Twitter_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Social Media Sentiment Analysis \n",
    "\n",
    "##### by Deepak Das"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![sen](https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2018/07/performing-twitter-sentiment-analysis1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "#### Dataset containing several tweets with positive and negative sentiment associated with it\n",
    "- Cyber bullying and hate speech has been a menace for quite a long time,So our objective for this task is to detect speeches tweets associated with negative sentiments.From this dataset we classify a tweet as hate speech if it has racist or sexist tweets associated with it.\n",
    "\n",
    "- So our task here is to classify racist and sexist tweets from other tweets and filter them out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![tweet](http://www.fuelaccounting.ca/wp-content/uploads/2015/04/twitter_logo-580-90.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Description\n",
    "\n",
    "- The data is in csv format.In computing, a comma-separated values (CSV) file stores tabular data (numbers and text) in plain text.Each line of the file is a data record. Each record consists of one or more fields, separated by commas. \n",
    "- Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist,our objective is to predict the labels on the given test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attribute Information\n",
    "\n",
    "- id : The id associated with the tweets in the given dataset\n",
    "- tweets : The tweets collected from various sources and having either postive or negative sentiments associated with it\n",
    "- label : A tweet with label '0' is of positive sentiment while a tweet with label '1' is of negative sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the necessary packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import string\n",
    "import nltk\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train dataset used for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('https://raw.githubusercontent.com/dD2405/Twitter_Sentiment_Analysis/master/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We make a copy of training data so that even if we have to make any changes in this dataset we would not lose the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_original=train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we see that there are a total of 31692 tweets in the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31962, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>no comment!  in #australia   #opkillingbay #se...</td>\n",
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       "      <td>0</td>\n",
       "      <td>ouch...junior is angryð#got7 #junior #yugyo...</td>\n",
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       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>retweet if you agree!</td>\n",
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       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>its #friday! ð smiles all around via ig use...</td>\n",
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       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>as we all know, essential oils are not made of...</td>\n",
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       "      <td>#euro2016 people blaming ha for conceded goal ...</td>\n",
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       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>sad little dude..   #badday #coneofshame #cats...</td>\n",
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       "      <td>0</td>\n",
       "      <td>@user all #smiles when #media is   !! ðð...</td>\n",
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       "      <th>27</th>\n",
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       "      <td>0</td>\n",
       "      <td>we had a great panel on the mediatization of t...</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>happy father's day @user ðððð</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>50 people went to nightclub to have a good nig...</td>\n",
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       "      <th>31932</th>\n",
       "      <td>31933</td>\n",
       "      <td>0</td>\n",
       "      <td>@user thanks gemma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31933</th>\n",
       "      <td>31934</td>\n",
       "      <td>1</td>\n",
       "      <td>@user judd is a  &amp;amp; #homophobic #freemilo #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31934</th>\n",
       "      <td>31935</td>\n",
       "      <td>1</td>\n",
       "      <td>lady banned from kentucky mall. @user  #jcpenn...</td>\n",
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       "    <tr>\n",
       "      <th>31935</th>\n",
       "      <td>31936</td>\n",
       "      <td>0</td>\n",
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       "      <th>31936</th>\n",
       "      <td>31937</td>\n",
       "      <td>0</td>\n",
       "      <td>want to know how to live a   life? do more thi...</td>\n",
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       "    <tr>\n",
       "      <th>31937</th>\n",
       "      <td>31938</td>\n",
       "      <td>0</td>\n",
       "      <td>love island ð</td>\n",
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       "    <tr>\n",
       "      <th>31938</th>\n",
       "      <td>31939</td>\n",
       "      <td>0</td>\n",
       "      <td>my fav actor #vijaysethupathi ! my fav actress...</td>\n",
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       "    <tr>\n",
       "      <th>31939</th>\n",
       "      <td>31940</td>\n",
       "      <td>0</td>\n",
       "      <td>whew  ð",
       " it's a productive and   #friday!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31940</th>\n",
       "      <td>31941</td>\n",
       "      <td>0</td>\n",
       "      <td>@user she's finally here! @user</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31941</th>\n",
       "      <td>31942</td>\n",
       "      <td>0</td>\n",
       "      <td>passed first year of uni #yay #love #pass #uni...</td>\n",
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       "      <th>31942</th>\n",
       "      <td>31943</td>\n",
       "      <td>0</td>\n",
       "      <td>this week is flying by   #humpday - #wednesday...</td>\n",
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       "      <th>31943</th>\n",
       "      <td>31944</td>\n",
       "      <td>0</td>\n",
       "      <td>@user modeling photoshoot this friday yay #mo...</td>\n",
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       "      <th>31944</th>\n",
       "      <td>31945</td>\n",
       "      <td>0</td>\n",
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       "      <th>31945</th>\n",
       "      <td>31946</td>\n",
       "      <td>0</td>\n",
       "      <td>feel like... ðð¶ð #dog #summer #hot #h...</td>\n",
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       "      <th>31946</th>\n",
       "      <td>31947</td>\n",
       "      <td>1</td>\n",
       "      <td>@user omfg i'm offended! i'm a  mailbox and i'...</td>\n",
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       "      <th>31947</th>\n",
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       "      <td>1</td>\n",
       "      <td>@user @user you don't have the balls to hashta...</td>\n",
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       "    <tr>\n",
       "      <th>31948</th>\n",
       "      <td>31949</td>\n",
       "      <td>1</td>\n",
       "      <td>makes you ask yourself, who am i? then am i a...</td>\n",
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       "    <tr>\n",
       "      <th>31949</th>\n",
       "      <td>31950</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>31950</th>\n",
       "      <td>31951</td>\n",
       "      <td>0</td>\n",
       "      <td>@user you can try to 'tail' us to stop, 'butt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31951</th>\n",
       "      <td>31952</td>\n",
       "      <td>0</td>\n",
       "      <td>i've just posted a new blog: #secondlife #lone...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31952</th>\n",
       "      <td>31953</td>\n",
       "      <td>0</td>\n",
       "      <td>@user you went too far with @user</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31953</th>\n",
       "      <td>31954</td>\n",
       "      <td>0</td>\n",
       "      <td>good morning #instagram #shower #water #berlin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31954</th>\n",
       "      <td>31955</td>\n",
       "      <td>0</td>\n",
       "      <td>#holiday   bull up: you will dominate your bul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31955</th>\n",
       "      <td>31956</td>\n",
       "      <td>0</td>\n",
       "      <td>less than 2 weeks ð",
       "ðð¼ð¹ððµ @us...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31956</th>\n",
       "      <td>31957</td>\n",
       "      <td>0</td>\n",
       "      <td>off fishing tomorrow @user carnt wait first ti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31957</th>\n",
       "      <td>31958</td>\n",
       "      <td>0</td>\n",
       "      <td>ate @user isz that youuu?ðððððð...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31958</th>\n",
       "      <td>31959</td>\n",
       "      <td>0</td>\n",
       "      <td>to see nina turner on the airwaves trying to...</td>\n",
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       "    <tr>\n",
       "      <th>31959</th>\n",
       "      <td>31960</td>\n",
       "      <td>0</td>\n",
       "      <td>listening to sad songs on a monday morning otw...</td>\n",
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       "    <tr>\n",
       "      <th>31960</th>\n",
       "      <td>31961</td>\n",
       "      <td>1</td>\n",
       "      <td>@user #sikh #temple vandalised in in #calgary,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31961</th>\n",
       "      <td>31962</td>\n",
       "      <td>0</td>\n",
       "      <td>thank you @user for you follow</td>\n",
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       "<p>31962 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "          id  label                                              tweet\n",
       "0          1      0   @user when a father is dysfunctional and is s...\n",
       "1          2      0  @user @user thanks for #lyft credit i can't us...\n",
       "2          3      0                                bihday your majesty\n",
       "3          4      0  #model   i love u take with u all the time in ...\n",
       "4          5      0             factsguide: society now    #motivation\n",
       "5          6      0  [2/2] huge fan fare and big talking before the...\n",
       "6          7      0   @user camping tomorrow @user @user @user @use...\n",
       "7          8      0  the next school year is the year for exams.ð...\n",
       "8          9      0  we won!!! love the land!!! #allin #cavs #champ...\n",
       "9         10      0   @user @user welcome here !  i'm   it's so #gr...\n",
       "10        11      0   â #ireland consumer price index (mom) climb...\n",
       "11        12      0  we are so selfish. #orlando #standwithorlando ...\n",
       "12        13      0  i get to see my daddy today!!   #80days #getti...\n",
       "13        14      1  @user #cnn calls #michigan middle school 'buil...\n",
       "14        15      1  no comment!  in #australia   #opkillingbay #se...\n",
       "15        16      0  ouch...junior is angryð#got7 #junior #yugyo...\n",
       "16        17      0  i am thankful for having a paner. #thankful #p...\n",
       "17        18      1                             retweet if you agree! \n",
       "18        19      0  its #friday! ð smiles all around via ig use...\n",
       "19        20      0  as we all know, essential oils are not made of...\n",
       "20        21      0  #euro2016 people blaming ha for conceded goal ...\n",
       "21        22      0  sad little dude..   #badday #coneofshame #cats...\n",
       "22        23      0  product of the day: happy man #wine tool  who'...\n",
       "23        24      1    @user @user lumpy says i am a . prove it lumpy.\n",
       "24        25      0   @user #tgif   #ff to my #gamedev #indiedev #i...\n",
       "25        26      0  beautiful sign by vendor 80 for $45.00!! #upsi...\n",
       "26        27      0   @user all #smiles when #media is   !! ðð...\n",
       "27        28      0  we had a great panel on the mediatization of t...\n",
       "28        29      0        happy father's day @user ðððð  \n",
       "29        30      0  50 people went to nightclub to have a good nig...\n",
       "...      ...    ...                                                ...\n",
       "31932  31933      0                               @user thanks gemma  \n",
       "31933  31934      1  @user judd is a  &amp; #homophobic #freemilo #...\n",
       "31934  31935      1  lady banned from kentucky mall. @user  #jcpenn...\n",
       "31935  31936      0  ugh i'm trying to enjoy my happy hour drink &a...\n",
       "31936  31937      0  want to know how to live a   life? do more thi...\n",
       "31937  31938      0                                 love island ð  \n",
       "31938  31939      0  my fav actor #vijaysethupathi ! my fav actress...\n",
       "31939  31940      0      whew  ð\n",
       " it's a productive and   #friday!!!\n",
       "31940  31941      0                 @user she's finally here! @user   \n",
       "31941  31942      0  passed first year of uni #yay #love #pass #uni...\n",
       "31942  31943      0  this week is flying by   #humpday - #wednesday...\n",
       "31943  31944      0   @user modeling photoshoot this friday yay #mo...\n",
       "31944  31945      0  you're surrounded by people who love you (even...\n",
       "31945  31946      0  feel like... ðð¶ð #dog #summer #hot #h...\n",
       "31946  31947      1  @user omfg i'm offended! i'm a  mailbox and i'...\n",
       "31947  31948      1  @user @user you don't have the balls to hashta...\n",
       "31948  31949      1   makes you ask yourself, who am i? then am i a...\n",
       "31949  31950      0  hear one of my new songs! don't go - katie ell...\n",
       "31950  31951      0   @user you can try to 'tail' us to stop, 'butt...\n",
       "31951  31952      0  i've just posted a new blog: #secondlife #lone...\n",
       "31952  31953      0                @user you went too far with @user  \n",
       "31953  31954      0  good morning #instagram #shower #water #berlin...\n",
       "31954  31955      0  #holiday   bull up: you will dominate your bul...\n",
       "31955  31956      0  less than 2 weeks ð\n",
       "ðð¼ð¹ððµ @us...\n",
       "31956  31957      0  off fishing tomorrow @user carnt wait first ti...\n",
       "31957  31958      0  ate @user isz that youuu?ðððððð...\n",
       "31958  31959      0    to see nina turner on the airwaves trying to...\n",
       "31959  31960      0  listening to sad songs on a monday morning otw...\n",
       "31960  31961      1  @user #sikh #temple vandalised in in #calgary,...\n",
       "31961  31962      0                   thank you @user for you follow  \n",
       "\n",
       "[31962 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_original"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test dataset used for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('https://raw.githubusercontent.com/dD2405/Twitter_Sentiment_Analysis/master/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We make a copy of test data so that even if we have to make any changes in this dataset we would not lose the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_original=test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we see that there are a total of 17197 tweets in the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17197, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31963</td>\n",
       "      <td>#studiolife #aislife #requires #passion #dedic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31964</td>\n",
       "      <td>@user #white #supremacists want everyone to s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31965</td>\n",
       "      <td>safe ways to heal your #acne!!    #altwaystohe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31966</td>\n",
       "      <td>is the hp and the cursed child book up for res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31967</td>\n",
       "      <td>3rd #bihday to my amazing, hilarious #nephew...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31968</td>\n",
       "      <td>choose to be   :) #momtips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31969</td>\n",
       "      <td>something inside me dies ð¦ð¿â¨  eyes nes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31970</td>\n",
       "      <td>#finished#tattoo#inked#ink#loveitâ¤ï¸ #â¤ï¸...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31971</td>\n",
       "      <td>@user @user @user i will never understand why...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31972</td>\n",
       "      <td>#delicious   #food #lovelife #capetown mannaep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31973</td>\n",
       "      <td>1000dayswasted - narcosis infinite ep.. make m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31974</td>\n",
       "      <td>one of the world's greatest spoing events   #l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31975</td>\n",
       "      <td>half way through the website now and #allgoing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31976</td>\n",
       "      <td>good food, good life , #enjoy and   ððð...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31977</td>\n",
       "      <td>i'll stand behind this #guncontrolplease   #se...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31978</td>\n",
       "      <td>i ate,i ate and i ate...ðð   #jamaisasth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>31979</td>\n",
       "      <td>@user got my @user limited edition rain or sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>31980</td>\n",
       "      <td>&amp;amp; #love &amp;amp; #hugs &amp;amp; #kisses too! how...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31981</td>\n",
       "      <td>ð­ðð #girls   #sun #fave @ london, uni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31982</td>\n",
       "      <td>thought factory: bbc neutrality on right wing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31983</td>\n",
       "      <td>hey guys tommorow is the last day of my exams ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31984</td>\n",
       "      <td>@user @user  @user  #levyrroni #recuerdos mem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>31985</td>\n",
       "      <td>my mind is like ððð½ð but my body l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>31986</td>\n",
       "      <td>never been this down on myself in my entire li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31987</td>\n",
       "      <td>check  twitterww - trends: \"trending worldwide...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31988</td>\n",
       "      <td>i thought i saw a mermaid!!! #ceegee  #smcr   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31989</td>\n",
       "      <td>chick gets fucked hottest naked lady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31990</td>\n",
       "      <td>happy bday lucyâ¨â¨ð xoxo #love #beautifu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31991</td>\n",
       "      <td>haroldfriday have a weekend filled with sunbe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31992</td>\n",
       "      <td>@user @user tried that! but nothing - will try...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17167</th>\n",
       "      <td>49130</td>\n",
       "      <td>people do anything for fucking attention nowad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17168</th>\n",
       "      <td>49131</td>\n",
       "      <td>creative bubble got burst ð¢ looking forward...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17169</th>\n",
       "      <td>49132</td>\n",
       "      <td>tomorrow is gonna be a big day! we are going t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17170</th>\n",
       "      <td>49133</td>\n",
       "      <td>i am thankful for baby giggles. #thankful #pos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17171</th>\n",
       "      <td>49134</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17172</th>\n",
       "      <td>49135</td>\n",
       "      <td>in life u will grow to learn some pple will wo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17173</th>\n",
       "      <td>49136</td>\n",
       "      <td>ði was the storm,you were the rain. togethe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17174</th>\n",
       "      <td>49137</td>\n",
       "      <td>lovelgq -  broken ep via   #rnb #love #heabrok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17175</th>\n",
       "      <td>49138</td>\n",
       "      <td>spread love not hateâ¤ï¸ðððð #pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17176</th>\n",
       "      <td>49139</td>\n",
       "      <td>@user @user are the most racist pay ever!!!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17177</th>\n",
       "      <td>49140</td>\n",
       "      <td>i am thankful for children. #thankful #positiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17178</th>\n",
       "      <td>49141</td>\n",
       "      <td>liverpool â¤ï¸ð¬ð§ #walk #liverpool #sta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17179</th>\n",
       "      <td>49142</td>\n",
       "      <td>#bakersfield   rooster simulation: i want to c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17180</th>\n",
       "      <td>49143</td>\n",
       "      <td>por do sol ó¾â¤ï¸#instagood #beautiful   #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17181</th>\n",
       "      <td>49144</td>\n",
       "      <td>@user hell yeah what a great surprise for your...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17182</th>\n",
       "      <td>49145</td>\n",
       "      <td>when ur the joke ur defensive towards everythi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17183</th>\n",
       "      <td>49146</td>\n",
       "      <td>#enjoying the #evening #sun in my #bedroom â¨...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17184</th>\n",
       "      <td>49147</td>\n",
       "      <td>tonight on @user from 9pm gmt  you can here a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17185</th>\n",
       "      <td>49148</td>\n",
       "      <td>today is a good day for excercise #imready #so...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17186</th>\n",
       "      <td>49149</td>\n",
       "      <td>good night with a tea and music âï¸ðð...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17187</th>\n",
       "      <td>49150</td>\n",
       "      <td>loving lifeðºð¸âï¸ð  #createyourfutu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17188</th>\n",
       "      <td>49151</td>\n",
       "      <td>black professor demonizes, proposes nazi style...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17189</th>\n",
       "      <td>49152</td>\n",
       "      <td>learn how to think positive.  #positive   #ins...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17190</th>\n",
       "      <td>49153</td>\n",
       "      <td>we love the pretty, happy and fresh you! #teen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17191</th>\n",
       "      <td>49154</td>\n",
       "      <td>2_damn_tuff-ruff_muff__techno_city-(ng005)-web...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17192</th>\n",
       "      <td>49155</td>\n",
       "      <td>thought factory: left-right polarisation! #tru...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17193</th>\n",
       "      <td>49156</td>\n",
       "      <td>feeling like a mermaid ð #hairflip #neverre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17194</th>\n",
       "      <td>49157</td>\n",
       "      <td>#hillary #campaigned today in #ohio((omg)) &amp;am...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17195</th>\n",
       "      <td>49158</td>\n",
       "      <td>happy, at work conference: right mindset leads...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17196</th>\n",
       "      <td>49159</td>\n",
       "      <td>my   song \"so glad\" free download!  #shoegaze ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17197 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                              tweet\n",
       "0      31963  #studiolife #aislife #requires #passion #dedic...\n",
       "1      31964   @user #white #supremacists want everyone to s...\n",
       "2      31965  safe ways to heal your #acne!!    #altwaystohe...\n",
       "3      31966  is the hp and the cursed child book up for res...\n",
       "4      31967    3rd #bihday to my amazing, hilarious #nephew...\n",
       "5      31968                        choose to be   :) #momtips \n",
       "6      31969  something inside me dies ð¦ð¿â¨  eyes nes...\n",
       "7      31970  #finished#tattoo#inked#ink#loveitâ¤ï¸ #â¤ï¸...\n",
       "8      31971   @user @user @user i will never understand why...\n",
       "9      31972  #delicious   #food #lovelife #capetown mannaep...\n",
       "10     31973  1000dayswasted - narcosis infinite ep.. make m...\n",
       "11     31974  one of the world's greatest spoing events   #l...\n",
       "12     31975  half way through the website now and #allgoing...\n",
       "13     31976  good food, good life , #enjoy and   ððð...\n",
       "14     31977  i'll stand behind this #guncontrolplease   #se...\n",
       "15     31978  i ate,i ate and i ate...ðð   #jamaisasth...\n",
       "16     31979   @user got my @user limited edition rain or sh...\n",
       "17     31980  &amp; #love &amp; #hugs &amp; #kisses too! how...\n",
       "18     31981  ð­ðð #girls   #sun #fave @ london, uni...\n",
       "19     31982  thought factory: bbc neutrality on right wing ...\n",
       "20     31983  hey guys tommorow is the last day of my exams ...\n",
       "21     31984   @user @user  @user  #levyrroni #recuerdos mem...\n",
       "22     31985  my mind is like ððð½ð but my body l...\n",
       "23     31986  never been this down on myself in my entire li...\n",
       "24     31987  check  twitterww - trends: \"trending worldwide...\n",
       "25     31988  i thought i saw a mermaid!!! #ceegee  #smcr   ...\n",
       "26     31989              chick gets fucked hottest naked lady \n",
       "27     31990  happy bday lucyâ¨â¨ð xoxo #love #beautifu...\n",
       "28     31991   haroldfriday have a weekend filled with sunbe...\n",
       "29     31992  @user @user tried that! but nothing - will try...\n",
       "...      ...                                                ...\n",
       "17167  49130  people do anything for fucking attention nowad...\n",
       "17168  49131  creative bubble got burst ð¢ looking forward...\n",
       "17169  49132  tomorrow is gonna be a big day! we are going t...\n",
       "17170  49133  i am thankful for baby giggles. #thankful #pos...\n",
       "17171  49134  #model   i love u take with u all the time in ...\n",
       "17172  49135  in life u will grow to learn some pple will wo...\n",
       "17173  49136  ði was the storm,you were the rain. togethe...\n",
       "17174  49137  lovelgq -  broken ep via   #rnb #love #heabrok...\n",
       "17175  49138  spread love not hateâ¤ï¸ðððð #pr...\n",
       "17176  49139     @user @user are the most racist pay ever!!!!! \n",
       "17177  49140  i am thankful for children. #thankful #positiv...\n",
       "17178  49141  liverpool â¤ï¸ð¬ð§ #walk #liverpool #sta...\n",
       "17179  49142  #bakersfield   rooster simulation: i want to c...\n",
       "17180  49143  por do sol ó¾â¤ï¸#instagood #beautiful   #...\n",
       "17181  49144  @user hell yeah what a great surprise for your...\n",
       "17182  49145  when ur the joke ur defensive towards everythi...\n",
       "17183  49146  #enjoying the #evening #sun in my #bedroom â¨...\n",
       "17184  49147  tonight on @user from 9pm gmt  you can here a ...\n",
       "17185  49148  today is a good day for excercise #imready #so...\n",
       "17186  49149  good night with a tea and music âï¸ðð...\n",
       "17187  49150  loving lifeðºð¸âï¸ð  #createyourfutu...\n",
       "17188  49151  black professor demonizes, proposes nazi style...\n",
       "17189  49152  learn how to think positive.  #positive   #ins...\n",
       "17190  49153  we love the pretty, happy and fresh you! #teen...\n",
       "17191  49154  2_damn_tuff-ruff_muff__techno_city-(ng005)-web...\n",
       "17192  49155  thought factory: left-right polarisation! #tru...\n",
       "17193  49156  feeling like a mermaid ð #hairflip #neverre...\n",
       "17194  49157  #hillary #campaigned today in #ohio((omg)) &am...\n",
       "17195  49158  happy, at work conference: right mindset leads...\n",
       "17196  49159  my   song \"so glad\" free download!  #shoegaze ...\n",
       "\n",
       "[17197 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_original"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We combine Train and Test datasets for pre-processing stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine = train.append(test,ignore_index=True,sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...\n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...\n",
       "2   3    0.0                                bihday your majesty\n",
       "3   4    0.0  #model   i love u take with u all the time in ...\n",
       "4   5    0.0             factsguide: society now    #motivation"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
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       "      <th>49156</th>\n",
       "      <td>49157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#hillary #campaigned today in #ohio((omg)) &amp;am...</td>\n",
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       "    <tr>\n",
       "      <th>49157</th>\n",
       "      <td>49158</td>\n",
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       "      <td>happy, at work conference: right mindset leads...</td>\n",
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       "      <th>49158</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>my   song \"so glad\" free download!  #shoegaze ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  label                                              tweet\n",
       "49154  49155    NaN  thought factory: left-right polarisation! #tru...\n",
       "49155  49156    NaN  feeling like a mermaid ð #hairflip #neverre...\n",
       "49156  49157    NaN  #hillary #campaigned today in #ohio((omg)) &am...\n",
       "49157  49158    NaN  happy, at work conference: right mindset leads...\n",
       "49158  49159    NaN  my   song \"so glad\" free download!  #shoegaze ..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pre](https://www.electronicsmedia.info/wp-content/uploads/2017/12/Data-Preprocessing.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Removing Twitter Handles (@user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given below is a user-defined function to remove unwanted text patterns from the tweets. It takes two arguments, one is the original string of text and the other is the pattern of text that we want to remove from the string. The function returns the same input string but without the given pattern. We will use this function to remove the pattern ‘@user’ from all the tweets in our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_pattern(text,pattern):\n",
    "    \n",
    "    # re.findall() finds the pattern i.e @user and puts it in a list for further task\n",
    "    r = re.findall(pattern,text)\n",
    "    \n",
    "    # re.sub() removes @user from the sentences in the dataset\n",
    "    for i in r:\n",
    "        text = re.sub(i,\"\",text)\n",
    "    \n",
    "    return text\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>Tidy_Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>when a father is dysfunctional and is so sel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks for #lyft credit i can't use cause th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                         Tidy_Tweets  \n",
       "0    when a father is dysfunctional and is so sel...  \n",
       "1    thanks for #lyft credit i can't use cause th...  \n",
       "2                                bihday your majesty  \n",
       "3  #model   i love u take with u all the time in ...  \n",
       "4             factsguide: society now    #motivation  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine['Tidy_Tweets'] = np.vectorize(remove_pattern)(combine['tweet'], \"@[\\w]*\")\n",
    "\n",
    "combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Punctuations, Numbers, and Special Characters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Punctuations, numbers and special characters do not help much. It is better to remove them from the text just as we removed the twitter handles. Here we will replace everything except characters and hashtags with spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine['Tidy_Tweets'] = combine['Tidy_Tweets'].str.replace(\"[^a-zA-Z#]\", \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>when a father is dysfunctional and is so sel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks for #lyft credit i can t use cause th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide  society now    #motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[2/2] huge fan fare and big talking before the...</td>\n",
       "      <td>huge fan fare and big talking before the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user camping tomorrow @user @user @user @use...</td>\n",
       "      <td>camping tomorrow        danny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>the next school year is the year for exams.ð...</td>\n",
       "      <td>the next school year is the year for exams    ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>we won!!! love the land!!! #allin #cavs #champ...</td>\n",
       "      <td>we won    love the land    #allin #cavs #champ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user welcome here !  i'm   it's so #gr...</td>\n",
       "      <td>welcome here    i m   it s so #gr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "5   6    0.0  [2/2] huge fan fare and big talking before the...   \n",
       "6   7    0.0   @user camping tomorrow @user @user @user @use...   \n",
       "7   8    0.0  the next school year is the year for exams.ð...   \n",
       "8   9    0.0  we won!!! love the land!!! #allin #cavs #champ...   \n",
       "9  10    0.0   @user @user welcome here !  i'm   it's so #gr...   \n",
       "\n",
       "                                         Tidy_Tweets  \n",
       "0    when a father is dysfunctional and is so sel...  \n",
       "1    thanks for #lyft credit i can t use cause th...  \n",
       "2                                bihday your majesty  \n",
       "3  #model   i love u take with u all the time in ...  \n",
       "4             factsguide  society now    #motivation  \n",
       "5        huge fan fare and big talking before the...  \n",
       "6                   camping tomorrow        danny     \n",
       "7  the next school year is the year for exams    ...  \n",
       "8  we won    love the land    #allin #cavs #champ...  \n",
       "9              welcome here    i m   it s so #gr      "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Short Words\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to be a little careful here in selecting the length of the words which we want to remove. So, I have decided to remove all the words having length 3 or less. For example, terms like “hmm”, “oh” are of very little use. It is better to get rid of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>tweet</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>when father dysfunctional selfish drags kids i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause they offer wheelchai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model love take with time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[2/2] huge fan fare and big talking before the...</td>\n",
       "      <td>huge fare talking before they leave chaos disp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user camping tomorrow @user @user @user @use...</td>\n",
       "      <td>camping tomorrow danny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>the next school year is the year for exams.ð...</td>\n",
       "      <td>next school year year exams think about that #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>we won!!! love the land!!! #allin #cavs #champ...</td>\n",
       "      <td>love land #allin #cavs #champions #cleveland #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user welcome here !  i'm   it's so #gr...</td>\n",
       "      <td>welcome here</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "5   6    0.0  [2/2] huge fan fare and big talking before the...   \n",
       "6   7    0.0   @user camping tomorrow @user @user @user @use...   \n",
       "7   8    0.0  the next school year is the year for exams.ð...   \n",
       "8   9    0.0  we won!!! love the land!!! #allin #cavs #champ...   \n",
       "9  10    0.0   @user @user welcome here !  i'm   it's so #gr...   \n",
       "\n",
       "                                         Tidy_Tweets  \n",
       "0  when father dysfunctional selfish drags kids i...  \n",
       "1  thanks #lyft credit cause they offer wheelchai...  \n",
       "2                                bihday your majesty  \n",
       "3                         #model love take with time  \n",
       "4                     factsguide society #motivation  \n",
       "5  huge fare talking before they leave chaos disp...  \n",
       "6                             camping tomorrow danny  \n",
       "7  next school year year exams think about that #...  \n",
       "8  love land #allin #cavs #champions #cleveland #...  \n",
       "9                                       welcome here  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine['Tidy_Tweets'] = combine['Tidy_Tweets'].apply(lambda x: ' '.join([w for w in x.split() if len(w)>3]))\n",
    "\n",
    "combine.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Tokenization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will tokenize all the cleaned tweets in our dataset. Tokens are individual terms or words, and tokenization is the process of splitting a string of text into tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [when, father, dysfunctional, selfish, drags, ...\n",
       "1    [thanks, #lyft, credit, cause, they, offer, wh...\n",
       "2                              [bihday, your, majesty]\n",
       "3                     [#model, love, take, with, time]\n",
       "4                   [factsguide, society, #motivation]\n",
       "Name: Tidy_Tweets, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_tweet = combine['Tidy_Tweets'].apply(lambda x: x.split())\n",
    "tokenized_tweet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stemming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stemming is a rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word. For example, For example – “play”, “player”, “played”, “plays” and “playing” are the different variations of the word – “play”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [when, father, dysfunct, selfish, drag, kid, i...\n",
       "1    [thank, #lyft, credit, caus, they, offer, whee...\n",
       "2                              [bihday, your, majesti]\n",
       "3                     [#model, love, take, with, time]\n",
       "4                         [factsguid, societi, #motiv]\n",
       "Name: Tidy_Tweets, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk import PorterStemmer\n",
    "\n",
    "ps = PorterStemmer()\n",
    "\n",
    "tokenized_tweet = tokenized_tweet.apply(lambda x: [ps.stem(i) for i in x])\n",
    "\n",
    "tokenized_tweet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let’s stitch these tokens back together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>Tidy_Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>when father dysfunct selfish drag kid into dys...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thank #lyft credit caus they offer wheelchair ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday your majesti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model love take with time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguid societi #motiv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                         Tidy_Tweets  \n",
       "0  when father dysfunct selfish drag kid into dys...  \n",
       "1  thank #lyft credit caus they offer wheelchair ...  \n",
       "2                                bihday your majesti  \n",
       "3                         #model love take with time  \n",
       "4                           factsguid societi #motiv  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(tokenized_tweet)):\n",
    "    tokenized_tweet[i] = ' '.join(tokenized_tweet[i])\n",
    "\n",
    "combine['Tidy_Tweets'] = tokenized_tweet\n",
    "combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization from Tweets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![vis](https://previews.123rf.com/images/mindscanner/mindscanner1404/mindscanner140401428/27857012-word-cloud-with-nlp-related-tags.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![wc](https://az158878.vo.msecnd.net/marketing/product/42949674199/f89c7e08-56ac-457d-ae87-b6165e51432f/screen2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A wordcloud is a visualization wherein the most frequent words appear in large size and the less frequent words appear in smaller sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Packages necessary for generating a WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud,ImageColorGenerator\n",
    "from PIL import Image\n",
    "import urllib\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Store all the words from the dataset which are non-racist/sexist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words_positive = ' '.join(text for text in combine['Tidy_Tweets'][combine['label']==0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see most of the words are positive or neutral. With happy, smile, and love being the most frequent ones. Hence, most of the frequent words are compatible with the sentiment which is non racist/sexists tweets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combining the image with the dataset\n",
    "Mask = np.array(Image.open(requests.get('http://clipart-library.com/image_gallery2/Twitter-PNG-Image.png', stream=True).raw))\n",
    "\n",
    "# We use the ImageColorGenerator library from Wordcloud \n",
    "# Here we take the color of the image and impose it over our wordcloud\n",
    "image_colors = ImageColorGenerator(Mask)\n",
    "\n",
    "# Now we use the WordCloud function from the wordcloud library \n",
    "wc = WordCloud(background_color='black', height=1500, width=4000,mask=Mask).generate(all_words_positive)\n",
    "\n",
    "# Size of the image generated \n",
    "plt.figure(figsize=(10,20))\n",
    "\n",
    "# Here we recolor the words from the dataset to the image's color\n",
    "# recolor just recolors the default colors to the image's blue color\n",
    "# interpolation is used to smooth the image generated \n",
    "plt.imshow(wc.recolor(color_func=image_colors),interpolation=\"hamming\")\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Store all the words from the dataset which are racist/sexist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words_negative = ' '.join(text for text in combine['Tidy_Tweets'][combine['label']==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can clearly see, most of the words have negative connotations. So, it seems we have a pretty good text data to work on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combining the image with the dataset\n",
    "Mask = np.array(Image.open(requests.get('http://clipart-library.com/image_gallery2/Twitter-PNG-Image.png', stream=True).raw))\n",
    "\n",
    "# We use the ImageColorGenerator library from Wordcloud \n",
    "# Here we take the color of the image and impose it over our wordcloud\n",
    "image_colors = ImageColorGenerator(Mask)\n",
    "\n",
    "# Now we use the WordCloud function from the wordcloud library \n",
    "wc = WordCloud(background_color='black', height=1500, width=4000,mask=Mask).generate(all_words_negative)\n",
    "\n",
    "# Size of the image generated \n",
    "plt.figure(figsize=(10,20))\n",
    "\n",
    "# Here we recolor the words from the dataset to the image's color\n",
    "# recolor just recolors the default colors to the image's blue color\n",
    "# interpolation is used to smooth the image generated \n",
    "plt.imshow(wc.recolor(color_func=image_colors),interpolation=\"gaussian\")\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the impact of Hashtags on tweets sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![hash](https://www.socialtalent.com/wp-content/uploads/2015/07/Twitter-Logo-Hashtag.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to extract hashtags from tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Hashtags_Extract(x):\n",
    "    hashtags=[]\n",
    "    \n",
    "    # Loop over the words in the tweet\n",
    "    for i in x:\n",
    "        ht = re.findall(r'#(\\w+)',i)\n",
    "        hashtags.append(ht)\n",
    "    \n",
    "    return hashtags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A nested list of all the hashtags from the positive reviews from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ht_positive = Hashtags_Extract(combine['Tidy_Tweets'][combine['label']==0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we unnest the list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ht_positive_unnest = sum(ht_positive,[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A nested list of all the hashtags from the negative reviews from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ht_negative = Hashtags_Extract(combine['Tidy_Tweets'][combine['label']==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we unnest the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ht_negative_unnest = sum(ht_negative,[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting BarPlots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![plot](https://www.mathworks.com/help/examples/graphics/win64/SingleDataSeriesExample_01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Positive Tweets in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting the frequency of the words having Positive Sentiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'love': 1654, 'posit': 917, 'smile': 676, 'healthi': 573, 'thank': 534, 'fun': 463, 'life': 425, 'affirm': 423, 'summer': 390, 'model': 375, ...})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_positive = nltk.FreqDist(ht_positive_unnest)\n",
    "\n",
    "word_freq_positive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a dataframe for the most frequently used words in hashtags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_positive = pd.DataFrame({'Hashtags':list(word_freq_positive.keys()),'Count':list(word_freq_positive.values())})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Hashtags</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>run</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lyft</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>disapoint</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>getthank</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>model</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>motiv</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>allshowandnogo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>school</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>exam</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hate</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Hashtags  Count\n",
       "0             run     72\n",
       "1            lyft      2\n",
       "2       disapoint      1\n",
       "3        getthank      2\n",
       "4           model    375\n",
       "5           motiv    202\n",
       "6  allshowandnogo      1\n",
       "7          school     30\n",
       "8            exam      9\n",
       "9            hate     27"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_positive.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the barplot for the 10 most frequent words used for hashtags "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_positive_plot = df_positive.nlargest(20,columns='Count') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df_positive_plot,y='Hashtags',x='Count')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Negative Tweets in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting the frequency of the words having Negative Sentiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_freq_negative = nltk.FreqDist(ht_negative_unnest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'trump': 136, 'polit': 95, 'allahsoil': 92, 'liber': 81, 'libtard': 77, 'sjw': 75, 'retweet': 63, 'black': 46, 'miami': 46, 'hate': 37, ...})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a dataframe for the most frequently used words in hashtags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_negative = pd.DataFrame({'Hashtags':list(word_freq_negative.keys()),'Count':list(word_freq_negative.values())})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Hashtags</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cnn</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>michigan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tcot</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>opkillingbay</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>seashepherd</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>helpcovedolphin</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>thecov</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>neverump</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>xenophobia</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Hashtags  Count\n",
       "0              cnn     10\n",
       "1         michigan      2\n",
       "2             tcot     14\n",
       "3        australia      6\n",
       "4     opkillingbay      5\n",
       "5      seashepherd     22\n",
       "6  helpcovedolphin      3\n",
       "7           thecov      4\n",
       "8         neverump      8\n",
       "9       xenophobia     12"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_negative.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the barplot for the 10 most frequent words used for hashtags "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_negative_plot = df_negative.nlargest(20,columns='Count') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df_negative_plot,y='Hashtags',x='Count')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting Features from cleaned Tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag-of-Words Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bag of Words is a method to extract features from text documents. These features can be used for training machine learning algorithms. It creates a vocabulary of all the unique words occurring in all the documents in the training set. \n",
    "\n",
    "Consider a corpus (a collection of texts) called C of D documents {d1,d2…..dD} and N unique tokens extracted out of the corpus C. The N tokens (words) will form a list, and the size of the bag-of-words matrix M will be given by D X N. Each row in the matrix M contains the frequency of tokens in document D(i).\n",
    "\n",
    "For example, if you have 2 documents-\n",
    "\n",
    "\n",
    "\n",
    "- D1: He is a lazy boy. She is also lazy.\n",
    "\n",
    "- D2: Smith is a lazy person.\n",
    "\n",
    "First, it creates a vocabulary using unique words from all the documents\n",
    "#### [‘He’ , ’She’ , ’lazy’ , 'boy’ ,  'Smith’  , ’person’] \n",
    "\n",
    "- Here, D=2, N=6\n",
    "\n",
    "\n",
    "\n",
    "- The matrix M of size 2 X 6 will be represented as:\n",
    "\n",
    "![bow](https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2018/07/table.png)\n",
    "\n",
    "The above table depicts the training features containing term frequencies of each word in each document. This is called bag-of-words approach since the number of occurrence and not sequence or order of words matters in this approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49129</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49130</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49131</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49132</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49133</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49135</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49137</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49138</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49139</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49140</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49141</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49142</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49143</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49144</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49145</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49146</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49147</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49148</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>49159 rows × 1000 columns</p>\n",
       "</div>"
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       "\n",
       "[49159 rows x 1000 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "bow_vectorizer = CountVectorizer(max_df=0.90, min_df=2, max_features=1000, stop_words='english')\n",
    "\n",
    "# bag-of-words feature matrix\n",
    "bow = bow_vectorizer.fit_transform(combine['Tidy_Tweets'])\n",
    "\n",
    "df_bow = pd.DataFrame(bow.todense())\n",
    "\n",
    "df_bow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. \n",
    "\n",
    "Typically, the tf-idf weight is composed by two terms: the first computes the normalized Term Frequency (TF), aka. the number of times a word appears in a document, divided by the total number of words in that document; the second term is the Inverse Document Frequency (IDF), computed as the logarithm of the number of the documents in the corpus divided by the number of documents where the specific term appears.\n",
    "\n",
    "- TF: Term Frequency, which measures how frequently a term occurs in a document. Since every document is different in length, it is possible that a term would appear much more times in long documents than shorter ones. Thus, the term frequency is often divided by the document length (aka. the total number of terms in the document) as a way of normalization: \n",
    "#### TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).\n",
    "\n",
    "- IDF: Inverse Document Frequency, which measures how important a term is. While computing TF, all terms are considered equally important. However it is known that certain terms, such as \"is\", \"of\", and \"that\", may appear a lot of times but have little importance. Thus we need to weigh down the frequent terms while scale up the rare ones, by computing the following: \n",
    "#### IDF(t) = log_e(Total number of documents / Number of documents with term t in it).\n",
    "\n",
    "#### Example:\n",
    "\n",
    "Consider a document containing 100 words wherein the word cat appears 3 times. The term frequency (i.e., tf) for cat is then (3 / 100) = 0.03. Now, assume we have 10 million documents and the word cat appears in one thousand of these. Then, the inverse document frequency (i.e., idf) is calculated as log(10,000,000 / 1,000) = 4. Thus, the Tf-idf weight is the product of these quantities: 0.03 * 4 = 0.12.\n",
    "\n",
    "![tfidf](https://skymind.ai/images/wiki/tfidf.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.342695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49129</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49130</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49131</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49132</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49133</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49134</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49135</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49136</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49137</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49138</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49139</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49140</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "<p>49159 rows × 1000 columns</p>\n",
       "</div>"
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       "29     0.0  0.0  0.0  0.0  0.0  0.342695  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "...    ...  ...  ...  ...  ...       ...  ...  ...  ...  ...  ...  ...   \n",
       "49129  0.0  0.0  0.0  0.0  0.0  0.000000  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "49130  0.0  0.0  0.0  0.0  0.0  0.000000  0.0  0.0  0.0  0.0  ...  0.0   \n",
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       "\n",
       "            991       992  993  994  995  996  997  998  999  \n",
       "0      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "4      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "7      0.000000  0.403826  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "8      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "9      0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "11     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "12     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "13     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "14     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "15     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "16     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "17     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "18     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "19     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "21     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "22     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "23     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "24     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "25     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "26     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "27     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "28     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "29     0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...         ...       ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "49129  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49130  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49131  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49132  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49133  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49134  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49135  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49136  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49137  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49138  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49139  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49140  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49141  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49142  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49143  0.351966  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49144  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49145  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49146  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49147  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49148  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49149  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49150  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49151  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49152  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49153  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49154  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49155  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49156  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49157  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "49158  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[49159 rows x 1000 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf=TfidfVectorizer(max_df=0.90, min_df=2,max_features=1000,stop_words='english')\n",
    "\n",
    "tfidf_matrix=tfidf.fit_transform(combine['Tidy_Tweets'])\n",
    "\n",
    "df_tfidf = pd.DataFrame(tfidf_matrix.todense())\n",
    "\n",
    "df_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Machine Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ml](https://ak8.picdn.net/shutterstock/videos/23516428/thumb/12.jpg?i10c=img.resize(height:160))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the features from Bag-of-Words Model for training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_bow = bow[:31962]\n",
    "\n",
    "train_bow.todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using features from TF-IDF for training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_tfidf_matrix = tfidf_matrix[:31962]\n",
    "\n",
    "train_tfidf_matrix.todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bag-of-Words Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_bow,x_valid_bow,y_train_bow,y_valid_bow = train_test_split(train_bow,train['label'],test_size=0.3,random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using TF-IDF features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_tfidf,x_valid_tfidf,y_train_tfidf,y_valid_tfidf = train_test_split(train_tfidf_matrix,train['label'],test_size=0.3,random_state=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Log_Reg = LogisticRegression(random_state=0,solver='lbfgs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Bag-of-Words Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=0, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the Logistic Regression Model\n",
    "\n",
    "Log_Reg.fit(x_train_bow,y_train_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.86501156e-01, 1.34988440e-02],\n",
       "       [9.99599096e-01, 4.00904144e-04],\n",
       "       [9.13577383e-01, 8.64226167e-02],\n",
       "       ...,\n",
       "       [8.95457155e-01, 1.04542845e-01],\n",
       "       [9.59736065e-01, 4.02639345e-02],\n",
       "       [9.67541420e-01, 3.24585797e-02]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first part of the list is predicting probabilities for label:0 \n",
    "# and the second part of the list is predicting probabilities for label:1\n",
    "prediction_bow = Log_Reg.predict_proba(x_valid_bow)\n",
    "\n",
    "prediction_bow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5721352019785655"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if prediction is greater than or equal to 0.3 than 1 else 0\n",
    "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\n",
    "prediction_int = prediction_bow[:,1]>=0.3\n",
    "\n",
    "prediction_int = prediction_int.astype(np.int)\n",
    "prediction_int\n",
    "\n",
    "# calculating f1 score\n",
    "log_bow = f1_score(y_valid_bow, prediction_int)\n",
    "\n",
    "log_bow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using TF-IDF Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=0, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Log_Reg.fit(x_train_tfidf,y_train_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98487907, 0.01512093],\n",
       "       [0.97949889, 0.02050111],\n",
       "       [0.9419737 , 0.0580263 ],\n",
       "       ...,\n",
       "       [0.98630906, 0.01369094],\n",
       "       [0.96746188, 0.03253812],\n",
       "       [0.99055287, 0.00944713]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_tfidf = Log_Reg.predict_proba(x_valid_tfidf)\n",
    "\n",
    "prediction_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5862068965517241"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_int = prediction_tfidf[:,1]>=0.3\n",
    "\n",
    "prediction_int = prediction_int.astype(np.int)\n",
    "prediction_int\n",
    "\n",
    "# calculating f1 score\n",
    "log_tfidf = f1_score(y_valid_tfidf, prediction_int)\n",
    "\n",
    "log_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Bag-of-Words Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_bow = XGBClassifier(random_state=22,learning_rate=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.9, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic',\n",
       "       random_state=22, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_bow.fit(x_train_bow, y_train_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9717447 , 0.02825526],\n",
       "       [0.99767685, 0.00232312],\n",
       "       [0.9436968 , 0.05630319],\n",
       "       ...,\n",
       "       [0.9660848 , 0.03391522],\n",
       "       [0.9436968 , 0.05630319],\n",
       "       [0.9436968 , 0.05630319]], dtype=float32)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first part of the list is predicting probabilities for label:0 \n",
    "# and the second part of the list is predicting probabilities for label:1\n",
    "xgb=model_bow.predict_proba(x_valid_bow)\n",
    "\n",
    "xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5712012728719172"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if prediction is greater than or equal to 0.3 than 1 else 0\n",
    "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\n",
    "xgb=xgb[:,1]>=0.3\n",
    "\n",
    "# converting the results to integer type\n",
    "xgb_int=xgb.astype(np.int)\n",
    "\n",
    "# calculating f1 score\n",
    "xgb_bow=f1_score(y_valid_bow,xgb_int)\n",
    "\n",
    "xgb_bow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using TF-IDF Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_tfidf=XGBClassifier(random_state=29,learning_rate=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.7, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic',\n",
       "       random_state=29, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tfidf.fit(x_train_tfidf, y_train_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9905173 , 0.00948265],\n",
       "       [0.9902541 , 0.00974591],\n",
       "       [0.9579129 , 0.0420871 ],\n",
       "       ...,\n",
       "       [0.9883729 , 0.0116271 ],\n",
       "       [0.9878232 , 0.0121768 ],\n",
       "       [0.9807036 , 0.01929642]], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first part of the list is predicting probabilities for label:0 \n",
    "# and the second part of the list is predicting probabilities for label:1\n",
    "xgb_tfidf=model_tfidf.predict_proba(x_valid_tfidf)\n",
    "\n",
    "xgb_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5657051282051281"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if prediction is greater than or equal to 0.3 than 1 else 0\n",
    "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\n",
    "xgb_tfidf=xgb_tfidf[:,1]>=0.3\n",
    "\n",
    "# converting the results to integer type\n",
    "xgb_int_tfidf=xgb_tfidf.astype(np.int)\n",
    "\n",
    "# calculating f1 score\n",
    "score=f1_score(y_valid_tfidf,xgb_int_tfidf)\n",
    "\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dct = DecisionTreeClassifier(criterion='entropy', random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Bag-of-Words Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct.fit(x_train_bow,y_train_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       ...,\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct_bow = dct.predict_proba(x_valid_bow)\n",
    "\n",
    "dct_bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5141776937618148"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if prediction is greater than or equal to 0.3 than 1 else 0\n",
    "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\n",
    "dct_bow=dct_bow[:,1]>=0.3\n",
    "\n",
    "# converting the results to integer type\n",
    "dct_int_bow=dct_bow.astype(np.int)\n",
    "\n",
    "# calculating f1 score\n",
    "dct_score_bow=f1_score(y_valid_bow,dct_int_bow)\n",
    "\n",
    "dct_score_bow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using TF-IDF Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct.fit(x_train_tfidf,y_train_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       ...,\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct_tfidf = dct.predict_proba(x_valid_tfidf)\n",
    "\n",
    "dct_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating F1 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5498821681068342"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if prediction is greater than or equal to 0.3 than 1 else 0\n",
    "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\n",
    "dct_tfidf=dct_tfidf[:,1]>=0.3\n",
    "\n",
    "# converting the results to integer type\n",
    "dct_int_tfidf=dct_tfidf.astype(np.int)\n",
    "\n",
    "# calculating f1 score\n",
    "dct_score_tfidf=f1_score(y_valid_tfidf,dct_int_tfidf)\n",
    "\n",
    "dct_score_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "Algo=['LogisticRegression(Bag-of-Words)','XGBoost(Bag-of-Words)','DecisionTree(Bag-of-Words)','LogisticRegression(TF-IDF)','XGBoost(TF-IDF)','DecisionTree(TF-IDF)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = [log_bow,xgb_bow,dct_score_bow,log_tfidf,score,dct_score_tfidf]\n",
    "\n",
    "compare=pd.DataFrame({'Model':Algo,'F1_Score':score},index=[i for i in range(1,7)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>LogisticRegression(Bag-of-Words)</td>\n",
       "      <td>XGBoost(Bag-of-Words)</td>\n",
       "      <td>DecisionTree(Bag-of-Words)</td>\n",
       "      <td>LogisticRegression(TF-IDF)</td>\n",
       "      <td>XGBoost(TF-IDF)</td>\n",
       "      <td>DecisionTree(TF-IDF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1_Score</th>\n",
       "      <td>0.572135</td>\n",
       "      <td>0.571201</td>\n",
       "      <td>0.514178</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.565705</td>\n",
       "      <td>0.549882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         1                      2  \\\n",
       "Model     LogisticRegression(Bag-of-Words)  XGBoost(Bag-of-Words)   \n",
       "F1_Score                          0.572135               0.571201   \n",
       "\n",
       "                                   3                           4  \\\n",
       "Model     DecisionTree(Bag-of-Words)  LogisticRegression(TF-IDF)   \n",
       "F1_Score                    0.514178                    0.586207   \n",
       "\n",
       "                        5                     6  \n",
       "Model     XGBoost(TF-IDF)  DecisionTree(TF-IDF)  \n",
       "F1_Score         0.565705              0.549882  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "sns.pointplot(x='Model',y='F1_Score',data=compare)\n",
    "\n",
    "plt.title('Model Vs Score')\n",
    "plt.xlabel('MODEL')\n",
    "plt.ylabel('SCORE')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the best possible model to predict for the test data\n",
    "\n",
    "#### From the above comaprison graph we can see that Logistic Regression trained using TF-IDF features gives us the best performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_tfidf = tfidf_matrix[31962:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = Log_Reg.predict_proba(test_tfidf)\n",
    "\n",
    "test_pred_int = test_pred[:,1] >= 0.3\n",
    "\n",
    "test_pred_int = test_pred_int.astype(np.int)\n",
    "\n",
    "test['label'] = test_pred_int\n",
    "\n",
    "submission = test[['id','label']]\n",
    "\n",
    "submission.to_csv('result.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test dataset after prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.read_csv('result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31965</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31966</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31967</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31970</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31972</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31973</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31976</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31978</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>31979</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>31980</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31981</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31982</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>31985</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>31986</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31987</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31988</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31989</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31991</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31992</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17167</th>\n",
       "      <td>49130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17168</th>\n",
       "      <td>49131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17169</th>\n",
       "      <td>49132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17170</th>\n",
       "      <td>49133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17171</th>\n",
       "      <td>49134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17172</th>\n",
       "      <td>49135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17173</th>\n",
       "      <td>49136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17174</th>\n",
       "      <td>49137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17175</th>\n",
       "      <td>49138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17176</th>\n",
       "      <td>49139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17177</th>\n",
       "      <td>49140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17178</th>\n",
       "      <td>49141</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17179</th>\n",
       "      <td>49142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17180</th>\n",
       "      <td>49143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17181</th>\n",
       "      <td>49144</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17182</th>\n",
       "      <td>49145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17183</th>\n",
       "      <td>49146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17184</th>\n",
       "      <td>49147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17185</th>\n",
       "      <td>49148</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17186</th>\n",
       "      <td>49149</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17187</th>\n",
       "      <td>49150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17188</th>\n",
       "      <td>49151</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17189</th>\n",
       "      <td>49152</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17190</th>\n",
       "      <td>49153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17191</th>\n",
       "      <td>49154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17192</th>\n",
       "      <td>49155</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17193</th>\n",
       "      <td>49156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17194</th>\n",
       "      <td>49157</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17195</th>\n",
       "      <td>49158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17196</th>\n",
       "      <td>49159</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17197 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  label\n",
       "0      31963      0\n",
       "1      31964      0\n",
       "2      31965      0\n",
       "3      31966      0\n",
       "4      31967      0\n",
       "5      31968      0\n",
       "6      31969      0\n",
       "7      31970      0\n",
       "8      31971      0\n",
       "9      31972      0\n",
       "10     31973      0\n",
       "11     31974      0\n",
       "12     31975      0\n",
       "13     31976      0\n",
       "14     31977      0\n",
       "15     31978      0\n",
       "16     31979      0\n",
       "17     31980      0\n",
       "18     31981      0\n",
       "19     31982      1\n",
       "20     31983      0\n",
       "21     31984      0\n",
       "22     31985      0\n",
       "23     31986      0\n",
       "24     31987      0\n",
       "25     31988      0\n",
       "26     31989      1\n",
       "27     31990      0\n",
       "28     31991      0\n",
       "29     31992      0\n",
       "...      ...    ...\n",
       "17167  49130      0\n",
       "17168  49131      0\n",
       "17169  49132      0\n",
       "17170  49133      0\n",
       "17171  49134      0\n",
       "17172  49135      0\n",
       "17173  49136      0\n",
       "17174  49137      0\n",
       "17175  49138      0\n",
       "17176  49139      1\n",
       "17177  49140      0\n",
       "17178  49141      0\n",
       "17179  49142      0\n",
       "17180  49143      0\n",
       "17181  49144      0\n",
       "17182  49145      0\n",
       "17183  49146      0\n",
       "17184  49147      0\n",
       "17185  49148      0\n",
       "17186  49149      0\n",
       "17187  49150      0\n",
       "17188  49151      1\n",
       "17189  49152      0\n",
       "17190  49153      0\n",
       "17191  49154      0\n",
       "17192  49155      1\n",
       "17193  49156      0\n",
       "17194  49157      0\n",
       "17195  49158      0\n",
       "17196  49159      0\n",
       "\n",
       "[17197 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "- From the given dataset we were able to predict on which class i.e Positive or Negative does the given tweet fall into.The following data was collected from Analytics Vidhya's site.\n",
    "\n",
    "### Pre-processing \n",
    "1. Removing Twitter Handles(@user)\n",
    "2. Removing puntuation,numbers,special characters\n",
    "3. Removing short words i.e. words with length<3\n",
    "4. Tokenization\n",
    "5. Stemming\n",
    "\n",
    "### Data Visualisation\n",
    "1. Wordclouds\n",
    "2. Barplots\n",
    "\n",
    "### Word Embeddings used to convert words to features for our Machine Learning Model\n",
    "\n",
    "1. Bag-of-Words \n",
    "2. TF-IDF \n",
    "\n",
    "### Machine Learning Models used\n",
    "1. Logistic Regression\n",
    "2. XGBoost\n",
    "3. Decision Trees \n",
    "\n",
    "### Evaluation Metrics \n",
    "- F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_original['label'])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why use F1-Score instead of Accuracy ?\n",
    "\n",
    "- From the above countplot generated above we see how imbalanced our dataset is.We can see that the values with label:0 i.e. positive sentiments are quite high in number as compared to the values with labels:1 i.e. negative sentiments.\n",
    "\n",
    "\n",
    "- So when we keep accuracy as our evaluation metric there may be cases where we may encounter high number of false positives.\n",
    "\n",
    "#### Precison & Recall :- \n",
    "- Precision means the percentage of your results which are relevant.\n",
    "- Recall refers to the percentage of total relevant results correctly classified by your algorithm\n",
    "![met](https://cdn-images-1.medium.com/max/800/1*pOtBHai4jFd-ujaNXPilRg.png)\n",
    "\n",
    "- We always face a trade-off situation between Precison and Recall i.e. High Precison gives low recall and vice versa.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- In most problems, you could either give a higher priority to maximizing precision, or recall, depending upon the problem you are trying to solve. But in general, there is a simpler metric which takes into account both precision and recall, and therefore, you can aim to maximize this number to make your model better. This metric is known as F1-score, which is simply the harmonic mean of precision and recall.\n",
    "\n",
    "![f1](https://cdn-images-1.medium.com/max/800/1*DIhRgfwTcxnXJuKr2_cRvA.png)\n",
    "\n",
    "\n",
    "- So this metric seems much more easier and convenient to work with, as you only have to maximize one score, rather than balancing two separate scores."
   ]
  }
 ],
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