und</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>416 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       _id  ... lang\n",
              "0     {'$oid': '5ee1244dc51e2d8beb570717'}  ...   ta\n",
              "10    {'$oid': '5ee1244dc51e2d8beb570721'}  ...   en\n",
              "18    {'$oid': '5ee1244dc51e2d8beb570729'}  ...  und\n",
              "29    {'$oid': '5ee1244dc51e2d8beb570734'}  ...   en\n",
              "34    {'$oid': '5ee1244dc51e2d8beb570739'}  ...   en\n",
              "...                                    ...  ...  ...\n",
              "9050  {'$oid': '5ee125dcc51e2d8beb572a71'}  ...   en\n",
              "9070  {'$oid': '5ee125dcc51e2d8beb572a85'}  ...  und\n",
              "9117  {'$oid': '5ee125e0c51e2d8beb572ab4'}  ...  und\n",
              "9140  {'$oid': '5ee125e0c51e2d8beb572acb'}  ...   ta\n",
              "9159  {'$oid': '5ee125e0c51e2d8beb572ade'}  ...  und\n",
              "\n",
              "[416 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6ZQJ7TNXdT-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0ff6b3ee-a567-474b-8338-0164902fd727"
      },
      "source": [
        "locations = []\n",
        "for i in df_B.index:\n",
        "  locations.append(df_B.loc[i,'user']['location'])\n",
        "  print(df_B.loc[i,'user']['location'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Charlotte, NC\n",
            "Andhra Pradesh, India\n",
            "مصر\n",
            "Switzerland\n",
            "\n",
            "\n",
            "England UK \n",
            "Kailua-Kona, HI\n",
            "Currently on planet Earth\n",
            "Hubli, India\n",
            "Global\n",
            "Hardoi, India\n",
            "Venezuela\n",
            "Johannesburg, South Africa\n",
            "\n",
            "Orl\n",
            "Madurai\n",
            "Dubai, United Arab Emirates\n",
            "\n",
            "PNW\n",
            "செக்காணூரணி, மதுரை\n",
            "New Delhi\n",
            "13 SummerSt, Rivonia, Sandton \n",
            "Somewhere Quiet\n",
            "Tamil Nadu, India\n",
            "Eldoret\n",
            "Somewhere Nowhere\n",
            "Singapore\n",
            "United States\n",
            "Chidambaram\n",
            "Marion, North Carolina\n",
            "world\n",
            "PDX☔️{in}TheUK☔️{daydreaming}\n",
            "\n",
            "california,usa\n",
            "Tamil Nadu, India\n",
            "Denver, Colorado\n",
            "\n",
            "Ramanathapuram \n",
            "\n",
            "\n",
            "Nationwide\n",
            "\n",
            "\n",
            "New York, USA\n",
            "Coimbatore, India\n",
            "\n",
            "United States\n",
            "\n",
            "India\n",
            "\n",
            "\n",
            "Singapore\n",
            "Guwahati, India\n",
            "Singapore\n",
            "Coimbatore, Tamilnadu\n",
            "Lovely Earth\n",
            "chennai\n",
            "Nairobi, Kenya\n",
            "ছিপাঝাৰ \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Southern California\n",
            "Tamil Nadu, India\n",
            "தல( தமிழ்)நாடு\n",
            "contact@vindylee.com\n",
            "MikeBoneMedia.com\n",
            "India\n",
            "\n",
            "Nairobi, Kenya\n",
            "India\n",
            "\n",
            "தஞ்சாவூர்\n",
            "\n",
            "Johannesburg, South Africa\n",
            "India\n",
            "Chennai\n",
            "\n",
            "West Virginia,USA\n",
            "Maharashtra, India\n",
            "East Bum F!#@, CT\n",
            "Chicago, IL\n",
            "Chennai, India\n",
            "Salem, India\n",
            "Albuquerque, NM\n",
            "மதுரை TamilNadu \n",
            "HONG KONG\n",
            "Nairobi\n",
            "North Lakhimpur, India\n",
            "Assam, India\n",
            "Chennai, India\n",
            "\n",
            "Ludhiana, India\n",
            "\n",
            "Chengalpattu, India\n",
            "Assam, India\n",
            "Kanungu \n",
            "Indore.M.P\n",
            "Kinshasa \n",
            "Ranchi, India\n",
            "\n",
            "Zuirch\n",
            "#LuchaKliq\n",
            "Tamil Nadu, India\n",
            "Ghana\n",
            "Nagercoil\n",
            "Yogyakarta, Indonesia\n",
            "\n",
            "\n",
            "Tamil \n",
            "Dubai, United Arab Emirates\n",
            "Tamil Nadu, India\n",
            "CNP,dindigul dist.TAMIL NADU\n",
            "Guwahati, Assam.\n",
            "\n",
            "\n",
            "India\n",
            "Chennai\n",
            "தொண்டாமுத்தூர், கோவை \n",
            "Twickenham, UK\n",
            "Nairobi\n",
            "Tamil Nadu, India\n",
            "Chennai, India\n",
            "Chennai, India\n",
            "Thiruthuraipoondi, India\n",
            "India\n",
            "\n",
            "Putnam, CT\n",
            "Jammu, Jammu And Kashmir\n",
            "Chennai india\n",
            "\n",
            "Karnal, Haryana, India\n",
            "Legoland\n",
            "\n",
            "\n",
            "India\n",
            "\n",
            "तमिलनाडु, भारत\n",
            "Here\n",
            "Yogyakarta, Indonesia\n",
            "Montana, USA\n",
            "Doha, Qatar\n",
            "தமிழ், தமிழர், தமிழ்நாடு, ஃ\n",
            "Hyderabad, India\n",
            "Orissa, India\n",
            "North Carolina, USA\n",
            "\n",
            "Bharat\n",
            "Bharat\n",
            "\n",
            "\n",
            "Yavatmal, India\n",
            "கொங்கு மண்டலம் \n",
            "Mombasa, Kenya\n",
            "Mumbai\n",
            "TN 49\n",
            "chennai,india\n",
            "\n",
            "DINDIGUL\n",
            "Egmore Nungambakkam, India\n",
            "Chennai\n",
            "India\n",
            "Laxmi Nagar, New Delhi\n",
            "Error 404 \n",
            "Kenya\n",
            "Sivakasi, India\n",
            "\n",
            "\n",
            "New York, USA\n",
            "தாராபுரம் திருப்பூர் மாவட்டம் \n",
            "\n",
            "Ariyalur, India\n",
            "India\n",
            "Kenya\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Chennai \n",
            "\n",
            "\n",
            "Kiambu Road,Nairobi, Kenya\n",
            "New Delhi, Delhi\n",
            "IT IS KNOWN  💯🌺 \n",
            "Chennai, India\n",
            "राजस्थान\n",
            "أبوظبي, الامارات العربية المتح\n",
            "Mumbai, India\n",
            "Nairobi, Kenya\n",
            "\n",
            "Nairobi, Kenya\n",
            "\n",
            "🇮🇳\n",
            "\n",
            "\n",
            "Jodhpur\n",
            "\n",
            "Karatina, Kenya\n",
            "Karatina, Kenya\n",
            "NunYa biz,\n",
            "Doha, Qatar \n",
            "ஈரோடு, தமிழ்நாடு\n",
            "Kenya\n",
            "\n",
            "\n",
            "Nairobi kenya\n",
            "Patna, India\n",
            "Tamil Nadu, India\n",
            "\n",
            "On a rock floating in space\n",
            "Tamil Nadu, India\n",
            "Karatina, Kenya\n",
            "Bhopal\n",
            "\n",
            "Marysville, WA\n",
            "Karatina, Kenya\n",
            "Darbhanga, India\n",
            "\n",
            "\n",
            "Al Jahra, Kuwait\n",
            "\n",
            "\n",
            "France\n",
            "Nakuru\n",
            "Nairobi, Kenya\n",
            "TN45,சோழநாடு\n",
            "Burundi\n",
            "kanpur\n",
            "Kenya\n",
            "Rajasthan, India\n",
            "தஞ்சை\n",
            "Hyderabad, India\n",
            "\n",
            "\n",
            "India\n",
            "\n",
            "Gurgaon, India\n",
            "\n",
            "Nairobi, Kenya\n",
            "Santa Ana, CA\n",
            "\n",
            "Pune, India\n",
            "\n",
            "Pune, India\n",
            "\n",
            "\n",
            "Maldives \n",
            "Silverdale, WA\n",
            "London, England\n",
            "Mumbai, India\n",
            "\n",
            "\n",
            "Pune\n",
            "Pune\n",
            "Pune\n",
            "\n",
            "Pune\n",
            "Yuvi ka Dil ❤\n",
            "\n",
            "Bangalore\n",
            "Bangalore\n",
            "Bangalore\n",
            "India\n",
            "\n",
            "\n",
            "Melbourne\n",
            "Chennai/Bengaluru\n",
            "Chennai, India\n",
            "Tiruchirapalli, India\n",
            "India\n",
            "India\n",
            "AKHAND BHARAT🚩\n",
            "Pune\n",
            "Chennai, India\n",
            "Allagadda, India\n",
            "Indore, India\n",
            "\n",
            "chennai\n",
            "\n",
            "Faridabad, haryana\n",
            "\n",
            "\n",
            "Surat, India\n",
            "Pune\n",
            "India\n",
            "Calcutta, India\n",
            "Pune\n",
            "Patna, India\n",
            "India\n",
            "India\n",
            "Chennai, Tamilnadu\n",
            "India\n",
            "\n",
            "Varanasi\n",
            "New Delhi, India\n",
            "Jaipur, India\n",
            "Kalinjikuppam\n",
            "ஈரோடு & கோவை\n",
            "\n",
            "Chennai\n",
            "Patna, Bihar\n",
            "\n",
            "\n",
            "Chennai\n",
            "\n",
            "Chennai, India\n",
            "India , Indore , Sendhwa\n",
            "गुड़गांव, भारत\n",
            "Chennai, India\n",
            "Jewar, India\n",
            "New Delhi , Noida \n",
            "Uttar Pradesh, India\n",
            "\n",
            "AIFF Football House, New Delhi\n",
            "India Normally\n",
            "Bihar, India\n",
            "RATANGARH\n",
            "Bengaluru, India\n",
            "\n",
            "Calicut\n",
            "Kanyakumari, India\n",
            "India\n",
            "\n",
            "Bengaluru, India\n",
            "Bengaluru, India\n",
            "Chennai, India\n",
            "Unofficial assembly\n",
            "\n",
            "India\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ஈரோடு\n",
            "Tamil Nadu, India\n",
            "Delhi, India\n",
            "Hyderabad, India\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "अजमेर, भारत\n",
            "Chennai, India\n",
            "Thirunelveli \n",
            "சிவகங்கை\n",
            "Chennai, India\n",
            "\n",
            "\n",
            "Bihar, India\n",
            "\n",
            "\n",
            "\n",
            "New Delhi, India\n",
            "\n",
            "Vaniyambadi,Tamizhnadu,India\n",
            "\n",
            "Jaipur, India\n",
            " Thiruthani,Thamizhnadu,India\n",
            "Auckland, New Zealand\n",
            "Thirupattur,Thamizhnadu,India\n",
            "\n",
            "\n",
            "Hardoi, India\n",
            "Rishikesh, India\n",
            "Neyveli Township\n",
            "India\n",
            "Chennai\n",
            "Noida (UP)\n",
            "Central Region, Singapore\n",
            "Chennai, India\n",
            "Tamil Nadu, India\n",
            "\n",
            "coimbatore\n",
            "India\n",
            "Vitry-sur-Seine\n",
            "செங்கோட்டை, லெமூரியா கண்டம்\n",
            "\n",
            "Pondicherry, India\n",
            "New Delhi, India\n",
            "Mansa punjab\n",
            "Tamil Nadu, India\n",
            "திருநெல்வேலி\n",
            "Indore, India\n",
            "\n",
            "\n",
            "Neelangarai, Chennai\n",
            "\n",
            "South Australia, Adelaide\n",
            "Neyveli Township\n",
            "Raipur, Chhattisgarh\n",
            "\n",
            "\n",
            "Kozhippara,Palakkad \n",
            "\n",
            "Chennai, India\n",
            "தமிழ்நாடு\n",
            "Mumbai, India\n",
            "காட்டாங்கொளத்தூர்\n",
            "\n",
            "Trichy\n",
            "தமிழ்நாடு\n",
            "Pattukkottai, Thanjavur\n",
            "Neyveli Township\n",
            "Indore,M.P\n",
            "Chennai, India\n",
            "Darbhanga, India\n",
            "\n",
            "Erode, India\n",
            "Coimbatore, India\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CDZvHdPX8qK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "de780969-993b-4028-f093-5dbbadab8daa"
      },
      "source": [
        "Counter(locations)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'': 119,\n",
              "         ' Thiruthani,Thamizhnadu,India': 1,\n",
              "         '#LuchaKliq': 1,\n",
              "         '13 SummerSt, Rivonia, Sandton ': 1,\n",
              "         'AIFF Football House, New Delhi': 1,\n",
              "         'AKHAND BHARAT🚩': 1,\n",
              "         'Al Jahra, Kuwait': 1,\n",
              "         'Albuquerque, NM': 1,\n",
              "         'Allagadda, India': 1,\n",
              "         'Andhra Pradesh, India': 1,\n",
              "         'Ariyalur, India': 1,\n",
              "         'Assam, India': 2,\n",
              "         'Auckland, New Zealand': 1,\n",
              "         'Bangalore': 3,\n",
              "         'Bengaluru, India': 3,\n",
              "         'Bharat': 2,\n",
              "         'Bhopal': 1,\n",
              "         'Bihar, India': 2,\n",
              "         'Burundi': 1,\n",
              "         'CNP,dindigul dist.TAMIL NADU': 1,\n",
              "         'Calcutta, India': 1,\n",
              "         'Calicut': 1,\n",
              "         'Central Region, Singapore': 1,\n",
              "         'Charlotte, NC': 1,\n",
              "         'Chengalpattu, India': 1,\n",
              "         'Chennai': 6,\n",
              "         'Chennai ': 1,\n",
              "         'Chennai india': 1,\n",
              "         'Chennai, India': 15,\n",
              "         'Chennai, Tamilnadu': 1,\n",
              "         'Chennai/Bengaluru': 1,\n",
              "         'Chicago, IL': 1,\n",
              "         'Chidambaram': 1,\n",
              "         'Coimbatore, India': 2,\n",
              "         'Coimbatore, Tamilnadu': 1,\n",
              "         'Currently on planet Earth': 1,\n",
              "         'DINDIGUL': 1,\n",
              "         'Darbhanga, India': 2,\n",
              "         'Delhi, India': 1,\n",
              "         'Denver, Colorado': 1,\n",
              "         'Doha, Qatar': 1,\n",
              "         'Doha, Qatar ': 1,\n",
              "         'Dubai, United Arab Emirates': 2,\n",
              "         'East Bum F!#@, CT': 1,\n",
              "         'Egmore Nungambakkam, India': 1,\n",
              "         'Eldoret': 1,\n",
              "         'England UK ': 1,\n",
              "         'Erode, India': 1,\n",
              "         'Error 404 ': 1,\n",
              "         'Faridabad, haryana': 1,\n",
              "         'France': 1,\n",
              "         'Ghana': 1,\n",
              "         'Global': 1,\n",
              "         'Gurgaon, India': 1,\n",
              "         'Guwahati, Assam.': 1,\n",
              "         'Guwahati, India': 1,\n",
              "         'HONG KONG': 1,\n",
              "         'Hardoi, India': 2,\n",
              "         'Here': 1,\n",
              "         'Hubli, India': 1,\n",
              "         'Hyderabad, India': 3,\n",
              "         'IT IS KNOWN  💯🌺 ': 1,\n",
              "         'India': 21,\n",
              "         'India , Indore , Sendhwa': 1,\n",
              "         'India Normally': 1,\n",
              "         'Indore, India': 2,\n",
              "         'Indore,M.P': 1,\n",
              "         'Indore.M.P': 1,\n",
              "         'Jaipur, India': 2,\n",
              "         'Jammu, Jammu And Kashmir': 1,\n",
              "         'Jewar, India': 1,\n",
              "         'Jodhpur': 1,\n",
              "         'Johannesburg, South Africa': 2,\n",
              "         'Kailua-Kona, HI': 1,\n",
              "         'Kalinjikuppam': 1,\n",
              "         'Kanungu ': 1,\n",
              "         'Kanyakumari, India': 1,\n",
              "         'Karatina, Kenya': 4,\n",
              "         'Karnal, Haryana, India': 1,\n",
              "         'Kenya': 4,\n",
              "         'Kiambu Road,Nairobi, Kenya': 1,\n",
              "         'Kinshasa ': 1,\n",
              "         'Kozhippara,Palakkad ': 1,\n",
              "         'Laxmi Nagar, New Delhi': 1,\n",
              "         'Legoland': 1,\n",
              "         'London, England': 1,\n",
              "         'Lovely Earth': 1,\n",
              "         'Ludhiana, India': 1,\n",
              "         'Madurai': 1,\n",
              "         'Maharashtra, India': 1,\n",
              "         'Maldives ': 1,\n",
              "         'Mansa punjab': 1,\n",
              "         'Marion, North Carolina': 1,\n",
              "         'Marysville, WA': 1,\n",
              "         'Melbourne': 1,\n",
              "         'MikeBoneMedia.com': 1,\n",
              "         'Mombasa, Kenya': 1,\n",
              "         'Montana, USA': 1,\n",
              "         'Mumbai': 1,\n",
              "         'Mumbai, India': 3,\n",
              "         'Nagercoil': 1,\n",
              "         'Nairobi': 2,\n",
              "         'Nairobi kenya': 1,\n",
              "         'Nairobi, Kenya': 6,\n",
              "         'Nakuru': 1,\n",
              "         'Nationwide': 1,\n",
              "         'Neelangarai, Chennai': 1,\n",
              "         'New Delhi': 1,\n",
              "         'New Delhi , Noida ': 1,\n",
              "         'New Delhi, Delhi': 1,\n",
              "         'New Delhi, India': 3,\n",
              "         'New York, USA': 2,\n",
              "         'Neyveli Township': 3,\n",
              "         'Noida (UP)': 1,\n",
              "         'North Carolina, USA': 1,\n",
              "         'North Lakhimpur, India': 1,\n",
              "         'NunYa biz,': 1,\n",
              "         'On a rock floating in space': 1,\n",
              "         'Orissa, India': 1,\n",
              "         'Orl': 1,\n",
              "         'PDX☔️{in}TheUK☔️{daydreaming}': 1,\n",
              "         'PNW': 1,\n",
              "         'Patna, Bihar': 1,\n",
              "         'Patna, India': 2,\n",
              "         'Pattukkottai, Thanjavur': 1,\n",
              "         'Pondicherry, India': 1,\n",
              "         'Pune': 7,\n",
              "         'Pune, India': 2,\n",
              "         'Putnam, CT': 1,\n",
              "         'RATANGARH': 1,\n",
              "         'Raipur, Chhattisgarh': 1,\n",
              "         'Rajasthan, India': 1,\n",
              "         'Ramanathapuram ': 1,\n",
              "         'Ranchi, India': 1,\n",
              "         'Rishikesh, India': 1,\n",
              "         'Salem, India': 1,\n",
              "         'Santa Ana, CA': 1,\n",
              "         'Silverdale, WA': 1,\n",
              "         'Singapore': 3,\n",
              "         'Sivakasi, India': 1,\n",
              "         'Somewhere Nowhere': 1,\n",
              "         'Somewhere Quiet': 1,\n",
              "         'South Australia, Adelaide': 1,\n",
              "         'Southern California': 1,\n",
              "         'Surat, India': 1,\n",
              "         'Switzerland': 1,\n",
              "         'TN 49': 1,\n",
              "         'TN45,சோழநாடு': 1,\n",
              "         'Tamil ': 1,\n",
              "         'Tamil Nadu, India': 11,\n",
              "         'Thirunelveli ': 1,\n",
              "         'Thirupattur,Thamizhnadu,India': 1,\n",
              "         'Thiruthuraipoondi, India': 1,\n",
              "         'Tiruchirapalli, India': 1,\n",
              "         'Trichy': 1,\n",
              "         'Twickenham, UK': 1,\n",
              "         'United States': 2,\n",
              "         'Unofficial assembly': 1,\n",
              "         'Uttar Pradesh, India': 1,\n",
              "         'Vaniyambadi,Tamizhnadu,India': 1,\n",
              "         'Varanasi': 1,\n",
              "         'Venezuela': 1,\n",
              "         'Vitry-sur-Seine': 1,\n",
              "         'West Virginia,USA': 1,\n",
              "         'Yavatmal, India': 1,\n",
              "         'Yogyakarta, Indonesia': 2,\n",
              "         'Yuvi ka Dil ❤': 1,\n",
              "         'Zuirch': 1,\n",
              "         'california,usa': 1,\n",
              "         'chennai': 2,\n",
              "         'chennai,india': 1,\n",
              "         'coimbatore': 1,\n",
              "         'contact@vindylee.com': 1,\n",
              "         'kanpur': 1,\n",
              "         'world': 1,\n",
              "         'أبوظبي, الامارات العربية المتح': 1,\n",
              "         'مصر': 1,\n",
              "         'अजमेर, भारत': 1,\n",
              "         'गुड़गांव, भारत': 1,\n",
              "         'तमिलनाडु, भारत': 1,\n",
              "         'राजस्थान': 1,\n",
              "         'ছিপাঝাৰ ': 1,\n",
              "         'ஈரோடு': 1,\n",
              "         'ஈரோடு & கோவை': 1,\n",
              "         'ஈரோடு, தமிழ்நாடு': 1,\n",
              "         'காட்டாங்கொளத்தூர்': 1,\n",
              "         'கொங்கு மண்டலம் ': 1,\n",
              "         'சிவகங்கை': 1,\n",
              "         'செக்காணூரணி, மதுரை': 1,\n",
              "         'செங்கோட்டை, லெமூரியா கண்டம்': 1,\n",
              "         'தஞ்சாவூர்': 1,\n",
              "         'தஞ்சை': 1,\n",
              "         'தமிழ், தமிழர், தமிழ்நாடு, ஃ': 1,\n",
              "         'தமிழ்நாடு': 2,\n",
              "         'தல( தமிழ்)நாடு': 1,\n",
              "         'தாராபுரம் திருப்பூர் மாவட்டம் ': 1,\n",
              "         'திருநெல்வேலி': 1,\n",
              "         'தொண்டாமுத்தூர், கோவை ': 1,\n",
              "         'மதுரை TamilNadu ': 1,\n",
              "         '🇮🇳': 1})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzMzLksPnKfw"
      },
      "source": [
        "We can see that the number of Covid-19 patients increased in Southern India, with many entries of cities of that region. \n",
        "\n",
        "The data from ​ https://www.covid19india.org/​ ​ also certifies the above fact. There was an increase of 300 corona positive cases on 10 June in Tamil Nadu.\n",
        "\n",
        "The topics obtained from topic modelling on class containing #RestInPeace have maximum words of some South Indian language, which again supports the above finding."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCzJ7VdxYbic"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}