Sentimental Analysis on COVID-19 Tweets using python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Name:** Hamza Shafiq\n",
    "\n",
    "**Reach me at:** \n",
    "1. [Github](https://github.com/hamza811998)\n",
    "2. [LinkedIn](https://www.linkedin.com/in/hamza811998/)\n",
    "3. [Fiverr](https://www.fiverr.com/hamza_811998?up_rollout=true)\n",
    "4. [Facebook](https://www.facebook.com/hamza811998/)\n",
    "5. [Twitter](https://twitter.com/hamza811998)\n",
    "6. [Instagram](https://www.instagram.com/hamza811998/)\n",
    "\n",
    "**Email:** hamza811998shafiq@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***`Sentimental Analysis on COVID-19 Tweets using python`***\n",
    "------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Introduction`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\hamza\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\hamza\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import re\n",
    "import string\n",
    "\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from nltk.corpus import words\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.util import *\n",
    "nltk.download('stopwords')\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import ticker\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Collected from the link: (\"https://raw.githubusercontent.com/gabrielpreda/covid-19-tweets/master/covid19_tweets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>user_location</th>\n",
       "      <th>user_description</th>\n",
       "      <th>user_created</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_friends</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>user_verified</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>source</th>\n",
       "      <th>is_retweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ᏉᎥ☻լꂅϮ</td>\n",
       "      <td>astroworld</td>\n",
       "      <td>wednesday addams as a disney princess keepin i...</td>\n",
       "      <td>2017-05-26 05:46:42</td>\n",
       "      <td>624</td>\n",
       "      <td>950</td>\n",
       "      <td>18775</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-07-25 12:27:21</td>\n",
       "      <td>If I smelled the scent of hand sanitizers toda...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom Basile 🇺🇸</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Husband, Father, Columnist &amp; Commentator. Auth...</td>\n",
       "      <td>2009-04-16 20:06:23</td>\n",
       "      <td>2253</td>\n",
       "      <td>1677</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-07-25 12:27:17</td>\n",
       "      <td>Hey @Yankees @YankeesPR and @MLB - wouldn't it...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Time4fisticuffs</td>\n",
       "      <td>Pewee Valley, KY</td>\n",
       "      <td>#Christian #Catholic #Conservative #Reagan #Re...</td>\n",
       "      <td>2009-02-28 18:57:41</td>\n",
       "      <td>9275</td>\n",
       "      <td>9525</td>\n",
       "      <td>7254</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-07-25 12:27:14</td>\n",
       "      <td>@diane3443 @wdunlap @realDonaldTrump Trump nev...</td>\n",
       "      <td>['COVID19']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ethel mertz</td>\n",
       "      <td>Stuck in the Middle</td>\n",
       "      <td>#Browns #Indians #ClevelandProud #[]_[] #Cavs ...</td>\n",
       "      <td>2019-03-07 01:45:06</td>\n",
       "      <td>197</td>\n",
       "      <td>987</td>\n",
       "      <td>1488</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-07-25 12:27:10</td>\n",
       "      <td>@brookbanktv The one gift #COVID19 has give me...</td>\n",
       "      <td>['COVID19']</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DIPR-J&amp;K</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>🖊️Official Twitter handle of Department of Inf...</td>\n",
       "      <td>2017-02-12 06:45:15</td>\n",
       "      <td>101009</td>\n",
       "      <td>168</td>\n",
       "      <td>101</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-07-25 12:27:08</td>\n",
       "      <td>25 July : Media Bulletin on Novel #CoronaVirus...</td>\n",
       "      <td>['CoronaVirusUpdates', 'COVID19']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_name         user_location  \\\n",
       "0           ᏉᎥ☻լꂅϮ            astroworld   \n",
       "1    Tom Basile 🇺🇸          New York, NY   \n",
       "2  Time4fisticuffs      Pewee Valley, KY   \n",
       "3      ethel mertz  Stuck in the Middle    \n",
       "4         DIPR-J&K     Jammu and Kashmir   \n",
       "\n",
       "                                    user_description         user_created  \\\n",
       "0  wednesday addams as a disney princess keepin i...  2017-05-26 05:46:42   \n",
       "1  Husband, Father, Columnist & Commentator. Auth...  2009-04-16 20:06:23   \n",
       "2  #Christian #Catholic #Conservative #Reagan #Re...  2009-02-28 18:57:41   \n",
       "3  #Browns #Indians #ClevelandProud #[]_[] #Cavs ...  2019-03-07 01:45:06   \n",
       "4  🖊️Official Twitter handle of Department of Inf...  2017-02-12 06:45:15   \n",
       "\n",
       "   user_followers  user_friends  user_favourites  user_verified  \\\n",
       "0             624           950            18775          False   \n",
       "1            2253          1677               24           True   \n",
       "2            9275          9525             7254          False   \n",
       "3             197           987             1488          False   \n",
       "4          101009           168              101          False   \n",
       "\n",
       "                  date                                               text  \\\n",
       "0  2020-07-25 12:27:21  If I smelled the scent of hand sanitizers toda...   \n",
       "1  2020-07-25 12:27:17  Hey @Yankees @YankeesPR and @MLB - wouldn't it...   \n",
       "2  2020-07-25 12:27:14  @diane3443 @wdunlap @realDonaldTrump Trump nev...   \n",
       "3  2020-07-25 12:27:10  @brookbanktv The one gift #COVID19 has give me...   \n",
       "4  2020-07-25 12:27:08  25 July : Media Bulletin on Novel #CoronaVirus...   \n",
       "\n",
       "                            hashtags               source  is_retweet  \n",
       "0                                NaN   Twitter for iPhone       False  \n",
       "1                                NaN  Twitter for Android       False  \n",
       "2                        ['COVID19']  Twitter for Android       False  \n",
       "3                        ['COVID19']   Twitter for iPhone       False  \n",
       "4  ['CoronaVirusUpdates', 'COVID19']  Twitter for Android       False  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/gabrielpreda/covid-19-tweets/master/covid19_tweets.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's check the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179108, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's select the needed columns for our project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ᏉᎥ☻լꂅϮ</td>\n",
       "      <td>2020-07-25 12:27:21</td>\n",
       "      <td>If I smelled the scent of hand sanitizers toda...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom Basile 🇺🇸</td>\n",
       "      <td>2020-07-25 12:27:17</td>\n",
       "      <td>Hey @Yankees @YankeesPR and @MLB - wouldn't it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Time4fisticuffs</td>\n",
       "      <td>2020-07-25 12:27:14</td>\n",
       "      <td>@diane3443 @wdunlap @realDonaldTrump Trump nev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ethel mertz</td>\n",
       "      <td>2020-07-25 12:27:10</td>\n",
       "      <td>@brookbanktv The one gift #COVID19 has give me...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DIPR-J&amp;K</td>\n",
       "      <td>2020-07-25 12:27:08</td>\n",
       "      <td>25 July : Media Bulletin on Novel #CoronaVirus...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_name                 date  \\\n",
       "0           ᏉᎥ☻լꂅϮ  2020-07-25 12:27:21   \n",
       "1    Tom Basile 🇺🇸  2020-07-25 12:27:17   \n",
       "2  Time4fisticuffs  2020-07-25 12:27:14   \n",
       "3      ethel mertz  2020-07-25 12:27:10   \n",
       "4         DIPR-J&K  2020-07-25 12:27:08   \n",
       "\n",
       "                                                text  \n",
       "0  If I smelled the scent of hand sanitizers toda...  \n",
       "1  Hey @Yankees @YankeesPR and @MLB - wouldn't it...  \n",
       "2  @diane3443 @wdunlap @realDonaldTrump Trump nev...  \n",
       "3  @brookbanktv The one gift #COVID19 has give me...  \n",
       "4  25 July : Media Bulletin on Novel #CoronaVirus...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "needed_columns = ['user_name', 'date', 'text']\n",
    "df = df[needed_columns]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change the type of some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76403</td>\n",
       "      <td>2020-07-25 12:27:17</td>\n",
       "      <td>Hey @Yankees @YankeesPR and @MLB - wouldn't it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76147</td>\n",
       "      <td>2020-07-25 12:27:14</td>\n",
       "      <td>@diane3443 @wdunlap @realDonaldTrump Trump nev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84572</td>\n",
       "      <td>2020-07-25 12:27:10</td>\n",
       "      <td>@brookbanktv The one gift #COVID19 has give me...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18398</td>\n",
       "      <td>2020-07-25 12:27:08</td>\n",
       "      <td>25 July : Media Bulletin on Novel #CoronaVirus...</td>\n",
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      "text/plain": [
       "   user_name                 date  \\\n",
       "0      89755  2020-07-25 12:27:21   \n",
       "1      76403  2020-07-25 12:27:17   \n",
       "2      76147  2020-07-25 12:27:14   \n",
       "3      84572  2020-07-25 12:27:10   \n",
       "4      18398  2020-07-25 12:27:08   \n",
       "\n",
       "                                                text  \n",
       "0  If I smelled the scent of hand sanitizers toda...  \n",
       "1  Hey @Yankees @YankeesPR and @MLB - wouldn't it...  \n",
       "2  @diane3443 @wdunlap @realDonaldTrump Trump nev...  \n",
       "3  @brookbanktv The one gift #COVID19 has give me...  \n",
       "4  25 July : Media Bulletin on Novel #CoronaVirus...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.user_name = df.user_name.astype('category')\n",
    "df.user_name = df.user_name.cat.codes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_name</th>\n",
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       "      <th>text</th>\n",
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       "      <th>1</th>\n",
       "      <td>76403</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>Hey @Yankees @YankeesPR and @MLB - wouldn't it...</td>\n",
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       "      <th>2</th>\n",
       "      <td>76147</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>@diane3443 @wdunlap @realDonaldTrump Trump nev...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84572</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>@brookbanktv The one gift #COVID19 has give me...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18398</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>25 July : Media Bulletin on Novel #CoronaVirus...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_name        date                                               text\n",
       "0      89755  2020-07-25  If I smelled the scent of hand sanitizers toda...\n",
       "1      76403  2020-07-25  Hey @Yankees @YankeesPR and @MLB - wouldn't it...\n",
       "2      76147  2020-07-25  @diane3443 @wdunlap @realDonaldTrump Trump nev...\n",
       "3      84572  2020-07-25  @brookbanktv The one gift #COVID19 has give me...\n",
       "4      18398  2020-07-25  25 July : Media Bulletin on Novel #CoronaVirus..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date = pd.to_datetime(df.date).dt.date\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Text Preprocessing`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Picking out the tweet texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         If I smelled the scent of hand sanitizers toda...\n",
       "1         Hey @Yankees @YankeesPR and @MLB - wouldn't it...\n",
       "2         @diane3443 @wdunlap @realDonaldTrump Trump nev...\n",
       "3         @brookbanktv The one gift #COVID19 has give me...\n",
       "4         25 July : Media Bulletin on Novel #CoronaVirus...\n",
       "                                ...                        \n",
       "179103    Thanks @IamOhmai for nominating me for the @WH...\n",
       "179104    2020! The year of insanity! Lol! #COVID19 http...\n",
       "179105    @CTVNews A powerful painting by Juan Lucena. I...\n",
       "179106    More than 1,200 students test positive for #CO...\n",
       "179107    I stop when I see a Stop\\n\\n@SABCNews\\n@Izinda...\n",
       "Name: text, Length: 179108, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = df['text']\n",
    "texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing URLs from tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         If I smelled the scent of hand sanitizers toda...\n",
       "1         Hey @Yankees @YankeesPR and @MLB - wouldn't it...\n",
       "2         @diane3443 @wdunlap @realDonaldTrump Trump nev...\n",
       "3         @brookbanktv The one gift #COVID19 has give me...\n",
       "4         25 July : Media Bulletin on Novel #CoronaVirus...\n",
       "                                ...                        \n",
       "179103    Thanks @IamOhmai for nominating me for the @WH...\n",
       "179104           2020! The year of insanity! Lol! #COVID19 \n",
       "179105    @CTVNews A powerful painting by Juan Lucena. I...\n",
       "179106    More than 1,200 students test positive for #CO...\n",
       "179107    I stop when I see a Stop\\n\\n@SABCNews\\n@Izinda...\n",
       "Name: text, Length: 179108, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_url = lambda x: re.sub(r'https\\S+', '', str(x))\n",
    "texts_lr = texts.apply(remove_url)\n",
    "texts_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting all tweets to lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         if i smelled the scent of hand sanitizers toda...\n",
       "1         hey @yankees @yankeespr and @mlb - wouldn't it...\n",
       "2         @diane3443 @wdunlap @realdonaldtrump trump nev...\n",
       "3         @brookbanktv the one gift #covid19 has give me...\n",
       "4         25 july : media bulletin on novel #coronavirus...\n",
       "                                ...                        \n",
       "179103    thanks @iamohmai for nominating me for the @wh...\n",
       "179104           2020! the year of insanity! lol! #covid19 \n",
       "179105    @ctvnews a powerful painting by juan lucena. i...\n",
       "179106    more than 1,200 students test positive for #co...\n",
       "179107    i stop when i see a stop\\n\\n@sabcnews\\n@izinda...\n",
       "Name: text, Length: 179108, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_lower = lambda x: x.lower()\n",
    "texts_lr_lc = texts_lr.apply(to_lower)\n",
    "texts_lr_lc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing punctuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         if i smelled the scent of hand sanitizers toda...\n",
       "1         hey yankees yankeespr and mlb  wouldnt it have...\n",
       "2         diane3443 wdunlap realdonaldtrump trump never ...\n",
       "3         brookbanktv the one gift covid19 has give me i...\n",
       "4         25 july  media bulletin on novel coronavirusup...\n",
       "                                ...                        \n",
       "179103    thanks iamohmai for nominating me for the who ...\n",
       "179104               2020 the year of insanity lol covid19 \n",
       "179105    ctvnews a powerful painting by juan lucena its...\n",
       "179106    more than 1200 students test positive for covi...\n",
       "179107    i stop when i see a stop\\n\\nsabcnews\\nizindaba...\n",
       "Name: text, Length: 179108, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_puncs = lambda x: x.translate(str.maketrans('', '', string.punctuation))\n",
    "texts_lr_lc_np = texts_lr_lc.apply(remove_puncs)\n",
    "texts_lr_lc_np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Let's Explore our data`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         smelled scent hand sanitizers today someone pa...\n",
       "1         hey yankees yankeespr mlb wouldnt made sense p...\n",
       "2         diane3443 wdunlap realdonaldtrump trump never ...\n",
       "3         brookbanktv one gift give appreciation simple ...\n",
       "4         25 july media bulletin novel coronavirusupdate...\n",
       "                                ...                        \n",
       "179103    thanks iamohmai nominating wearamask challenge...\n",
       "179104                               2020 year insanity lol\n",
       "179105    ctvnews powerful painting juan lucena tribute ...\n",
       "179106    1200 students test positive major university a...\n",
       "179107    stop see stop sabcnews izindabanews24 dailysun...\n",
       "Name: text, Length: 179108, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_words = ['covid','#coronavirus', '#coronavirusoutbreak', '#coronavirusPandemic', '#covid19', '#covid_19', '#epitwitter', '#ihavecorona', 'amp', 'coronavirus', 'covid19']\n",
    "stop_words = set(stopwords.words('English'))\n",
    "stop_words.update(more_words)\n",
    "\n",
    "remove_words = lambda x: \" \".join([word for word in x.split() if word not in stop_words])\n",
    "texts_lr_lc_np_ns = texts_lr_lc_np.apply(remove_words)\n",
    "texts_lr_lc_np_ns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's create a big list of words out of all the tweets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['smelled', 'scent', 'hand', 'sanitizers', 'today']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_list = [word for line in texts_lr_lc_np_ns for word in line.split()]\n",
    "words_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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    "word_counts = Counter(words_list).most_common(50)\n",
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           "align": "left"
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          "hovermode": "closest",
          "mapbox": {
           "style": "light"
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          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
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           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Most Frequent/Common Words"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "word"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "frq"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.bar(words_df, x='word', y='frq', title='Most Frequent/Common Words')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### put the Cleaned text in main dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_name</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76403</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>Hey @Yankees @YankeesPR and @MLB - wouldn't it...</td>\n",
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       "      <th>2</th>\n",
       "      <td>76147</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>@diane3443 @wdunlap @realDonaldTrump Trump nev...</td>\n",
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       "      <th>3</th>\n",
       "      <td>84572</td>\n",
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       "      <td>@brookbanktv The one gift #COVID19 has give me...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2020-07-25</td>\n",
       "      <td>25 July : Media Bulletin on Novel #CoronaVirus...</td>\n",
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      ],
      "text/plain": [
       "   user_name        date                                               text\n",
       "0      89755  2020-07-25  If I smelled the scent of hand sanitizers toda...\n",
       "1      76403  2020-07-25  Hey @Yankees @YankeesPR and @MLB - wouldn't it...\n",
       "2      76147  2020-07-25  @diane3443 @wdunlap @realDonaldTrump Trump nev...\n",
       "3      84572  2020-07-25  @brookbanktv The one gift #COVID19 has give me...\n",
       "4      18398  2020-07-25  25 July : Media Bulletin on Novel #CoronaVirus..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>text</th>\n",
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       "      <td>89755</td>\n",
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       "      <td>2020-07-25</td>\n",
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       "      <th>3</th>\n",
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       "      <td>25 july media bulletin novel coronavirusupdate...</td>\n",
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      ],
      "text/plain": [
       "   user_name        date                                               text\n",
       "0      89755  2020-07-25  smelled scent hand sanitizers today someone pa...\n",
       "1      76403  2020-07-25  hey yankees yankeespr mlb wouldnt made sense p...\n",
       "2      76147  2020-07-25  diane3443 wdunlap realdonaldtrump trump never ...\n",
       "3      84572  2020-07-25  brookbanktv one gift give appreciation simple ...\n",
       "4      18398  2020-07-25  25 july media bulletin novel coronavirusupdate..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text = texts_lr_lc_np_ns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Sentiment Analysis`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the polarity scores for each tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         {'neg': 0.0, 'neu': 0.758, 'pos': 0.242, 'comp...\n",
       "1         {'neg': 0.11, 'neu': 0.709, 'pos': 0.181, 'com...\n",
       "2         {'neg': 0.0, 'neu': 0.832, 'pos': 0.168, 'comp...\n",
       "3         {'neg': 0.0, 'neu': 0.563, 'pos': 0.437, 'comp...\n",
       "4         {'neg': 0.0, 'neu': 0.796, 'pos': 0.204, 'comp...\n",
       "                                ...                        \n",
       "179103    {'neg': 0.0, 'neu': 0.588, 'pos': 0.412, 'comp...\n",
       "179104    {'neg': 0.435, 'neu': 0.235, 'pos': 0.329, 'co...\n",
       "179105    {'neg': 0.25, 'neu': 0.556, 'pos': 0.194, 'com...\n",
       "179106    {'neg': 0.0, 'neu': 0.66, 'pos': 0.34, 'compou...\n",
       "179107    {'neg': 0.355, 'neu': 0.645, 'pos': 0.0, 'comp...\n",
       "Name: text, Length: 179108, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sid = SentimentIntensityAnalyzer()\n",
    "ps = lambda x:sid.polarity_scores(x)\n",
    "sentiment_scores = df.text.apply(ps)\n",
    "sentiment_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.4939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.2263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.2057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.7351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.3182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    neg    neu    pos  compound\n",
       "0  0.00  0.758  0.242    0.4939\n",
       "1  0.11  0.709  0.181    0.2263\n",
       "2  0.00  0.832  0.168    0.2057\n",
       "3  0.00  0.563  0.437    0.7351\n",
       "4  0.00  0.796  0.204    0.3182"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_df = pd.DataFrame(data = list(sentiment_scores))\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeling the scores based on the compound polarity value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>neg</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.4939</td>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.2263</td>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.2057</td>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.7351</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    neg    neu    pos  compound     label\n",
       "0  0.00  0.758  0.242    0.4939  positive\n",
       "1  0.11  0.709  0.181    0.2263  positive\n",
       "2  0.00  0.832  0.168    0.2057  positive\n",
       "3  0.00  0.563  0.437    0.7351  positive\n",
       "4  0.00  0.796  0.204    0.3182  positive"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelize = lambda x: 'neutral' if x == 0 else ('positive' if x > 0 else 'negative')\n",
    "sentiment_df['label'] = sentiment_df.compound.apply(labelize)\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Visualizing the results`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's join two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89755</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>smelled scent hand sanitizers today someone pa...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76403</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>hey yankees yankeespr mlb wouldnt made sense p...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76147</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>diane3443 wdunlap realdonaldtrump trump never ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84572</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>brookbanktv one gift give appreciation simple ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18398</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>25 july media bulletin novel coronavirusupdate...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_name        date                                               text  \\\n",
       "0      89755  2020-07-25  smelled scent hand sanitizers today someone pa...   \n",
       "1      76403  2020-07-25  hey yankees yankeespr mlb wouldnt made sense p...   \n",
       "2      76147  2020-07-25  diane3443 wdunlap realdonaldtrump trump never ...   \n",
       "3      84572  2020-07-25  brookbanktv one gift give appreciation simple ...   \n",
       "4      18398  2020-07-25  25 july media bulletin novel coronavirusupdate...   \n",
       "\n",
       "      label  \n",
       "0  positive  \n",
       "1  positive  \n",
       "2  positive  \n",
       "3  positive  \n",
       "4  positive  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.join(sentiment_df.label)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the sentiment score counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>positive</td>\n",
       "      <td>70048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "      <td>62520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>negative</td>\n",
       "      <td>46540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      label  count\n",
       "0  positive  70048\n",
       "1   neutral  62520\n",
       "2  negative  46540"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_df = data.label.value_counts().reset_index()\n",
    "counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['label', 'count'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(counts_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_palette = \"Set2\"\n",
    "sns.barplot(x='label', y='count', hue = 'count', data=counts_df, palette=color_palette)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89755</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>smelled scent hand sanitizers today someone pa...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76403</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>hey yankees yankeespr mlb wouldnt made sense p...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76147</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>diane3443 wdunlap realdonaldtrump trump never ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84572</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>brookbanktv one gift give appreciation simple ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18398</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>25 july media bulletin novel coronavirusupdate...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_name        date                                               text  \\\n",
       "0      89755  2020-07-25  smelled scent hand sanitizers today someone pa...   \n",
       "1      76403  2020-07-25  hey yankees yankeespr mlb wouldnt made sense p...   \n",
       "2      76147  2020-07-25  diane3443 wdunlap realdonaldtrump trump never ...   \n",
       "3      84572  2020-07-25  brookbanktv one gift give appreciation simple ...   \n",
       "4      18398  2020-07-25  25 july media bulletin novel coronavirusupdate...   \n",
       "\n",
       "      label  \n",
       "0  positive  \n",
       "1  positive  \n",
       "2  positive  \n",
       "3  positive  \n",
       "4  positive  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>label</th>\n",
       "      <th>user_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>negative</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>positive</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>negative</td>\n",
       "      <td>4325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>neutral</td>\n",
       "      <td>5798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     label  user_name\n",
       "0  2020-07-24  negative         84\n",
       "1  2020-07-24   neutral         96\n",
       "2  2020-07-24  positive        115\n",
       "3  2020-07-25  negative       4325\n",
       "4  2020-07-25   neutral       5798"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_agg = data[['user_name', 'date', 'label']].groupby(['date', 'label']).count().reset_index()\n",
    "data_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>label</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>negative</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>positive</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>negative</td>\n",
       "      <td>4325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>neutral</td>\n",
       "      <td>5798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     label  count\n",
       "0  2020-07-24  negative     84\n",
       "1  2020-07-24   neutral     96\n",
       "2  2020-07-24  positive    115\n",
       "3  2020-07-25  negative   4325\n",
       "4  2020-07-25   neutral   5798"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_agg.columns = ['date', 'label', 'count']\n",
    "data_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "label=negative<br>date=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "negative",
         "line": {
          "color": "#636efa",
          "dash": "solid"
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