Data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 50\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "plt.style.use('bmh')\n",
    "# packages for data cleaning function\n",
    "import re\n",
    "import string\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>count</th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "      <th>class</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  hate_speech  offensive_language  neither  class  \\\n",
       "0      3            0                   0        3      2   \n",
       "1      3            0                   3        0      1   \n",
       "2      3            0                   3        0      1   \n",
       "3      3            0                   2        1      1   \n",
       "4      6            0                   6        0      1   \n",
       "\n",
       "                                               tweet  \n",
       "0  !!! RT @mayasolovely: As a woman you shouldn't...  \n",
       "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...  \n",
       "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...  \n",
       "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
       "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\Lalit\\Documents\\twitter_data.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = df['class'].replace(1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing hate speech to 1 and not hate speech to 0\n",
    "# df['label'] = df['label'].replace(1, 0)\n",
    "df['label'] = df['label'].replace(2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "ax = sns.countplot(df['label'], palette='Set2')\n",
    "\n",
    "ax.set_title('Amount of Tweets Per Label',fontsize = 20)\n",
    "ax.set_xlabel('Type of Tweet',fontsize = 15)\n",
    "ax.set_ylabel('Count',fontsize = 15)\n",
    "ax.set_xticklabels(['Not Hate Speech','Hate Speech'],fontsize = 13)\n",
    "\n",
    "total = float(len(df)) # one person per row \n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 3,\n",
    "            '{:1.2f}'.format(height/total * 100) + '%',\n",
    "            ha=\"center\") \n",
    "\n",
    "# plt.savefig('../visualizations/cleaned_class_imbalance.png', bbox_inches = \"tight\", pad_inches=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16797804946939435"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can see that only 16% of the data is actual hate speech\n",
    "df['label'].sum() / len(df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new dataframe `clean_df`\n",
    "raw_df = df[['tweet', 'label']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating column for vote count for not hate speech tweets\n",
    "# raw_df['other_votes'] = raw_df['offensive_language'] + raw_df['neither']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reordering the columns and dropping the old 'offensive_language' and 'neither' columns\n",
    "# raw_df = raw_df.reindex(columns=['total_votes', 'hate_speech_votes', 'other_votes','label', 'tweet'])"
   ]
  },
  {
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  label\n",
       "0  !!! RT @mayasolovely: As a woman you shouldn't...      1\n",
       "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...      0\n",
       "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...      0\n",
       "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...      0\n",
       "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...      0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle the raw_data for later eda\n",
    "raw_df.to_pickle(r\"C:\\Users\\Lalit\\Documents\\AI_sentimentAnalysis\\raw_tweets_df_new.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning the tweets\n",
    "\n",
    "# copying new `clean_df` and cleaning up the tweets\n",
    "clean_df = raw_df.copy()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  label\n",
       "0  !!! RT @mayasolovely: As a woman you shouldn't...      1\n",
       "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...      0\n",
       "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...      0\n",
       "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...      0\n",
       "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...      0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to clean all data\n",
    "def clean_text_round1(text):\n",
    "    '''Make text lowercase, remove text in square brackets, remove punctuation and remove words containing numbers.'''\n",
    "    # make text lowercase\n",
    "    text = text.lower()\n",
    "    # removing text within brackets\n",
    "    text = re.sub('\\[.*?\\]', '', text)\n",
    "    # removing text within parentheses\n",
    "    text = re.sub('\\(.*?\\)', '', text)\n",
    "    # removing numbers\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\n",
    "    # if there's more than 1 whitespace, then make it just 1\n",
    "    text = re.sub('\\s+', ' ', text)\n",
    "    # if there's a new line, then make it a whitespace\n",
    "    text = re.sub('\\n', ' ', text)\n",
    "    # removing any quotes\n",
    "    text = re.sub('\\\"+', '', text)\n",
    "    # removing &amp;\n",
    "    text = re.sub('(\\&amp\\;)', '', text)\n",
    "    # removing any usernames\n",
    "    text = re.sub('(@[^\\s]+)', '', text)\n",
    "    # removing any hashtags\n",
    "    text = re.sub('(#[^\\s]+)', '', text)\n",
    "    # remove `rt` for retweet\n",
    "    text = re.sub('(rt)', '', text)\n",
    "    # string.punctuation is a string of all punctuation marks\n",
    "    # so this gets rid of all punctuation\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
    "    # getting rid of `httptco`\n",
    "    text = re.sub('(httptco)', '', text)\n",
    "\n",
    "    return text\n",
    "\n",
    "round1 = lambda x: clean_text_round1(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# appling data cleaning function to `tweet` column\n",
    "clean_df['clean_tweets'] = clean_df['tweet'].apply(clean_text_round1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "      <th>clean_tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
       "      <td>1</td>\n",
       "      <td>as a woman you shouldnt complain about clea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>boy dats coldtyga dwn bad for cuffin dat ho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
       "      <td>0</td>\n",
       "      <td>dawg   you ever fuck a bitch and she sta to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "      <td>0</td>\n",
       "      <td>she look like a tranny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n",
       "      <td>0</td>\n",
       "      <td>the shit you hear about me might be true or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>!!!!!!!!!!!!!!!!!!\"@T_Madison_x: The shit just...</td>\n",
       "      <td>0</td>\n",
       "      <td>the shit just blows meclaim you so faithful a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>!!!!!!\"@__BrighterDays: I can not just sit up ...</td>\n",
       "      <td>0</td>\n",
       "      <td>i can not just sit up and hate on another bit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>!!!!&amp;#8220;@selfiequeenbri: cause I'm tired of...</td>\n",
       "      <td>0</td>\n",
       "      <td>cause im tired of you big bitches coming for ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\" &amp;amp; you might not get ya bitch back &amp;amp; ...</td>\n",
       "      <td>0</td>\n",
       "      <td>you might not get ya bitch back  thats that</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\" @rhythmixx_ :hobbies include: fighting Maria...</td>\n",
       "      <td>0</td>\n",
       "      <td>hobbies include fighting mariam bitch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  label  \\\n",
       "0  !!! RT @mayasolovely: As a woman you shouldn't...      1   \n",
       "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...      0   \n",
       "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...      0   \n",
       "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...      0   \n",
       "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...      0   \n",
       "5  !!!!!!!!!!!!!!!!!!\"@T_Madison_x: The shit just...      0   \n",
       "6  !!!!!!\"@__BrighterDays: I can not just sit up ...      0   \n",
       "7  !!!!&#8220;@selfiequeenbri: cause I'm tired of...      0   \n",
       "8  \" &amp; you might not get ya bitch back &amp; ...      0   \n",
       "9  \" @rhythmixx_ :hobbies include: fighting Maria...      0   \n",
       "\n",
       "                                        clean_tweets  \n",
       "0     as a woman you shouldnt complain about clea...  \n",
       "1     boy dats coldtyga dwn bad for cuffin dat ho...  \n",
       "2     dawg   you ever fuck a bitch and she sta to...  \n",
       "3                             she look like a tranny  \n",
       "4     the shit you hear about me might be true or...  \n",
       "5   the shit just blows meclaim you so faithful a...  \n",
       "6   i can not just sit up and hate on another bit...  \n",
       "7   cause im tired of you big bitches coming for ...  \n",
       "8       you might not get ya bitch back  thats that   \n",
       "9              hobbies include fighting mariam bitch  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet           0\n",
       "label           0\n",
       "clean_tweets    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "clean_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle clean_df for futher use\n",
    "clean_df.to_pickle(r\"C:\\Users\\Lalit\\Documents\\AI_sentimentAnalysis\\clean_df_new.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "      <th>clean_tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
       "      <td>1</td>\n",
       "      <td>as a woman you shouldnt complain about clea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>\" momma said no pussy cats inside my doghouse \"</td>\n",
       "      <td>1</td>\n",
       "      <td>momma said no pussy cats inside my doghouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>\"@Addicted2Guys: -SimplyAddictedToGuys http://...</td>\n",
       "      <td>1</td>\n",
       "      <td>simplyaddictedtoguys  woof woof hot scally lad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>\"@AllAboutManFeet: http://t.co/3gzUpfuMev\" woo...</td>\n",
       "      <td>1</td>\n",
       "      <td>woof woof and hot soles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>\"@Allyhaaaaa: Lemmie eat a Oreo &amp;amp; do these...</td>\n",
       "      <td>1</td>\n",
       "      <td>lemmie eat a oreo  do these dishes one oreo lol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25249</th>\n",
       "      <td>yaya ho.. cute avi tho RT @ViVaLa_Ari I had no...</td>\n",
       "      <td>1</td>\n",
       "      <td>yaya ho cute avi tho   i had no idea she was s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25250</th>\n",
       "      <td>yea so about @N_tel 's new friend.. all my fri...</td>\n",
       "      <td>1</td>\n",
       "      <td>yea so about  s new friend all my friends kno ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25280</th>\n",
       "      <td>you know what they say, the early bird gets th...</td>\n",
       "      <td>1</td>\n",
       "      <td>you know what they say the early bird gets the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25292</th>\n",
       "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
       "      <td>1</td>\n",
       "      <td>youve gone and broke the wrong hea baby and dr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25296</th>\n",
       "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
       "      <td>1</td>\n",
       "      <td>ruffled  ntac eileen dahlia  beautiful color c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4163 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   tweet  label  \\\n",
       "0      !!! RT @mayasolovely: As a woman you shouldn't...      1   \n",
       "40       \" momma said no pussy cats inside my doghouse \"      1   \n",
       "63     \"@Addicted2Guys: -SimplyAddictedToGuys http://...      1   \n",
       "66     \"@AllAboutManFeet: http://t.co/3gzUpfuMev\" woo...      1   \n",
       "67     \"@Allyhaaaaa: Lemmie eat a Oreo &amp; do these...      1   \n",
       "...                                                  ...    ...   \n",
       "25249  yaya ho.. cute avi tho RT @ViVaLa_Ari I had no...      1   \n",
       "25250  yea so about @N_tel 's new friend.. all my fri...      1   \n",
       "25280  you know what they say, the early bird gets th...      1   \n",
       "25292  you've gone and broke the wrong heart baby, an...      1   \n",
       "25296  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...      1   \n",
       "\n",
       "                                            clean_tweets  \n",
       "0         as a woman you shouldnt complain about clea...  \n",
       "40          momma said no pussy cats inside my doghouse   \n",
       "63        simplyaddictedtoguys  woof woof hot scally lad  \n",
       "66                               woof woof and hot soles  \n",
       "67       lemmie eat a oreo  do these dishes one oreo lol  \n",
       "...                                                  ...  \n",
       "25249  yaya ho cute avi tho   i had no idea she was s...  \n",
       "25250  yea so about  s new friend all my friends kno ...  \n",
       "25280  you know what they say the early bird gets the...  \n",
       "25292  youve gone and broke the wrong hea baby and dr...  \n",
       "25296  ruffled  ntac eileen dahlia  beautiful color c...  \n",
       "\n",
       "[4163 rows x 3 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hate = clean_df[clean_df.label==1]\n",
    "df_hate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18259</th>\n",
       "      <td>RT @TrueNovacane: @UFUCKIN_RIGHT hell nahh. If...</td>\n",
       "      <td>0</td>\n",
       "      <td>hell nahh if a bitch had respect for her ow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8252</th>\n",
       "      <td>Biggest booth I ever seen lastnight bitch had ...</td>\n",
       "      <td>0</td>\n",
       "      <td>biggest booth i ever seen lastnight bitch had ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17582</th>\n",
       "      <td>RT @Sam_James3: @will123_william @haydenhill13...</td>\n",
       "      <td>0</td>\n",
       "      <td>i dont care tho cracker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>&amp;#8220;@BeenSlimm: Too many \"lil niggas\" on my...</td>\n",
       "      <td>0</td>\n",
       "      <td>too many lil niggas on my tl vqgezmmwws boosi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7014</th>\n",
       "      <td>@penisgravy \\nIs dem? I wants to has my weenis...</td>\n",
       "      <td>0</td>\n",
       "      <td>is dem i wants to has my weenis swingin but d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9048</th>\n",
       "      <td>Don't know no ugly bitches, don't know no brok...</td>\n",
       "      <td>0</td>\n",
       "      <td>dont know no ugly bitches dont know no broke n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11647</th>\n",
       "      <td>I'm only on here cause my bitch tripping &amp;#128...</td>\n",
       "      <td>0</td>\n",
       "      <td>im only on here cause my bitch tripping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4273</th>\n",
       "      <td>@MoenKiana fine walk bitch</td>\n",
       "      <td>0</td>\n",
       "      <td>fine walk bitch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24430</th>\n",
       "      <td>hoes pick me like dandelions #PickMe</td>\n",
       "      <td>0</td>\n",
       "      <td>hoes pick me like dandelions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11772</th>\n",
       "      <td>Idk who tf Roro &amp;amp; Sophie are, but I'm glad...</td>\n",
       "      <td>0</td>\n",
       "      <td>idk who tf roro  sophie are but im glad they g...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4163 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   tweet  label  \\\n",
       "18259  RT @TrueNovacane: @UFUCKIN_RIGHT hell nahh. If...      0   \n",
       "8252   Biggest booth I ever seen lastnight bitch had ...      0   \n",
       "17582  RT @Sam_James3: @will123_william @haydenhill13...      0   \n",
       "1160   &#8220;@BeenSlimm: Too many \"lil niggas\" on my...      0   \n",
       "7014   @penisgravy \\nIs dem? I wants to has my weenis...      0   \n",
       "...                                                  ...    ...   \n",
       "9048   Don't know no ugly bitches, don't know no brok...      0   \n",
       "11647  I'm only on here cause my bitch tripping &#128...      0   \n",
       "4273                          @MoenKiana fine walk bitch      0   \n",
       "24430               hoes pick me like dandelions #PickMe      0   \n",
       "11772  Idk who tf Roro &amp; Sophie are, but I'm glad...      0   \n",
       "\n",
       "                                            clean_tweets  \n",
       "18259     hell nahh if a bitch had respect for her ow...  \n",
       "8252   biggest booth i ever seen lastnight bitch had ...  \n",
       "17582                            i dont care tho cracker  \n",
       "1160    too many lil niggas on my tl vqgezmmwws boosi...  \n",
       "7014    is dem i wants to has my weenis swingin but d...  \n",
       "...                                                  ...  \n",
       "9048   dont know no ugly bitches dont know no broke n...  \n",
       "11647           im only on here cause my bitch tripping   \n",
       "4273                                     fine walk bitch  \n",
       "24430                      hoes pick me like dandelions   \n",
       "11772  idk who tf roro  sophie are but im glad they g...  \n",
       "\n",
       "[4163 rows x 3 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# randomly choosing the not hate rows\n",
    "df_label_not_hate = clean_df[clean_df.label==0]\n",
    "random_df_label_not_hate = df_label_not_hate.sample(4163)\n",
    "random_df_label_not_hate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "new_train_df = df_hate.append(random_df_label_not_hate, ignore_index=True)\n",
    "new_train_df = shuffle(new_train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "ax = sns.countplot(new_train_df['label'], palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train_df\n",
    "# pickle clean_df for futher use\n",
    "new_train_df.to_pickle(r\"C:\\Users\\Lalit\\Documents\\AI_sentimentAnalysis\\train_df_.pkl\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}