task3_topic_modeling_and_sentiment_analysis..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style='color:green'> Task 3 : Data Exploration & Preprocessing, Topic Modeling & Sentiment Analysis</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the csv file generated from clean_tweets_dataframe.py\n",
    "tweets_df = pd.read_csv(\"data/clean_processed_tweet_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1 Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 created_at  \\\n",
       "0           0  2022-08-07 22:31:20+00:00   \n",
       "1           1  2022-08-07 22:31:16+00:00   \n",
       "2           2  2022-08-07 22:31:07+00:00   \n",
       "3           3  2022-08-07 22:31:06+00:00   \n",
       "4           4  2022-08-07 22:31:04+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/android\" ...   \n",
       "1  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2  <a href=\"http://twitter.com/download/android\" ...   \n",
       "3  <a href=\"http://twitter.com/download/android\" ...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                       original_text  subjectivity  \\\n",
       "0  RT @i_ameztoy: Extra random image (I):\\n\\nLets...      0.190625   \n",
       "1  RT @IndoPac_Info: #China's media explains the ...      0.100000   \n",
       "2  China even cut off communication, they don't a...      0.000000   \n",
       "3  Putin to #XiJinping : I told you my friend, Ta...      0.350000   \n",
       "4  RT @ChinaUncensored: I’m sorry, I thought Taiw...      0.556250   \n",
       "\n",
       "       polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0 -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1 -1.000000e-01   en             691            201           ZIisq   \n",
       "2  0.000000e+00   en               0              0       Fin21Free   \n",
       "3  1.000000e-01   en               0              0       Fin21Free   \n",
       "4 -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "\n",
       "   followers_count  friends_count possibly_sensitive       hashtags  \\\n",
       "0            20497           2621                NaN           City   \n",
       "1               65            272                NaN  China, Taiwan   \n",
       "2               85            392                NaN      XiJinping   \n",
       "3               85            392                NaN      XiJinping   \n",
       "4              910           2608                NaN            NaN   \n",
       "\n",
       "     user_mentions           place  \n",
       "0        i_ameztoy             NaN  \n",
       "1     IndoPac_Info             NaN  \n",
       "2      ZelenskyyUa     Netherlands  \n",
       "3              NaN     Netherlands  \n",
       "4  ChinaUncensored  Ayent, Schweiz  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first 5 rows from our dataset\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22000 entries, 0 to 21999\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Unnamed: 0          22000 non-null  int64  \n",
      " 1   created_at          22000 non-null  object \n",
      " 2   source              22000 non-null  object \n",
      " 3   original_text       22000 non-null  object \n",
      " 4   subjectivity        22000 non-null  float64\n",
      " 5   polarity            22000 non-null  float64\n",
      " 6   lang                22000 non-null  object \n",
      " 7   favorite_count      22000 non-null  int64  \n",
      " 8   retweet_count       22000 non-null  int64  \n",
      " 9   original_author     22000 non-null  object \n",
      " 10  followers_count     22000 non-null  int64  \n",
      " 11  friends_count       22000 non-null  int64  \n",
      " 12  possibly_sensitive  6191 non-null   object \n",
      " 13  hashtags            19141 non-null  object \n",
      " 14  user_mentions       18698 non-null  object \n",
      " 15  place               12107 non-null  object \n",
      "dtypes: float64(2), int64(5), object(9)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# display dataframe information\n",
    "tweets_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  created_at  source  original_text  subjectivity  polarity  \\\n",
       "0           False       False   False          False         False     False   \n",
       "1           False       False   False          False         False     False   \n",
       "2           False       False   False          False         False     False   \n",
       "3           False       False   False          False         False     False   \n",
       "4           False       False   False          False         False     False   \n",
       "...           ...         ...     ...            ...           ...       ...   \n",
       "21995       False       False   False          False         False     False   \n",
       "21996       False       False   False          False         False     False   \n",
       "21997       False       False   False          False         False     False   \n",
       "21998       False       False   False          False         False     False   \n",
       "21999       False       False   False          False         False     False   \n",
       "\n",
       "        lang  favorite_count  retweet_count  original_author  followers_count  \\\n",
       "0      False           False          False            False            False   \n",
       "1      False           False          False            False            False   \n",
       "2      False           False          False            False            False   \n",
       "3      False           False          False            False            False   \n",
       "4      False           False          False            False            False   \n",
       "...      ...             ...            ...              ...              ...   \n",
       "21995  False           False          False            False            False   \n",
       "21996  False           False          False            False            False   \n",
       "21997  False           False          False            False            False   \n",
       "21998  False           False          False            False            False   \n",
       "21999  False           False          False            False            False   \n",
       "\n",
       "       friends_count  possibly_sensitive  hashtags  user_mentions  place  \n",
       "0              False                True     False          False   True  \n",
       "1              False                True     False          False   True  \n",
       "2              False                True     False          False  False  \n",
       "3              False                True     False           True  False  \n",
       "4              False                True      True          False  False  \n",
       "...              ...                 ...       ...            ...    ...  \n",
       "21995          False               False     False          False   True  \n",
       "21996          False               False     False          False  False  \n",
       "21997          False                True     False          False  False  \n",
       "21998          False                True     False          False  False  \n",
       "21999          False                True     False          False   True  \n",
       "\n",
       "[22000 rows x 16 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check null values\n",
    "tweets_df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22000, 16)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataframe\n",
    "tweets_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'created_at', 'source', 'original_text', 'subjectivity',\n",
       "       'polarity', 'lang', 'favorite_count', 'retweet_count',\n",
       "       'original_author', 'followers_count', 'friends_count',\n",
       "       'possibly_sensitive', 'hashtags', 'user_mentions', 'place'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show columns of the dataframe\n",
    "tweets_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-08-07 22:31:02+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @benedictrogers: We must not let this happe...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>en</td>\n",
       "      <td>116</td>\n",
       "      <td>36</td>\n",
       "      <td>GraceCh15554845</td>\n",
       "      <td>207</td>\n",
       "      <td>54</td>\n",
       "      <td>False</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>benedictrogers</td>\n",
       "      <td>Melbourne, Victoria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>2022-08-07 22:26:25+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @ChinaInfo777: #PinkFloyd Roger Waters tell...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>en</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>nhohn2011</td>\n",
       "      <td>870</td>\n",
       "      <td>508</td>\n",
       "      <td>False</td>\n",
       "      <td>PinkFloyd, Taiwan, China</td>\n",
       "      <td>ChinaInfo777</td>\n",
       "      <td>Florida, USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>2022-08-07 22:25:37+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @WilliamYang120: \"For too long, #Taiwan has...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>en</td>\n",
       "      <td>311</td>\n",
       "      <td>84</td>\n",
       "      <td>hoggothoaryhost</td>\n",
       "      <td>44</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>WilliamYang120</td>\n",
       "      <td>Hong Kong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>2022-08-07 22:25:05+00:00</td>\n",
       "      <td>&lt;a href=\"https://help.twitter.com/en/using-twi...</td>\n",
       "      <td>RT @odisseoisback: #Corfu #Greece🇬🇷\\n#Summer #...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>hephaistos_ai</td>\n",
       "      <td>4161</td>\n",
       "      <td>208</td>\n",
       "      <td>False</td>\n",
       "      <td>Corfu, Greece, Summer, Beaches, Travel, Excurs...</td>\n",
       "      <td>odisseoisback</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>2022-08-07 22:24:44+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @benedictrogers: We must not let this happe...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>en</td>\n",
       "      <td>116</td>\n",
       "      <td>36</td>\n",
       "      <td>hoggothoaryhost</td>\n",
       "      <td>44</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>benedictrogers</td>\n",
       "      <td>Hong Kong</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21981</th>\n",
       "      <td>21981</td>\n",
       "      <td>2022-08-06 18:04:09+00:00</td>\n",
       "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
       "      <td>RT @jenniferatntd: Head of #Taiwan's #missile ...</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>en</td>\n",
       "      <td>194</td>\n",
       "      <td>99</td>\n",
       "      <td>threadmaxwhispe</td>\n",
       "      <td>657</td>\n",
       "      <td>864</td>\n",
       "      <td>False</td>\n",
       "      <td>Taiwan, missile</td>\n",
       "      <td>jenniferatntd</td>\n",
       "      <td>Land of Ethan South Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21989</th>\n",
       "      <td>21989</td>\n",
       "      <td>2022-08-06 18:03:48+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Minister Wu is crystal clear in his @BBCNews i...</td>\n",
       "      <td>0.419444</td>\n",
       "      <td>0.158333</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TECO_Toronto</td>\n",
       "      <td>955</td>\n",
       "      <td>202</td>\n",
       "      <td>False</td>\n",
       "      <td>Taiwan, StandWithTaiwan, DefendDemocracy</td>\n",
       "      <td>BBCNews, SpeakerPelosi</td>\n",
       "      <td>Toronto, Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21990</th>\n",
       "      <td>21990</td>\n",
       "      <td>2022-08-06 18:03:47+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @SpokespersonCHN: #PLA Live-fire military d...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>en</td>\n",
       "      <td>2611</td>\n",
       "      <td>405</td>\n",
       "      <td>mumaralid</td>\n",
       "      <td>1164</td>\n",
       "      <td>605</td>\n",
       "      <td>True</td>\n",
       "      <td>PLA, Taiwan</td>\n",
       "      <td>SpokespersonCHN</td>\n",
       "      <td>Driver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21992</th>\n",
       "      <td>21992</td>\n",
       "      <td>2022-08-06 18:03:33+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @jenniferatntd: Head of #Taiwan's #missile ...</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>en</td>\n",
       "      <td>194</td>\n",
       "      <td>99</td>\n",
       "      <td>9thousandbytes</td>\n",
       "      <td>401</td>\n",
       "      <td>858</td>\n",
       "      <td>False</td>\n",
       "      <td>Taiwan, missile</td>\n",
       "      <td>jenniferatntd</td>\n",
       "      <td>Northern Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21996</th>\n",
       "      <td>21996</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>sashalenik</td>\n",
       "      <td>94</td>\n",
       "      <td>1751</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Gelendzhik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1976 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                 created_at  \\\n",
       "5               5  2022-08-07 22:31:02+00:00   \n",
       "36             36  2022-08-07 22:26:25+00:00   \n",
       "39             39  2022-08-07 22:25:37+00:00   \n",
       "43             43  2022-08-07 22:25:05+00:00   \n",
       "48             48  2022-08-07 22:24:44+00:00   \n",
       "...           ...                        ...   \n",
       "21981       21981  2022-08-06 18:04:09+00:00   \n",
       "21989       21989  2022-08-06 18:03:48+00:00   \n",
       "21990       21990  2022-08-06 18:03:47+00:00   \n",
       "21992       21992  2022-08-06 18:03:33+00:00   \n",
       "21996       21996  2022-08-06 18:03:27+00:00   \n",
       "\n",
       "                                                  source  \\\n",
       "5      <a href=\"http://twitter.com/download/android\" ...   \n",
       "36     <a href=\"http://twitter.com/download/android\" ...   \n",
       "39     <a href=\"http://twitter.com/download/android\" ...   \n",
       "43     <a href=\"https://help.twitter.com/en/using-twi...   \n",
       "48     <a href=\"http://twitter.com/download/android\" ...   \n",
       "...                                                  ...   \n",
       "21981  <a href=\"https://mobile.twitter.com\" rel=\"nofo...   \n",
       "21989  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "21990  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "21992  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "21996  <a href=\"http://twitter.com/download/android\" ...   \n",
       "\n",
       "                                           original_text  subjectivity  \\\n",
       "5      RT @benedictrogers: We must not let this happe...      0.500000   \n",
       "36     RT @ChinaInfo777: #PinkFloyd Roger Waters tell...      0.000000   \n",
       "39     RT @WilliamYang120: \"For too long, #Taiwan has...      0.200000   \n",
       "43     RT @odisseoisback: #Corfu #Greece🇬🇷\\n#Summer #...      0.000000   \n",
       "48     RT @benedictrogers: We must not let this happe...      0.500000   \n",
       "...                                                  ...           ...   \n",
       "21981  RT @jenniferatntd: Head of #Taiwan's #missile ...      0.400000   \n",
       "21989  Minister Wu is crystal clear in his @BBCNews i...      0.419444   \n",
       "21990  RT @SpokespersonCHN: #PLA Live-fire military d...      0.250000   \n",
       "21992  RT @jenniferatntd: Head of #Taiwan's #missile ...      0.400000   \n",
       "21996  RT @IndoPac_Info: A good infographic of #China...      0.600000   \n",
       "\n",
       "       polarity lang  favorite_count  retweet_count  original_author  \\\n",
       "5      0.200000   en             116             36  GraceCh15554845   \n",
       "36     0.000000   en              23              5        nhohn2011   \n",
       "39    -0.025000   en             311             84  hoggothoaryhost   \n",
       "43     0.000000   en               0              2    hephaistos_ai   \n",
       "48     0.200000   en             116             36  hoggothoaryhost   \n",
       "...         ...  ...             ...            ...              ...   \n",
       "21981 -0.200000   en             194             99  threadmaxwhispe   \n",
       "21989  0.158333   en               0              0     TECO_Toronto   \n",
       "21990 -0.100000   en            2611            405        mumaralid   \n",
       "21992 -0.200000   en             194             99   9thousandbytes   \n",
       "21996  0.700000   en             507            183       sashalenik   \n",
       "\n",
       "       followers_count  friends_count possibly_sensitive  \\\n",
       "5                  207             54              False   \n",
       "36                 870            508              False   \n",
       "39                  44             60              False   \n",
       "43                4161            208              False   \n",
       "48                  44             60              False   \n",
       "...                ...            ...                ...   \n",
       "21981              657            864              False   \n",
       "21989              955            202              False   \n",
       "21990             1164            605               True   \n",
       "21992              401            858              False   \n",
       "21996               94           1751              False   \n",
       "\n",
       "                                                hashtags  \\\n",
       "5                                                 Taiwan   \n",
       "36                              PinkFloyd, Taiwan, China   \n",
       "39                                                Taiwan   \n",
       "43     Corfu, Greece, Summer, Beaches, Travel, Excurs...   \n",
       "48                                                Taiwan   \n",
       "...                                                  ...   \n",
       "21981                                    Taiwan, missile   \n",
       "21989           Taiwan, StandWithTaiwan, DefendDemocracy   \n",
       "21990                                        PLA, Taiwan   \n",
       "21992                                    Taiwan, missile   \n",
       "21996                   China, Taiwan, ChinaTaiwanCrisis   \n",
       "\n",
       "                user_mentions                         place  \n",
       "5              benedictrogers           Melbourne, Victoria  \n",
       "36               ChinaInfo777                  Florida, USA  \n",
       "39             WilliamYang120                     Hong Kong  \n",
       "43              odisseoisback                         Paris  \n",
       "48             benedictrogers                     Hong Kong  \n",
       "...                       ...                           ...  \n",
       "21981           jenniferatntd   Land of Ethan South Dakota   \n",
       "21989  BBCNews, SpeakerPelosi               Toronto, Canada  \n",
       "21990         SpokespersonCHN                        Driver  \n",
       "21992           jenniferatntd             Northern Virginia  \n",
       "21996            IndoPac_Info                    Gelendzhik  \n",
       "\n",
       "[1976 rows x 16 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop empty values\n",
    "tweets_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df[\"source\"] = tweets_df[\"source\"].str.replace(r\"(\\s*\\<.*?\\>\\s*)\", \" \", regex=True).str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21995</th>\n",
       "      <td>21995</td>\n",
       "      <td>2022-08-06 18:03:29+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>VandelayT</td>\n",
       "      <td>62</td>\n",
       "      <td>471</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21996</th>\n",
       "      <td>21996</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>sashalenik</td>\n",
       "      <td>94</td>\n",
       "      <td>1751</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Gelendzhik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21997</th>\n",
       "      <td>21997</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>@Reuters Thanks #Pelosi smart move.</td>\n",
       "      <td>0.421429</td>\n",
       "      <td>2.071429e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ZeitounRimal</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pelosi</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>🇺🇲🇷🇺🇺🇦🇫🇷🇦🇪🇮🇱🏳️‍🌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21998</th>\n",
       "      <td>21998</td>\n",
       "      <td>2022-08-06 18:03:26+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>en</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>SazzyCowgirl1</td>\n",
       "      <td>537</td>\n",
       "      <td>317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan, China</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Oregon, USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21999</th>\n",
       "      <td>21999</td>\n",
       "      <td>2022-08-06 18:03:25+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>en</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>VandelayT</td>\n",
       "      <td>62</td>\n",
       "      <td>471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan, China</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                 created_at               source  \\\n",
       "0               0  2022-08-07 22:31:20+00:00  Twitter for Android   \n",
       "1               1  2022-08-07 22:31:16+00:00  Twitter for Android   \n",
       "2               2  2022-08-07 22:31:07+00:00  Twitter for Android   \n",
       "3               3  2022-08-07 22:31:06+00:00  Twitter for Android   \n",
       "4               4  2022-08-07 22:31:04+00:00   Twitter for iPhone   \n",
       "...           ...                        ...                  ...   \n",
       "21995       21995  2022-08-06 18:03:29+00:00  Twitter for Android   \n",
       "21996       21996  2022-08-06 18:03:27+00:00  Twitter for Android   \n",
       "21997       21997  2022-08-06 18:03:27+00:00  Twitter for Android   \n",
       "21998       21998  2022-08-06 18:03:26+00:00  Twitter for Android   \n",
       "21999       21999  2022-08-06 18:03:25+00:00  Twitter for Android   \n",
       "\n",
       "                                           original_text  subjectivity  \\\n",
       "0      RT @i_ameztoy: Extra random image (I):\\n\\nLets...      0.190625   \n",
       "1      RT @IndoPac_Info: #China's media explains the ...      0.100000   \n",
       "2      China even cut off communication, they don't a...      0.000000   \n",
       "3      Putin to #XiJinping : I told you my friend, Ta...      0.350000   \n",
       "4      RT @ChinaUncensored: I’m sorry, I thought Taiw...      0.556250   \n",
       "...                                                  ...           ...   \n",
       "21995  RT @IndoPac_Info: A good infographic of #China...      0.600000   \n",
       "21996  RT @IndoPac_Info: A good infographic of #China...      0.600000   \n",
       "21997                @Reuters Thanks #Pelosi smart move.      0.421429   \n",
       "21998  RT @IndoPac_Info: #Taiwan people's Desire for ...      0.350000   \n",
       "21999  RT @IndoPac_Info: #Taiwan people's Desire for ...      0.350000   \n",
       "\n",
       "           polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0     -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1     -1.000000e-01   en             691            201           ZIisq   \n",
       "2      0.000000e+00   en               0              0       Fin21Free   \n",
       "3      1.000000e-01   en               0              0       Fin21Free   \n",
       "4     -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "...             ...  ...             ...            ...             ...   \n",
       "21995  7.000000e-01   en             507            183       VandelayT   \n",
       "21996  7.000000e-01   en             507            183      sashalenik   \n",
       "21997  2.071429e-01   en               0              0    ZeitounRimal   \n",
       "21998  5.000000e-02   en             199             67   SazzyCowgirl1   \n",
       "21999  5.000000e-02   en             199             67       VandelayT   \n",
       "\n",
       "       followers_count  friends_count possibly_sensitive  \\\n",
       "0                20497           2621                NaN   \n",
       "1                   65            272                NaN   \n",
       "2                   85            392                NaN   \n",
       "3                   85            392                NaN   \n",
       "4                  910           2608                NaN   \n",
       "...                ...            ...                ...   \n",
       "21995               62            471              False   \n",
       "21996               94           1751              False   \n",
       "21997               88              0                NaN   \n",
       "21998              537            317                NaN   \n",
       "21999               62            471                NaN   \n",
       "\n",
       "                               hashtags    user_mentions             place  \n",
       "0                                  City        i_ameztoy               NaN  \n",
       "1                         China, Taiwan     IndoPac_Info               NaN  \n",
       "2                             XiJinping      ZelenskyyUa       Netherlands  \n",
       "3                             XiJinping              NaN       Netherlands  \n",
       "4                                   NaN  ChinaUncensored    Ayent, Schweiz  \n",
       "...                                 ...              ...               ...  \n",
       "21995  China, Taiwan, ChinaTaiwanCrisis     IndoPac_Info               NaN  \n",
       "21996  China, Taiwan, ChinaTaiwanCrisis     IndoPac_Info        Gelendzhik  \n",
       "21997                            Pelosi          Reuters  🇺🇲🇷🇺🇺🇦🇫🇷🇦🇪🇮🇱🏳️‍🌈  \n",
       "21998                     Taiwan, China     IndoPac_Info       Oregon, USA  \n",
       "21999                     Taiwan, China     IndoPac_Info               NaN  \n",
       "\n",
       "[22000 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yonny\\AppData\\Local\\Temp/ipykernel_11044/1494577151.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[0].set_xticklabels(labels, rotation=90)\n",
      "C:\\Users\\Yonny\\AppData\\Local\\Temp/ipykernel_11044/1494577151.py:28: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize polarity column using piechart and barchart\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = ['neutral', 'positive', 'negative']\n",
    "\n",
    "neutral_count =  len(tweets_df[tweets_df['polarity'] > 0])\n",
    "positive_count = len(tweets_df[tweets_df['polarity'] == 0])\n",
    "negative_count = len(tweets_df[tweets_df['polarity'] <0])\n",
    "sizes = [neutral_count, positive_count, negative_count]\n",
    "# Create a figure for 2 subplots (1 row, 2 columns)\n",
    "fig, ax = plt.subplots(1, 2, figsize = (10,4))\n",
    "\n",
    "# Create a bar plot of score column\n",
    "ax[0].bar(x=labels, height=[neutral_count, positive_count, negative_count], color='orange')\n",
    "ax[0].set_title('Barchart of score column')\n",
    "ax[0].set_xticklabels(labels, rotation=90)\n",
    "\n",
    "# Create a pie chart of score column based on neutral, positive or negative\n",
    "\n",
    "ax[1].pie(sizes,labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax[1].set_title('Piechart of score column')\n",
    "ax[1].legend(labels)\n",
    "\n",
    "# Add a title to the Figure\n",
    "fig.suptitle('Score column plots')\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new column named clean_text to store cleaned original text\n",
    "tweets_df.insert(4,column = 'clean_text',value = tweets_df['original_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 created_at               source  \\\n",
       "0           0  2022-08-07 22:31:20+00:00  Twitter for Android   \n",
       "1           1  2022-08-07 22:31:16+00:00  Twitter for Android   \n",
       "2           2  2022-08-07 22:31:07+00:00  Twitter for Android   \n",
       "3           3  2022-08-07 22:31:06+00:00  Twitter for Android   \n",
       "4           4  2022-08-07 22:31:04+00:00   Twitter for iPhone   \n",
       "\n",
       "                                       original_text  \\\n",
       "0  RT @i_ameztoy: Extra random image (I):\\n\\nLets...   \n",
       "1  RT @IndoPac_Info: #China's media explains the ...   \n",
       "2  China even cut off communication, they don't a...   \n",
       "3  Putin to #XiJinping : I told you my friend, Ta...   \n",
       "4  RT @ChinaUncensored: I’m sorry, I thought Taiw...   \n",
       "\n",
       "                                          clean_text  subjectivity  \\\n",
       "0  RT @i_ameztoy: Extra random image (I):\\n\\nLets...      0.190625   \n",
       "1  RT @IndoPac_Info: #China's media explains the ...      0.100000   \n",
       "2  China even cut off communication, they don't a...      0.000000   \n",
       "3  Putin to #XiJinping : I told you my friend, Ta...      0.350000   \n",
       "4  RT @ChinaUncensored: I’m sorry, I thought Taiw...      0.556250   \n",
       "\n",
       "       polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0 -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1 -1.000000e-01   en             691            201           ZIisq   \n",
       "2  0.000000e+00   en               0              0       Fin21Free   \n",
       "3  1.000000e-01   en               0              0       Fin21Free   \n",
       "4 -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "\n",
       "   followers_count  friends_count possibly_sensitive       hashtags  \\\n",
       "0            20497           2621                NaN           City   \n",
       "1               65            272                NaN  China, Taiwan   \n",
       "2               85            392                NaN      XiJinping   \n",
       "3               85            392                NaN      XiJinping   \n",
       "4              910           2608                NaN            NaN   \n",
       "\n",
       "     user_mentions           place  \n",
       "0        i_ameztoy             NaN  \n",
       "1     IndoPac_Info             NaN  \n",
       "2      ZelenskyyUa     Netherlands  \n",
       "3              NaN     Netherlands  \n",
       "4  ChinaUncensored  Ayent, Schweiz  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21995</th>\n",
       "      <td>21995</td>\n",
       "      <td>2022-08-06 18:03:29+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>VandelayT</td>\n",
       "      <td>62</td>\n",
       "      <td>471</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21996</th>\n",
       "      <td>21996</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>sashalenik</td>\n",
       "      <td>94</td>\n",
       "      <td>1751</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Gelendzhik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21997</th>\n",
       "      <td>21997</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>@Reuters Thanks #Pelosi smart move.</td>\n",
       "      <td>@Reuters Thanks #Pelosi smart move.</td>\n",
       "      <td>0.421429</td>\n",
       "      <td>2.071429e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ZeitounRimal</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pelosi</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>🇺🇲🇷🇺🇺🇦🇫🇷🇦🇪🇮🇱🏳️‍🌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21998</th>\n",
       "      <td>21998</td>\n",
       "      <td>2022-08-06 18:03:26+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>en</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>SazzyCowgirl1</td>\n",
       "      <td>537</td>\n",
       "      <td>317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan, China</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Oregon, USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21999</th>\n",
       "      <td>21999</td>\n",
       "      <td>2022-08-06 18:03:25+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>en</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>VandelayT</td>\n",
       "      <td>62</td>\n",
       "      <td>471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan, China</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                 created_at               source  \\\n",
       "0               0  2022-08-07 22:31:20+00:00  Twitter for Android   \n",
       "1               1  2022-08-07 22:31:16+00:00  Twitter for Android   \n",
       "2               2  2022-08-07 22:31:07+00:00  Twitter for Android   \n",
       "3               3  2022-08-07 22:31:06+00:00  Twitter for Android   \n",
       "4               4  2022-08-07 22:31:04+00:00   Twitter for iPhone   \n",
       "...           ...                        ...                  ...   \n",
       "21995       21995  2022-08-06 18:03:29+00:00  Twitter for Android   \n",
       "21996       21996  2022-08-06 18:03:27+00:00  Twitter for Android   \n",
       "21997       21997  2022-08-06 18:03:27+00:00  Twitter for Android   \n",
       "21998       21998  2022-08-06 18:03:26+00:00  Twitter for Android   \n",
       "21999       21999  2022-08-06 18:03:25+00:00  Twitter for Android   \n",
       "\n",
       "                                           original_text  \\\n",
       "0      RT @i_ameztoy: Extra random image (I):\\n\\nLets...   \n",
       "1      RT @IndoPac_Info: #China's media explains the ...   \n",
       "2      China even cut off communication, they don't a...   \n",
       "3      Putin to #XiJinping : I told you my friend, Ta...   \n",
       "4      RT @ChinaUncensored: I’m sorry, I thought Taiw...   \n",
       "...                                                  ...   \n",
       "21995  RT @IndoPac_Info: A good infographic of #China...   \n",
       "21996  RT @IndoPac_Info: A good infographic of #China...   \n",
       "21997                @Reuters Thanks #Pelosi smart move.   \n",
       "21998  RT @IndoPac_Info: #Taiwan people's Desire for ...   \n",
       "21999  RT @IndoPac_Info: #Taiwan people's Desire for ...   \n",
       "\n",
       "                                              clean_text  subjectivity  \\\n",
       "0      RT @i_ameztoy: Extra random image (I):\\n\\nLets...      0.190625   \n",
       "1      RT @IndoPac_Info: #China's media explains the ...      0.100000   \n",
       "2      China even cut off communication, they don't a...      0.000000   \n",
       "3      Putin to #XiJinping : I told you my friend, Ta...      0.350000   \n",
       "4      RT @ChinaUncensored: I’m sorry, I thought Taiw...      0.556250   \n",
       "...                                                  ...           ...   \n",
       "21995  RT @IndoPac_Info: A good infographic of #China...      0.600000   \n",
       "21996  RT @IndoPac_Info: A good infographic of #China...      0.600000   \n",
       "21997                @Reuters Thanks #Pelosi smart move.      0.421429   \n",
       "21998  RT @IndoPac_Info: #Taiwan people's Desire for ...      0.350000   \n",
       "21999  RT @IndoPac_Info: #Taiwan people's Desire for ...      0.350000   \n",
       "\n",
       "           polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0     -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1     -1.000000e-01   en             691            201           ZIisq   \n",
       "2      0.000000e+00   en               0              0       Fin21Free   \n",
       "3      1.000000e-01   en               0              0       Fin21Free   \n",
       "4     -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "...             ...  ...             ...            ...             ...   \n",
       "21995  7.000000e-01   en             507            183       VandelayT   \n",
       "21996  7.000000e-01   en             507            183      sashalenik   \n",
       "21997  2.071429e-01   en               0              0    ZeitounRimal   \n",
       "21998  5.000000e-02   en             199             67   SazzyCowgirl1   \n",
       "21999  5.000000e-02   en             199             67       VandelayT   \n",
       "\n",
       "       followers_count  friends_count possibly_sensitive  \\\n",
       "0                20497           2621                NaN   \n",
       "1                   65            272                NaN   \n",
       "2                   85            392                NaN   \n",
       "3                   85            392                NaN   \n",
       "4                  910           2608                NaN   \n",
       "...                ...            ...                ...   \n",
       "21995               62            471              False   \n",
       "21996               94           1751              False   \n",
       "21997               88              0                NaN   \n",
       "21998              537            317                NaN   \n",
       "21999               62            471                NaN   \n",
       "\n",
       "                               hashtags    user_mentions             place  \n",
       "0                                  City        i_ameztoy               NaN  \n",
       "1                         China, Taiwan     IndoPac_Info               NaN  \n",
       "2                             XiJinping      ZelenskyyUa       Netherlands  \n",
       "3                             XiJinping              NaN       Netherlands  \n",
       "4                                   NaN  ChinaUncensored    Ayent, Schweiz  \n",
       "...                                 ...              ...               ...  \n",
       "21995  China, Taiwan, ChinaTaiwanCrisis     IndoPac_Info               NaN  \n",
       "21996  China, Taiwan, ChinaTaiwanCrisis     IndoPac_Info        Gelendzhik  \n",
       "21997                            Pelosi          Reuters  🇺🇲🇷🇺🇺🇦🇫🇷🇦🇪🇮🇱🏳️‍🌈  \n",
       "21998                     Taiwan, China     IndoPac_Info       Oregon, USA  \n",
       "21999                     Taiwan, China     IndoPac_Info               NaN  \n",
       "\n",
       "[22000 rows x 17 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add new column named sentimnt to where the text is positive,negative or neutral\n",
    "# tweets_df.insert(7,column = 'sentiment',value = tweets_df['polarity'])\n",
    "tweets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "# tweets_df = tweets_df[['original_text','clean_text','polarity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_category (polarity):\n",
    "    if polarity > 0:\n",
    "        return 'positive'\n",
    "    if polarity < 0:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return 'neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 created_at               source  \\\n",
       "0           0  2022-08-07 22:31:20+00:00  Twitter for Android   \n",
       "1           1  2022-08-07 22:31:16+00:00  Twitter for Android   \n",
       "2           2  2022-08-07 22:31:07+00:00  Twitter for Android   \n",
       "3           3  2022-08-07 22:31:06+00:00  Twitter for Android   \n",
       "4           4  2022-08-07 22:31:04+00:00   Twitter for iPhone   \n",
       "\n",
       "                                       original_text  \\\n",
       "0  RT @i_ameztoy: Extra random image (I):\\n\\nLets...   \n",
       "1  RT @IndoPac_Info: #China's media explains the ...   \n",
       "2  China even cut off communication, they don't a...   \n",
       "3  Putin to #XiJinping : I told you my friend, Ta...   \n",
       "4  RT @ChinaUncensored: I’m sorry, I thought Taiw...   \n",
       "\n",
       "                                          clean_text  subjectivity  \\\n",
       "0  RT @i_ameztoy: Extra random image (I):\\n\\nLets...      0.190625   \n",
       "1  RT @IndoPac_Info: #China's media explains the ...      0.100000   \n",
       "2  China even cut off communication, they don't a...      0.000000   \n",
       "3  Putin to #XiJinping : I told you my friend, Ta...      0.350000   \n",
       "4  RT @ChinaUncensored: I’m sorry, I thought Taiw...      0.556250   \n",
       "\n",
       "       polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0 -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1 -1.000000e-01   en             691            201           ZIisq   \n",
       "2  0.000000e+00   en               0              0       Fin21Free   \n",
       "3  1.000000e-01   en               0              0       Fin21Free   \n",
       "4 -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "\n",
       "   followers_count  friends_count possibly_sensitive       hashtags  \\\n",
       "0            20497           2621                NaN           City   \n",
       "1               65            272                NaN  China, Taiwan   \n",
       "2               85            392                NaN      XiJinping   \n",
       "3               85            392                NaN      XiJinping   \n",
       "4              910           2608                NaN            NaN   \n",
       "\n",
       "     user_mentions           place sentiment  \n",
       "0        i_ameztoy             NaN  negative  \n",
       "1     IndoPac_Info             NaN  negative  \n",
       "2      ZelenskyyUa     Netherlands   neutral  \n",
       "3              NaN     Netherlands  positive  \n",
       "4  ChinaUncensored  Ayent, Schweiz  negative  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score= pd.Series([text_category(row_value) for row_value in tweets_df['polarity']])\n",
    "tweets_df = pd.concat([tweets_df, score.rename('sentiment')], axis=1)\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gensim\n",
    "from gensim.models import CoherenceModel\n",
    "from gensim import corpora\n",
    "from pprint import pprint\n",
    "import string\n",
    "import re\n",
    "import emoji\n",
    "import nltk\n",
    "#nltk.download('words')\n",
    "words = set(nltk.corpus.words.words())\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "stop = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>: Extra random image ( I ): focus in one very ...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>: China ' s media the military for each area o...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>China even cut off communication , they don ' ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>to : told you my friend , will be vassal state...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>: I ’ m sorry , thought was an independent cou...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 created_at               source  \\\n",
       "0           0  2022-08-07 22:31:20+00:00  Twitter for Android   \n",
       "1           1  2022-08-07 22:31:16+00:00  Twitter for Android   \n",
       "2           2  2022-08-07 22:31:07+00:00  Twitter for Android   \n",
       "3           3  2022-08-07 22:31:06+00:00  Twitter for Android   \n",
       "4           4  2022-08-07 22:31:04+00:00   Twitter for iPhone   \n",
       "\n",
       "                                       original_text  \\\n",
       "0  RT @i_ameztoy: Extra random image (I):\\n\\nLets...   \n",
       "1  RT @IndoPac_Info: #China's media explains the ...   \n",
       "2  China even cut off communication, they don't a...   \n",
       "3  Putin to #XiJinping : I told you my friend, Ta...   \n",
       "4  RT @ChinaUncensored: I’m sorry, I thought Taiw...   \n",
       "\n",
       "                                          clean_text  subjectivity  \\\n",
       "0  : Extra random image ( I ): focus in one very ...      0.190625   \n",
       "1  : China ' s media the military for each area o...      0.100000   \n",
       "2  China even cut off communication , they don ' ...      0.000000   \n",
       "3  to : told you my friend , will be vassal state...      0.350000   \n",
       "4  : I ’ m sorry , thought was an independent cou...      0.556250   \n",
       "\n",
       "       polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0 -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1 -1.000000e-01   en             691            201           ZIisq   \n",
       "2  0.000000e+00   en               0              0       Fin21Free   \n",
       "3  1.000000e-01   en               0              0       Fin21Free   \n",
       "4 -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "\n",
       "   followers_count  friends_count possibly_sensitive       hashtags  \\\n",
       "0            20497           2621                NaN           City   \n",
       "1               65            272                NaN  China, Taiwan   \n",
       "2               85            392                NaN      XiJinping   \n",
       "3               85            392                NaN      XiJinping   \n",
       "4              910           2608                NaN            NaN   \n",
       "\n",
       "     user_mentions           place sentiment  \n",
       "0        i_ameztoy             NaN  negative  \n",
       "1     IndoPac_Info             NaN  negative  \n",
       "2      ZelenskyyUa     Netherlands   neutral  \n",
       "3              NaN     Netherlands  positive  \n",
       "4  ChinaUncensored  Ayent, Schweiz  negative  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def cleaner(tweet):\n",
    "    # remove all single characters\n",
    "    tweet= re.sub(r'\\s+[a-zA-Z]\\s+', ' ', tweet)\n",
    "\n",
    "    # Remove single characters from the start\n",
    "    tweet = re.sub(r'\\^[a-zA-Z]\\s+', ' ', tweet) \n",
    "\n",
    "    # Substituting multiple spaces with single space\n",
    "    tweet = re.sub(r'\\s+', ' ', tweet, flags=re.I)\n",
    "\n",
    "    tweet = re.sub(\"@[A-Za-z0-9]+\",\"\",tweet) #Remove @ sign\n",
    "    tweet = re.sub(r\"(?:\\@|http?\\://|https?\\://|www)\\S+\", \"\", tweet) #Remove http links\n",
    "    tweet = \" \".join(tweet.split())\n",
    "    tweet = ''.join(c for c in tweet if c not in emoji.UNICODE_EMOJI) #Remove Emojis\n",
    "    tweet = tweet.replace(\"#\", \"\").replace(\"_\", \" \") #Remove hashtag sign but keep the text\n",
    "    tweet = \" \".join(w for w in nltk.wordpunct_tokenize(tweet) \\\n",
    "         if w.lower() in words or not w.isalpha())\n",
    "    return tweet\n",
    "\n",
    "tweets_df['clean_text'] = tweets_df['original_text'].map(lambda x: cleaner(x))\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PrepareData:\n",
    "  def __init__(self,df):\n",
    "    self.df=df\n",
    "    \n",
    "  def preprocess_data(self):\n",
    "    #tweets_df = self.df.loc[self.df['lang'] ==\"en\"]\n",
    "\n",
    "    \n",
    "    #text Preprocessing\n",
    "    tweets_df['clean_text']=tweets_df['clean_text'].astype(str)\n",
    "    tweets_df['clean_text'] = tweets_df['clean_text'].apply(lambda x: x.lower())\n",
    "    tweets_df['clean_text']= tweets_df['clean_text'].apply(lambda x: x.translate(str.maketrans(' ', ' ', string.punctuation)))\n",
    "    \n",
    "    #Converting tweets to list of words For feature engineering\n",
    "    sentence_list = [tweet for tweet in tweets_df['clean_text']]\n",
    "    word_list = [sent.split() for sent in sentence_list]\n",
    "    # print(word_list)\n",
    "\n",
    "    #Create dictionary which contains Id and word \n",
    "    word_to_id = corpora.Dictionary(word_list) #generate unique tokens\n",
    "    #  we can see the word to unique integer mapping\n",
    "    # print(word_to_id.token2id)\n",
    "    # using bag of words(bow), we create a corpus that contains the word id and its frequency in each document.\n",
    "    corpus_1= [word_to_id.doc2bow(tweet) for tweet in word_list]\n",
    "    # TFIDF\n",
    "\n",
    "    return tweets_df['clean_text'],word_list, word_to_id, corpus_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "PrepareData_obj=PrepareData(tweets_df)\n",
    "tweets_df['clean_text'],word_list ,id2word,corpus=PrepareData_obj.preprocess_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Unnamed: 0</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>original_text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>lang</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>original_author</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_mentions</th>\n",
       "      <th>place</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-07 22:31:20+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @i_ameztoy: Extra random image (I):\\n\\nLets...</td>\n",
       "      <td>extrarandomimageifocusinoneveryspecificzoneoft...</td>\n",
       "      <td>0.190625</td>\n",
       "      <td>-1.250000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>20497</td>\n",
       "      <td>2621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>i_ameztoy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-07 22:31:16+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #China's media explains the ...</td>\n",
       "      <td>chinasmediathemilitaryforeachareaoftheinthestr...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>691</td>\n",
       "      <td>201</td>\n",
       "      <td>ZIisq</td>\n",
       "      <td>65</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China, Taiwan</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-07 22:31:07+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>China even cut off communication, they don't a...</td>\n",
       "      <td>chinaevencutoffcommunicationtheydontfromtheusb...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>ZelenskyyUa</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-07 22:31:06+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>Putin to #XiJinping : I told you my friend, Ta...</td>\n",
       "      <td>totoldyoumyfriendwillbevassalstatemuchlikethem...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fin21Free</td>\n",
       "      <td>85</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XiJinping</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-08-07 22:31:04+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @ChinaUncensored: I’m sorry, I thought Taiw...</td>\n",
       "      <td>imsorrythoughtwasanindependentcountrybecauseit...</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>en</td>\n",
       "      <td>1521</td>\n",
       "      <td>381</td>\n",
       "      <td>VizziniDolores</td>\n",
       "      <td>910</td>\n",
       "      <td>2608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChinaUncensored</td>\n",
       "      <td>Ayent, Schweiz</td>\n",
       "      <td>negative</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21995</th>\n",
       "      <td>21995</td>\n",
       "      <td>2022-08-06 18:03:29+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>goodofchinasmissileononaugust4th</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>VandelayT</td>\n",
       "      <td>62</td>\n",
       "      <td>471</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21996</th>\n",
       "      <td>21996</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: A good infographic of #China...</td>\n",
       "      <td>goodofchinasmissileononaugust4th</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>507</td>\n",
       "      <td>183</td>\n",
       "      <td>sashalenik</td>\n",
       "      <td>94</td>\n",
       "      <td>1751</td>\n",
       "      <td>False</td>\n",
       "      <td>China, Taiwan, ChinaTaiwanCrisis</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Gelendzhik</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21997</th>\n",
       "      <td>21997</td>\n",
       "      <td>2022-08-06 18:03:27+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>@Reuters Thanks #Pelosi smart move.</td>\n",
       "      <td>thankssmartmove</td>\n",
       "      <td>0.421429</td>\n",
       "      <td>2.071429e-01</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ZeitounRimal</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pelosi</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>🇺🇲🇷🇺🇺🇦🇫🇷🇦🇪🇮🇱🏳️‍🌈</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21998</th>\n",
       "      <td>21998</td>\n",
       "      <td>2022-08-06 18:03:26+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>peoplesdesireforunificationwithchinanearrecord...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>en</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>SazzyCowgirl1</td>\n",
       "      <td>537</td>\n",
       "      <td>317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan, China</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>Oregon, USA</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21999</th>\n",
       "      <td>21999</td>\n",
       "      <td>2022-08-06 18:03:25+00:00</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>RT @IndoPac_Info: #Taiwan people's Desire for ...</td>\n",
       "      <td>peoplesdesireforunificationwithchinanearrecord...</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>en</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>VandelayT</td>\n",
       "      <td>62</td>\n",
       "      <td>471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan, China</td>\n",
       "      <td>IndoPac_Info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                 created_at               source  \\\n",
       "0               0  2022-08-07 22:31:20+00:00  Twitter for Android   \n",
       "1               1  2022-08-07 22:31:16+00:00  Twitter for Android   \n",
       "2               2  2022-08-07 22:31:07+00:00  Twitter for Android   \n",
       "3               3  2022-08-07 22:31:06+00:00  Twitter for Android   \n",
       "4               4  2022-08-07 22:31:04+00:00   Twitter for iPhone   \n",
       "...           ...                        ...                  ...   \n",
       "21995       21995  2022-08-06 18:03:29+00:00  Twitter for Android   \n",
       "21996       21996  2022-08-06 18:03:27+00:00  Twitter for Android   \n",
       "21997       21997  2022-08-06 18:03:27+00:00  Twitter for Android   \n",
       "21998       21998  2022-08-06 18:03:26+00:00  Twitter for Android   \n",
       "21999       21999  2022-08-06 18:03:25+00:00  Twitter for Android   \n",
       "\n",
       "                                           original_text  \\\n",
       "0      RT @i_ameztoy: Extra random image (I):\\n\\nLets...   \n",
       "1      RT @IndoPac_Info: #China's media explains the ...   \n",
       "2      China even cut off communication, they don't a...   \n",
       "3      Putin to #XiJinping : I told you my friend, Ta...   \n",
       "4      RT @ChinaUncensored: I’m sorry, I thought Taiw...   \n",
       "...                                                  ...   \n",
       "21995  RT @IndoPac_Info: A good infographic of #China...   \n",
       "21996  RT @IndoPac_Info: A good infographic of #China...   \n",
       "21997                @Reuters Thanks #Pelosi smart move.   \n",
       "21998  RT @IndoPac_Info: #Taiwan people's Desire for ...   \n",
       "21999  RT @IndoPac_Info: #Taiwan people's Desire for ...   \n",
       "\n",
       "                                              clean_text  subjectivity  \\\n",
       "0      extrarandomimageifocusinoneveryspecificzoneoft...      0.190625   \n",
       "1      chinasmediathemilitaryforeachareaoftheinthestr...      0.100000   \n",
       "2      chinaevencutoffcommunicationtheydontfromtheusb...      0.000000   \n",
       "3      totoldyoumyfriendwillbevassalstatemuchlikethem...      0.350000   \n",
       "4      imsorrythoughtwasanindependentcountrybecauseit...      0.556250   \n",
       "...                                                  ...           ...   \n",
       "21995                   goodofchinasmissileononaugust4th      0.600000   \n",
       "21996                   goodofchinasmissileononaugust4th      0.600000   \n",
       "21997                                    thankssmartmove      0.421429   \n",
       "21998  peoplesdesireforunificationwithchinanearrecord...      0.350000   \n",
       "21999  peoplesdesireforunificationwithchinanearrecord...      0.350000   \n",
       "\n",
       "           polarity lang  favorite_count  retweet_count original_author  \\\n",
       "0     -1.250000e-01   en               4              2       i_ameztoy   \n",
       "1     -1.000000e-01   en             691            201           ZIisq   \n",
       "2      0.000000e+00   en               0              0       Fin21Free   \n",
       "3      1.000000e-01   en               0              0       Fin21Free   \n",
       "4     -6.938894e-18   en            1521            381  VizziniDolores   \n",
       "...             ...  ...             ...            ...             ...   \n",
       "21995  7.000000e-01   en             507            183       VandelayT   \n",
       "21996  7.000000e-01   en             507            183      sashalenik   \n",
       "21997  2.071429e-01   en               0              0    ZeitounRimal   \n",
       "21998  5.000000e-02   en             199             67   SazzyCowgirl1   \n",
       "21999  5.000000e-02   en             199             67       VandelayT   \n",
       "\n",
       "       followers_count  friends_count possibly_sensitive  \\\n",
       "0                20497           2621                NaN   \n",
       "1                   65            272                NaN   \n",
       "2                   85            392                NaN   \n",
       "3                   85            392                NaN   \n",
       "4                  910           2608                NaN   \n",
       "...                ...            ...                ...   \n",
       "21995               62            471              False   \n",
       "21996               94           1751              False   \n",
       "21997               88              0                NaN   \n",
       "21998              537            317                NaN   \n",
       "21999               62            471                NaN   \n",
       "\n",
       "                               hashtags    user_mentions             place  \\\n",
       "0                                  City        i_ameztoy               NaN   \n",
       "1                         China, Taiwan     IndoPac_Info               NaN   \n",
       "2                             XiJinping      ZelenskyyUa       Netherlands   \n",
       "3                             XiJinping              NaN       Netherlands   \n",
       "4                                   NaN  ChinaUncensored    Ayent, Schweiz   \n",
       "...                                 ...              ...               ...   \n",
       "21995  China, Taiwan, ChinaTaiwanCrisis     IndoPac_Info               NaN   \n",
       "21996  China, Taiwan, ChinaTaiwanCrisis     IndoPac_Info        Gelendzhik   \n",
       "21997                            Pelosi          Reuters  🇺🇲🇷🇺🇺🇦🇫🇷🇦🇪🇮🇱🏳️‍🌈   \n",
       "21998                     Taiwan, China     IndoPac_Info       Oregon, USA   \n",
       "21999                     Taiwan, China     IndoPac_Info               NaN   \n",
       "\n",
       "      sentiment  \n",
       "0      negative  \n",
       "1      negative  \n",
       "2       neutral  \n",
       "3      positive  \n",
       "4      negative  \n",
       "...         ...  \n",
       "21995  positive  \n",
       "21996  positive  \n",
       "21997  positive  \n",
       "21998  positive  \n",
       "21999  positive  \n",
       "\n",
       "[22000 rows x 18 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tweets_df['clean_text'] = tweets_df['clean_text'].apply(lambda x: [item for item in x if item not in stop])\n",
    "#tweets_df['clean_text'] = tweets_df['clean_text'].str.replace(r'\\b\\w\\b','').str.replace(r'\\s+', ' ')\n",
    "#print(corpus)\n",
    "\n",
    "tweets_df['clean_text'] = tweets_df['clean_text'].str.replace('\\W', '')\n",
    "\n",
    "tweets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22000 entries, 0 to 21999\n",
      "Data columns (total 18 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Unnamed: 0          22000 non-null  int64  \n",
      " 1   created_at          22000 non-null  object \n",
      " 2   source              22000 non-null  object \n",
      " 3   original_text       22000 non-null  object \n",
      " 4   clean_text          22000 non-null  object \n",
      " 5   subjectivity        22000 non-null  float64\n",
      " 6   polarity            22000 non-null  float64\n",
      " 7   lang                22000 non-null  object \n",
      " 8   favorite_count      22000 non-null  int64  \n",
      " 9   retweet_count       22000 non-null  int64  \n",
      " 10  original_author     22000 non-null  object \n",
      " 11  followers_count     22000 non-null  int64  \n",
      " 12  friends_count       22000 non-null  int64  \n",
      " 13  possibly_sensitive  6191 non-null   object \n",
      " 14  hashtags            19141 non-null  object \n",
      " 15  user_mentions       18698 non-null  object \n",
      " 16  place               12107 non-null  object \n",
      " 17  sentiment           22000 non-null  object \n",
      "dtypes: float64(2), int64(5), object(11)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "tweets_df.to_csv('data/cleaned_tweet_data_for_database.csv', index=False)\n",
    "tweets_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_words = [[(id2word[id], count) for id, count in line] for line in corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(id_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3 Topic Modelling using Latent Dirichlet Allocation(LDA)\n",
    "#### The purpose of LDA is mapping each teweets in our corpus to a set of topics which covers a good deal of the words in the tweet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=5, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.062*\"the\" + 0.061*\"on\" + 0.061*\"s\" + 0.047*\"…\" + 0.045*\"in\" + '\n",
      "  '0.037*\"china\" + 0.027*\"of\" + 0.024*\"island\" + 0.021*\"’\" + 0.017*\"military\"'),\n",
      " (1,\n",
      "  '0.072*\"the\" + 0.052*\"to\" + 0.041*\"is\" + 0.038*\"china\" + 0.028*\"…\" + '\n",
      "  '0.026*\"of\" + 0.023*\"and\" + 0.021*\"in\" + 0.020*\"s\" + 0.019*\"this\"'),\n",
      " (2,\n",
      "  '0.051*\"…\" + 0.040*\"an\" + 0.039*\"its\" + 0.033*\"was\" + 0.029*\"of\" + 0.026*\"’\" '\n",
      "  '+ 0.025*\"“\" + 0.022*\"to\" + 0.020*\"military\" + 0.018*\"”\"'),\n",
      " (3,\n",
      "  '0.072*\"missile\" + 0.061*\"of\" + 0.053*\"china\" + 0.043*\"good\" + 0.042*\"s\" + '\n",
      "  '0.041*\"4th\" + 0.038*\"video\" + 0.031*\"part\" + 0.029*\"dead\" + 0.028*\"found\"'),\n",
      " (4,\n",
      "  '0.047*\"visit\" + 0.045*\"…\" + 0.044*\"to\" + 0.040*\"and\" + 0.032*\"s\" + '\n",
      "  '0.031*\"about\" + 0.027*\"the\" + 0.027*\"us\" + 0.025*\"for\" + 0.025*\"are\"')]\n"
     ]
    }
   ],
   "source": [
    "pprint(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  [('the', 0.061636142),\n",
      "   ('on', 0.06142575),\n",
      "   ('s', 0.06123042),\n",
      "   ('…', 0.046802636),\n",
      "   ('in', 0.045378935),\n",
      "   ('china', 0.036967568),\n",
      "   ('of', 0.027478099),\n",
      "   ('island', 0.024373932),\n",
      "   ('’', 0.021451669),\n",
      "   ('military', 0.017324224)]),\n",
      " (1,\n",
      "  [('the', 0.07178997),\n",
      "   ('to', 0.052038234),\n",
      "   ('is', 0.040591836),\n",
      "   ('china', 0.038327307),\n",
      "   ('…', 0.02826326),\n",
      "   ('of', 0.025984004),\n",
      "   ('and', 0.023459306),\n",
      "   ('in', 0.020753909),\n",
      "   ('s', 0.019584186),\n",
      "   ('this', 0.018563509)]),\n",
      " (2,\n",
      "  [('…', 0.05057732),\n",
      "   ('an', 0.040242374),\n",
      "   ('its', 0.039366025),\n",
      "   ('was', 0.03344181),\n",
      "   ('of', 0.029460566),\n",
      "   ('’', 0.025647646),\n",
      "   ('“', 0.025218138),\n",
      "   ('to', 0.021728719),\n",
      "   ('military', 0.019927686),\n",
      "   ('”', 0.01795085)]),\n",
      " (3,\n",
      "  [('missile', 0.07198438),\n",
      "   ('of', 0.061110746),\n",
      "   ('china', 0.053099047),\n",
      "   ('good', 0.043407246),\n",
      "   ('s', 0.04176584),\n",
      "   ('4th', 0.041226592),\n",
      "   ('video', 0.038430456),\n",
      "   ('part', 0.030996826),\n",
      "   ('dead', 0.028903106),\n",
      "   ('found', 0.02777241)]),\n",
      " (4,\n",
      "  [('visit', 0.047259122),\n",
      "   ('…', 0.04543724),\n",
      "   ('to', 0.0435242),\n",
      "   ('and', 0.039556928),\n",
      "   ('s', 0.031579293),\n",
      "   ('about', 0.031041864),\n",
      "   ('the', 0.027299797),\n",
      "   ('us', 0.026593724),\n",
      "   ('for', 0.024942158),\n",
      "   ('are', 0.024534674)])]\n"
     ]
    }
   ],
   "source": [
    "pprint(lda_model.show_topics(formatted=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -6.3986978132653825\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "\n",
    "#It's a measure of how good the model is. The lower the better. Perplexity is a negative value\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  \n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Compute Coherence Score\n",
    "# coherence_model_lda = CoherenceModel(model=lda_model, texts=word_list, dictionary=id2word, coherence='c_v')\n",
    "# coherence_lda = coherence_model_lda.get_coherence()\n",
    "# print('\\n Ldamodel Coherence Score/Accuracy on Tweets: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install pyLDAvis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el582822844002705766415519427\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el582822844002705766415519427_data = {\"mdsDat\": {\"x\": [-0.020693044406476343, -0.054290641976484866, -0.13692332749338362, -0.15075669516880946, 0.3626637090451544], \"y\": [0.10380978626912146, 0.07832181087786687, 0.16260722420832285, -0.2993438953080708, -0.0453949260472404], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [39.231831328912, 23.385693538075206, 16.16352245545282, 13.599851907347475, 7.619100770212493]}, \"tinfo\": {\"Term\": [\"on\", \"missile\", \"visit\", \"china\", \"of\", \"is\", \"an\", \"its\", \"s\", \"was\", \"about\", \"in\", \"good\", \"and\", \"to\", \"island\", \"us\", \"4th\", \"\\u2026\", \"video\", \"are\", \"military\", \"\\u201c\", \"this\", \"t\", \"part\", \"it\", \"dead\", \"for\", \"august\", \"this\", \"time\", \"no\", \"president\", \"how\", \"your\", \"russia\", \"when\", \"attack\", \"our\", \"speech\", \"based\", \"democratic\", \"did\", \"why\", \"thing\", \"like\", \"or\", \"respond\", \"one\", \"forget\", \"million\", \"easily\", \"23\", \"unless\", \"between\", \"long\", \"small\", \"into\", \"than\", \"watch\", \"air\", \"world\", \"is\", \"you\", \"there\", \"by\", \"be\", \"not\", \"to\", \"so\", \"the\", \"it\", \"can\", \"china\", \"and\", \"but\", \"will\", \"that\", \"of\", \"in\", \"\\u2026\", \"us\", \"with\", \"s\", \"on\", \"for\", \"\\u2019\", \"as\", \"island\", \"august\", \"near\", \"said\", \"around\", \"what\", \"army\", \"strait\", \"fired\", \"official\", \"national\", \"day\", \"restricted\", \"live\", \"check\", \"u\", \"foreign\", \"today\", \"crisis\", \"trip\", \"new\", \"would\", \"funny\", \"defense\", \"political\", \"rise\", \"fire\", \"record\", \"desire\", \"low\", \"3\", \"tension\", \"just\", \"on\", \"in\", \"s\", \"military\", \"the\", \"\\u2026\", \"china\", \"\\u2019\", \"now\", \"of\", \"from\", \"they\", \"after\", \"for\", \"with\", \"as\", \"visit\", \"are\", \"over\", \"out\", \"nancy\", \"support\", \"japan\", \"speaker\", \"should\", \"do\", \"5\", \"house\", \"where\", \"responsible\", \"democracy\", \"about\", \"high\", \"talking\", \"must\", \"showing\", \"aircraft\", \"stopped\", \"firm\", \"meanwhile\", \"2\", \"practice\", \"amphibious\", \"coincidence\", \"could\", \"independence\", \"stop\", \"their\", \"have\", \"us\", \"and\", \"we\", \"for\", \"to\", \"\\u2026\", \"s\", \"the\", \"war\", \"that\", \"in\", \"\\u2019\", \"an\", \"its\", \"\\u201c\", \"\\u201d\", \"m\", \"i\", \"his\", \"own\", \"d\", \"she\", \"amid\", \"delegation\", \"because\", \"independent\", \"thought\", \"against\", \"cheating\", \"father\", \"pain\", \"teen\", \"currency\", \"sorry\", \"state\", \"deputy\", \"prime\", \"until\", \"via\", \"make\", \"suspension\", \"want\", \"during\", \"had\", \"was\", \"her\", \"minister\", \"government\", \"country\", \"\\u2026\", \"military\", \"\\u2019\", \"we\", \"of\", \"war\", \"it\", \"to\", \"s\", \"as\", \"the\", \"missile\", \"4th\", \"video\", \"part\", \"dead\", \"found\", \"head\", \"again\", \"matter\", \"know\", \"don\", \"\\ud83e\\udd17\", \"x\", \"true\", \"king\", \"go\", \"production\", \"room\", \"territory\", \"development\", \"international\", \"ballistic\", \"whatever\", \"provocative\", \"interesting\", \"bunch\", \"quick\", \"shipping\", \"secure\", \"unelected\", \"hotel\", \"\\ud83c\\udf81\", \"maybe\", \"good\", \"leading\", \"t\", \"of\", \"china\", \"s\", \"that\", \"is\", \"by\", \"and\"], \"Freq\": [7178.0, 1942.0, 2705.0, 10305.0, 9358.0, 6206.0, 1938.0, 1896.0, 11444.0, 1860.0, 1779.0, 7130.0, 1275.0, 5972.0, 11280.0, 2018.0, 3042.0, 1112.0, 12927.0, 1037.0, 1404.0, 2413.0, 1215.0, 2579.0, 1116.0, 837.0, 2578.0, 780.0, 3712.0, 1341.0, 2578.491510738965, 965.0764235814082, 829.7910419822599, 808.2130552137946, 812.4266249831487, 769.3903757131202, 756.3642978571362, 734.9634072760172, 616.9418639428452, 557.3336675376089, 436.8161869416848, 433.1383040326677, 437.29123549828284, 456.33295648945614, 427.8098303256623, 421.8849991141524, 413.1101352864836, 409.22294512317404, 378.39475490658396, 376.75338113076407, 378.1157863689562, 366.37831289296054, 371.21336867988856, 364.8313408284014, 353.23425715322213, 333.5374160986676, 339.07393578324775, 339.76091116885976, 321.3355017266747, 313.50772735233653, 662.7292487981879, 323.2792908509015, 870.0248637471183, 5638.249995698838, 1289.4117264493698, 390.67577396708117, 2129.427065591017, 997.9935467660546, 1532.0470860939245, 7228.167044838339, 751.765488901575, 9971.704957928096, 1894.4821305202777, 435.481109365711, 5323.70439930992, 3258.5230182323785, 553.3807616232606, 759.0376870519499, 1690.5612393020983, 3609.2063270536964, 2882.7404320891533, 3925.7973908745507, 1520.122264514697, 1287.7842267174453, 2720.26470573175, 2092.1425615583057, 1400.957487028367, 1268.4967831493075, 886.0604125325586, 2018.1010010845923, 1340.284802237546, 1106.1759839062204, 1011.3514986150852, 932.451014966626, 920.3722401971975, 745.1654254142792, 692.5812192218259, 683.978771366522, 648.907751444161, 618.8438698883712, 569.8395509455571, 550.5810684328217, 537.3002843484255, 538.004271007458, 496.06595935023194, 504.10156280764477, 476.66667179457056, 432.4780241199785, 390.4801440642529, 399.06605156812884, 384.2509860274899, 379.280976701537, 352.34233478690226, 339.89998073083837, 331.4744259579398, 330.3990717815296, 321.5028688270872, 320.21123702518594, 319.7807830542802, 912.1688427784445, 364.88774795571015, 870.6446953520815, 5085.899433178191, 3757.263048128162, 5069.726747930128, 1434.4027176446004, 5103.319469124157, 3875.142007306973, 3060.8227991291, 1776.1448848351886, 697.1010919442298, 2275.1183218105716, 870.9468941271236, 839.9080957270218, 622.1875269906916, 883.9907926190397, 618.2129460870455, 563.041929175798, 2704.5107556922335, 1404.052544830933, 901.0893250062562, 887.6641172285014, 833.8312693180922, 830.8807977809823, 806.0801335729627, 772.8775087985287, 745.5509906717839, 800.4537769139455, 510.0248128142422, 488.50095799664837, 471.900951387221, 451.42928698285044, 444.22856203736757, 1776.441290104341, 420.97958821991523, 397.970211302607, 398.4352014383666, 400.3852872974195, 398.67354585777366, 351.14864396407097, 347.46182034768395, 342.35518989748783, 344.68840161944496, 329.77735195965977, 321.7272318862498, 311.42629733426446, 312.2376117180802, 310.3783173881999, 433.500957930471, 693.0105962879, 1065.2706824762347, 1521.8863589485572, 2263.735176452794, 857.2229193454623, 1427.3717108667124, 2490.771240418854, 2600.249355811756, 1807.1968873009048, 1562.2929864372115, 512.0525524789919, 613.0548375905199, 490.02935757521186, 435.6371038002909, 1937.6923448342009, 1895.495686263592, 1214.2671785594878, 864.3432397123091, 820.6088909422191, 747.8573998434947, 721.6742891633529, 585.3602278984117, 527.3615939763017, 509.6185145718773, 467.74872167242665, 453.0300802328261, 446.1111469905412, 435.1708108223692, 402.16231084150337, 411.3488550583337, 389.8008332294189, 372.6781887259838, 369.9803922138582, 369.931983448319, 350.1190089074802, 348.3323016861933, 331.83760340216133, 325.8942895235368, 284.387013030403, 287.4956898706927, 268.6053742932346, 251.8744618614696, 231.9683469722983, 220.53589612786183, 225.08737391531122, 473.457458198077, 1610.241553182379, 720.6335119151827, 840.2753611412176, 525.8443973440752, 528.452371941078, 2435.325665213762, 959.5289985254061, 1234.9481924468228, 724.7601629862487, 1418.5423932101417, 518.2431673276549, 683.7603825557434, 1046.2497032826366, 720.413463918424, 514.8688140339729, 648.3203230622946, 1941.820317672167, 1112.111172880414, 1036.6837905227142, 836.1573055741381, 779.6780154160682, 749.1768261178613, 634.9834018081119, 556.8919802031312, 321.84525957211815, 312.2060299750298, 291.6002742747013, 273.27877395423434, 258.79000810685244, 240.82404799222783, 224.97507455406617, 218.15481753166335, 204.52736742884744, 176.7492940309768, 159.75588159007077, 155.103033035618, 154.15346031372553, 152.59957978294872, 145.50270635039703, 125.17738575095284, 106.83912841258761, 95.88253844214917, 93.33709215287267, 90.60094999499482, 89.18786484286355, 89.16278584268817, 722.6141138539599, 149.40093639418802, 497.46375637054365, 1170.9355913634245, 117.06349699598695, 606.3926130421734, 1648.4977385068835, 1432.377529448252, 1126.657671302204, 448.16471020558834, 481.9353017245058, 207.5894046934372, 217.12889611802734], \"Total\": [7178.0, 1942.0, 2705.0, 10305.0, 9358.0, 6206.0, 1938.0, 1896.0, 11444.0, 1860.0, 1779.0, 7130.0, 1275.0, 5972.0, 11280.0, 2018.0, 3042.0, 1112.0, 12927.0, 1037.0, 1404.0, 2413.0, 1215.0, 2579.0, 1116.0, 837.0, 2578.0, 780.0, 3712.0, 1341.0, 2579.388239073639, 966.0167647150064, 830.6869513354474, 809.1040807957849, 813.3334650907432, 770.287971553867, 757.2573698789821, 735.8511378036686, 617.8378050549431, 558.2321704827042, 437.6949803073785, 434.0168905012754, 438.1792361787991, 457.2799607534855, 428.7008016505976, 422.7803873039466, 413.9996135302951, 410.11512091030846, 379.27562496120663, 377.6470945199939, 379.0151578654628, 367.26261957648666, 372.1122575883053, 365.71530725732373, 354.11816664298397, 334.4209986332176, 339.9823010250359, 340.7002527792141, 322.2350659862117, 314.39489810236654, 664.8838307542427, 324.2011942579422, 889.5128203392686, 6206.894205753203, 1366.3864953748073, 396.5960643836887, 2578.5951039607125, 1118.729121024383, 1859.2628404598458, 11280.897544381694, 841.3895550281478, 17294.214788370726, 2578.9061335205565, 449.41772328911617, 10305.280657329069, 5972.587778582271, 610.4623719801659, 940.7071175151466, 2890.619682474943, 9358.312400727671, 7130.488810279125, 12927.31997691271, 3042.6628887813154, 2717.4560889421796, 11444.259476183412, 7178.734772402988, 3712.771733511101, 4715.454136535984, 2397.0686936271895, 2018.9760504681567, 1341.1984603702062, 1107.0511280886794, 1012.2311672929114, 933.3345319504787, 921.2763987592016, 746.0386768093892, 693.4568790924827, 684.8526586650524, 649.8294559627578, 619.7329715047384, 570.7179824345343, 551.4512884079618, 538.1767233072283, 538.8962237286715, 496.9612883526424, 505.0128016357531, 477.54873886005885, 433.36905635534646, 391.38239602357476, 399.9899487156661, 385.16652184148535, 380.1905946018756, 353.23804856736007, 340.7898252286539, 332.3477081042659, 331.27344945729556, 322.3735479981694, 321.08678698410864, 320.6552253200287, 921.3604918127172, 366.3220344366142, 893.8280217445252, 7178.734772402988, 7130.488810279125, 11444.259476183412, 2413.848996421166, 17294.214788370726, 12927.31997691271, 10305.280657329069, 4715.454136535984, 1029.2654767287017, 9358.312400727671, 1615.392996748021, 1553.5330605165282, 952.0763328978746, 3712.771733511101, 2717.4560889421796, 2397.0686936271895, 2705.3753711531936, 1404.9176700532214, 901.9536631604605, 888.538620085315, 834.6881854349358, 831.7359092240218, 806.9439940824459, 773.731651230578, 746.4076149219699, 801.3904195973323, 510.91592010195865, 489.3568868424143, 472.757910398147, 452.28010263780874, 445.09106825228633, 1779.9001808352637, 421.8387028417755, 398.8222865967965, 399.2886909859304, 401.252287105402, 399.5408036775558, 351.9974402952525, 348.3205907811215, 343.21321255921396, 345.55243937604126, 330.63523898081235, 322.5844252032036, 312.2784407315064, 313.1005369145702, 311.23945671535927, 442.69450212733364, 889.6749956313895, 1616.9090138448896, 3042.6628887813154, 5972.587778582271, 1582.6085125922386, 3712.771733511101, 11280.897544381694, 12927.31997691271, 11444.259476183412, 17294.214788370726, 1030.9271610606202, 2890.619682474943, 7130.488810279125, 4715.454136535984, 1938.554011388487, 1896.3602606050413, 1215.1296326549825, 865.1985913004935, 821.4626113347397, 748.7127288660554, 722.5304844706186, 586.2087326231551, 528.211759704894, 510.4719871325048, 468.60903832473656, 453.8845722817072, 446.95928610751685, 436.01846637364605, 403.0086964221586, 412.2148954534975, 390.64940990601843, 373.5257481285854, 370.82650744406925, 370.77803993590084, 350.9637875405749, 349.17703695148253, 332.6967931590466, 326.7537729997849, 285.23921733193936, 288.3602777564669, 269.46912946047036, 252.74241314742588, 232.82873475409016, 221.39827852057294, 225.97875185345842, 495.76661592328594, 1860.4888760126883, 799.6313161813464, 1002.7296467538274, 713.0503734219756, 759.9765830285402, 12927.31997691271, 2413.848996421166, 4715.454136535984, 1582.6085125922386, 9358.312400727671, 1030.9271610606202, 2578.9061335205565, 11280.897544381694, 11444.259476183412, 2397.0686936271895, 17294.214788370726, 1942.7035300151747, 1112.9944499065043, 1037.582472801716, 837.0590907125198, 780.5625838549128, 750.059213794443, 635.8658179609139, 557.776633229995, 322.7355160503759, 313.1172264927676, 292.49090825024257, 274.1637284798427, 259.67174061426437, 241.70685569228485, 225.85568128348783, 219.05297574612732, 205.40961908365082, 177.63284432538597, 160.6506354882912, 155.98627287832397, 155.052492128158, 153.5050721654624, 146.39632874026006, 126.07861269950102, 107.72212357427854, 96.76928510806118, 94.22005759800051, 91.4936758720918, 90.07155957600065, 90.04665792395494, 756.2243751460089, 151.94896523574803, 522.4496817611758, 1275.3557172757044, 125.01684092253154, 1116.1394107729566, 9358.312400727671, 10305.280657329069, 11444.259476183412, 2890.619682474943, 6206.894205753203, 2578.5951039607125, 5972.587778582271], \"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\"], \"logprob\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, -3.9866, -4.9693, -5.1203, -5.1467, -5.1415, -5.1959, -5.213, -5.2417, -5.4167, -5.5184, -5.762, -5.7705, -5.7609, -5.7183, -5.7828, -5.7968, -5.8178, -5.8273, -5.9056, -5.9099, -5.9063, -5.9379, -5.9247, -5.9421, -5.9744, -6.0318, -6.0153, -6.0133, -6.069, -6.0937, -5.3452, -6.063, -5.073, -3.2042, -4.6796, -5.8736, -4.1779, -4.9358, -4.5072, -2.9558, -5.2191, -2.634, -4.2948, -5.7651, -3.2616, -3.7525, -5.5255, -5.2095, -4.4087, -3.6503, -3.875, -3.5662, -4.515, -4.6808, -3.933, -4.1956, -4.5966, -4.6959, -5.0547, -3.7142, -4.1235, -4.3155, -4.4051, -4.4863, -4.4994, -4.7105, -4.7837, -4.7962, -4.8489, -4.8963, -4.9788, -5.0132, -5.0376, -5.0363, -5.1174, -5.1014, -5.1573, -5.2546, -5.3568, -5.335, -5.3729, -5.3859, -5.4596, -5.4955, -5.5206, -5.5239, -5.5511, -5.5552, -5.5565, -4.5083, -5.4246, -4.5549, -2.7899, -3.0927, -2.7931, -4.0556, -2.7865, -3.0618, -3.2977, -3.842, -4.7772, -3.5944, -4.5546, -4.5909, -4.8909, -4.5397, -4.8973, -4.9908, -3.0521, -3.7077, -4.1512, -4.1662, -4.2288, -4.2323, -4.2626, -4.3047, -4.3407, -4.2696, -4.7203, -4.7634, -4.798, -4.8424, -4.8584, -3.4724, -4.9122, -4.9684, -4.9672, -4.9624, -4.9666, -5.0936, -5.1041, -5.1189, -5.1121, -5.1564, -5.1811, -5.2136, -5.211, -5.217, -4.8829, -4.4137, -3.9838, -3.6271, -3.23, -4.2011, -3.6912, -3.1344, -3.0914, -3.4553, -3.6009, -4.7164, -4.5363, -4.7603, -4.878, -3.2128, -3.2349, -3.6802, -4.0201, -4.072, -4.1649, -4.2005, -4.4099, -4.5142, -4.5484, -4.6342, -4.6661, -4.6815, -4.7063, -4.7852, -4.7626, -4.8165, -4.8614, -4.8686, -4.8688, -4.9238, -4.9289, -4.9774, -4.9955, -5.1318, -5.1209, -5.1888, -5.2532, -5.3355, -5.386, -5.3656, -4.622, -3.3979, -4.202, -4.0484, -4.5171, -4.5121, -2.9843, -3.9156, -3.6633, -4.1962, -3.5247, -4.5316, -4.2545, -3.8291, -4.2023, -4.5382, -4.3077, -2.6313, -3.1887, -3.2589, -3.4739, -3.5438, -3.5837, -3.7491, -3.8803, -4.4286, -4.459, -4.5273, -4.5922, -4.6467, -4.7186, -4.7867, -4.8175, -4.882, -5.028, -5.129, -5.1586, -5.1647, -5.1749, -5.2225, -5.373, -5.5314, -5.6396, -5.6665, -5.6962, -5.7119, -5.7122, -3.6198, -5.1961, -3.9932, -3.1371, -5.44, -3.7952, -2.7951, -2.9356, -3.1757, -4.0975, -4.0249, -4.8671, -4.8222], \"loglift\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.9353, 0.9347, 0.9346, 0.9346, 0.9346, 0.9345, 0.9345, 0.9345, 0.9342, 0.9341, 0.9337, 0.9337, 0.9337, 0.9336, 0.9336, 0.9336, 0.9335, 0.9335, 0.9334, 0.9333, 0.9333, 0.9333, 0.9333, 0.9333, 0.9332, 0.933, 0.933, 0.9329, 0.9329, 0.9329, 0.9324, 0.9328, 0.9135, 0.8396, 0.8777, 0.9206, 0.7443, 0.8215, 0.7421, 0.4906, 0.8231, 0.3851, 0.6273, 0.9042, 0.2752, 0.3298, 0.8375, 0.7211, 0.3993, -0.0171, 0.03, -0.2561, 0.2417, 0.1889, -0.5011, -0.2973, -0.0389, -0.3773, -0.0595, 1.4526, 1.4524, 1.4523, 1.4522, 1.4521, 1.4521, 1.4519, 1.4518, 1.4518, 1.4516, 1.4516, 1.4515, 1.4515, 1.4514, 1.4514, 1.4512, 1.4512, 1.4512, 1.451, 1.4507, 1.4507, 1.4507, 1.4507, 1.4505, 1.4504, 1.4504, 1.4504, 1.4503, 1.4503, 1.4503, 1.443, 1.4491, 1.4268, 1.1084, 0.8124, 0.6388, 0.9326, 0.2326, 0.2483, 0.2391, 0.4766, 1.0634, 0.0388, 0.8353, 0.8381, 1.0276, 0.018, -0.0276, 0.0044, 1.8221, 1.8218, 1.8215, 1.8214, 1.8214, 1.8214, 1.8213, 1.8213, 1.8213, 1.8212, 1.8207, 1.8207, 1.8206, 1.8205, 1.8205, 1.8205, 1.8204, 1.8203, 1.8203, 1.8203, 1.8202, 1.82, 1.8199, 1.8199, 1.8199, 1.8198, 1.8198, 1.8197, 1.8197, 1.8196, 1.8014, 1.5726, 1.4051, 1.1296, 0.8522, 1.2093, 0.8665, 0.3119, 0.2187, -0.0233, -0.5818, 1.1226, 0.2716, -0.8553, -0.5594, 1.9947, 1.9947, 1.9944, 1.9941, 1.9941, 1.994, 1.9939, 1.9937, 1.9935, 1.9934, 1.9933, 1.9932, 1.9932, 1.9932, 1.993, 1.993, 1.9929, 1.9928, 1.9928, 1.9928, 1.9927, 1.9927, 1.9925, 1.9925, 1.9921, 1.9921, 1.9919, 1.9917, 1.9914, 1.9912, 1.9912, 1.9491, 1.8507, 1.8911, 1.8184, 1.6906, 1.6318, 0.3258, 1.0726, 0.6553, 1.2141, 0.1085, 1.3073, 0.6676, -0.3828, -0.7703, 0.457, -1.2886, 2.5741, 2.5737, 2.5736, 2.5734, 2.5734, 2.5733, 2.5731, 2.5729, 2.5717, 2.5716, 2.5715, 2.5713, 2.5711, 2.5709, 2.5706, 2.5704, 2.5702, 2.5695, 2.5689, 2.5688, 2.5687, 2.5686, 2.5684, 2.5673, 2.5663, 2.5653, 2.5651, 2.5647, 2.5647, 2.5646, 2.529, 2.5576, 2.5255, 2.4891, 2.5088, 1.9644, 0.8381, 0.6012, 0.2563, 0.7104, 0.0189, 0.0551, -0.7399]}, \"token.table\": {\"Topic\": [3, 1, 2, 3, 5, 3, 3, 4, 2, 4, 5, 4, 1, 3, 4, 3, 4, 1, 3, 4, 5, 3, 2, 2, 1, 2, 3, 4, 1, 2, 5, 1, 1, 3, 4, 1, 5, 1, 4, 1, 3, 5, 1, 2, 4, 2, 1, 2, 3, 4, 5, 3, 3, 1, 2, 4, 2, 4, 4, 2, 5, 2, 4, 3, 1, 4, 2, 5, 1, 3, 5, 4, 1, 4, 2, 2, 3, 1, 2, 3, 2, 1, 5, 1, 2, 2, 5, 1, 5, 1, 3, 4, 2, 4, 1, 3, 5, 3, 4, 3, 4, 2, 5, 3, 1, 4, 1, 2, 3, 3, 4, 5, 5, 1, 1, 4, 5, 2, 1, 4, 4, 3, 2, 3, 5, 5, 2, 5, 1, 2, 1, 2, 4, 4, 5, 2, 5, 3, 1, 2, 4, 1, 1, 4, 5, 3, 3, 2, 2, 2, 1, 1, 3, 5, 1, 2, 3, 1, 2, 3, 4, 5, 2, 1, 2, 1, 1, 1, 3, 3, 4, 4, 5, 2, 3, 1, 4, 5, 5, 5, 2, 1, 3, 2, 2, 5, 1, 1, 2, 3, 4, 5, 2, 5, 4, 5, 3, 3, 1, 1, 2, 4, 3, 1, 4, 1, 3, 3, 2, 3, 4, 1, 5, 3, 4, 2, 3, 5, 1, 1, 2, 3, 5, 1, 2, 3, 4, 5, 1, 3, 4, 1, 3, 1, 2, 4, 1, 1, 4, 1, 1, 2, 3, 4, 2, 2, 5, 2, 5, 1, 4, 1, 3, 4, 5, 3, 4, 3, 4, 1, 4, 1, 5, 3, 4, 2, 5, 1, 3, 1, 1, 2, 1, 2, 3, 4, 1, 3, 2, 5, 1, 5, 1, 1, 2, 3, 4, 4, 4, 1, 2, 3, 4, 5, 2, 5, 5], \"Freq\": [0.9984012864240264, 0.998044087181671, 0.9898405760873238, 0.009768163579809117, 0.9991065095548429, 0.9982072977843872, 0.9978087642906843, 0.0016854877775180478, 0.6533089611699437, 0.3455605276927837, 0.9986076268102203, 0.9970527618800361, 0.9962949110638177, 0.9986464369281485, 0.998700327405306, 0.9981883030997686, 0.9997142141074057, 0.5456596237374342, 0.379065169727386, 0.039011565612403244, 0.03633265981927684, 0.9993468157794708, 0.9986077440196112, 0.9985701461750378, 0.36961810996718875, 0.23487019854574184, 0.18063729302008208, 0.21484574112088284, 0.9986439724987877, 0.9991064257784225, 0.9967097363081399, 0.9976570255132216, 0.8920836878601718, 0.1072645716865938, 0.9978537505823605, 0.9987411118472278, 0.9920503173377572, 0.905870738938791, 0.0917337457153206, 0.8256433888088378, 0.0934617457505542, 0.08066407931998039, 0.9679191038938154, 0.028926318047401377, 0.9983376145219964, 0.998336927057179, 0.5166283361932114, 0.2970321820224305, 0.016011208766318532, 0.03134315413042961, 0.1389578845658675, 0.99590608711728, 0.9964850366421745, 0.30264090385974773, 0.0013158300167815118, 0.6947582488606382, 0.9968408996090762, 0.9972538832358495, 0.9977059206224962, 0.9987419663360324, 0.9992792584905437, 0.9964951437921785, 0.9980511074054348, 0.9975486628914605, 0.9973087812442168, 0.9976931467604341, 0.9966152858723445, 0.9936771815870407, 0.9972009253338449, 0.9982649909914934, 0.9983216290271061, 0.9956688323772445, 0.9970109622415722, 0.9985924715197828, 0.9961558964070868, 0.9987549750238037, 0.9962086916017223, 0.3773461178220886, 0.23809705078852697, 0.38434897225704523, 0.9979945030453237, 0.9973215903258857, 0.9985878264342829, 0.4605690389259832, 0.5391876786351228, 0.996868426997458, 0.9951930543625772, 0.08154587664542334, 0.9181752072287571, 0.24402203054037064, 0.018231531017384013, 0.7376757934726147, 0.04437571892376925, 0.9540779568610388, 0.34077365843224716, 0.6586641492383725, 0.9986383637294888, 0.09754495405769048, 0.9016655368666004, 0.9980117925734991, 0.9992657964168705, 0.04363784226556898, 0.9560654532729204, 0.9992707023196973, 0.9983604940064843, 0.9990480609737531, 0.40432010717749717, 0.526892349173034, 0.06871899150779522, 0.9960176748525403, 0.9976641668823878, 0.9932964227744674, 0.9932120270128385, 0.9961671893701831, 0.9083447877642425, 0.013855560792430801, 0.07765558490641448, 0.9995165616412685, 0.7344199059367985, 0.2652287305495091, 0.9992826992669593, 0.9988301615857254, 0.9744603870216882, 0.02573201940470589, 0.9962113802999102, 0.9964319226211803, 0.05599245628305109, 0.9358739121595683, 0.9975854723105388, 0.9978135001826222, 0.9971107289347878, 0.9979566048880858, 0.9994368443209022, 0.9970625699969367, 0.9977209943938705, 0.0459374382602667, 0.9512877839730229, 0.9964651344563122, 0.007871246307523746, 0.5940719581573185, 0.39770507659067345, 0.9965620798056097, 0.1615590009973759, 0.8377133385049121, 0.9996378603300478, 0.9967725332196403, 0.9991755179395798, 0.9988172785079374, 0.9990505153176672, 0.9975250660201719, 0.999173032230321, 0.8239824766363292, 0.11402368475646331, 0.061314622935079326, 0.15642222890026755, 0.6771819474750713, 0.16613789529158854, 0.38564645477312515, 0.24309938614820165, 0.04349074732409585, 0.1516299028326585, 0.17610012675702694, 0.9987235790019261, 0.2914162545804336, 0.7084813913939222, 0.9982865099999871, 0.9972809563621227, 0.9977927275641625, 0.9993938135347867, 0.9989426694525317, 0.9979380508070115, 0.997771174855417, 0.9987347479714743, 0.9976823685151868, 0.998078731768669, 0.9986354279727536, 0.9956555155930846, 0.9980058427376567, 0.9914449193530404, 0.9870509780071881, 0.998841257291459, 0.996636680879935, 0.9971696684635409, 0.9991816350465611, 0.9959448852168913, 0.9964373462138187, 0.9983395739295571, 0.23767374426109245, 0.44301686889843334, 0.15789575583815957, 0.06291363818675977, 0.09847731977288647, 0.9987837093613665, 0.9881032416775631, 0.9990753907277927, 0.9946042623450614, 0.9994538976909922, 0.9968790530405799, 0.9979446661001807, 0.8937596093343976, 0.10577740057282099, 0.996629111233205, 0.9990543863244907, 0.9984121812251755, 0.997905621053842, 0.020330046921186524, 0.9803600404216612, 0.9971663421915345, 0.9993411571703195, 0.9991152128748314, 0.9964405821516599, 0.45603622189767834, 0.5429429282318136, 0.9979382130226142, 0.9979016018962845, 0.9963910594713544, 0.002729838519099601, 0.9959499974194711, 0.9987439423961709, 0.5849956707387286, 0.048086574945407023, 0.21206525497506837, 0.15498406888879385, 0.576609006076734, 0.295069771160206, 0.09031922056677283, 0.03746917729018488, 0.0005204052401414568, 0.2169331508109101, 0.7789361321863248, 0.0033720178882524886, 0.9858897631967554, 0.012607285974383061, 0.45766647525582904, 0.5407030087410638, 0.001931082174075228, 0.9981541544324629, 0.9994617952223673, 0.9974970852214514, 0.9989474668016695, 0.6407291593211758, 0.04574104125757149, 0.2208157631252143, 0.09272311851825539, 0.9988509259570684, 0.9964679146593719, 0.9970755662256235, 0.9980656675375482, 0.9883764933859195, 0.9968423911894041, 0.9952827145019754, 0.49956240818016123, 0.500219727138293, 0.998259060466742, 0.9994386250568177, 0.9998612498815521, 0.99820107670559, 0.49664032468914027, 0.5024603284940912, 0.1343732839380304, 0.8653639485609158, 0.9971666768432228, 0.0015040221370184357, 0.5415110516474312, 0.45810444859321775, 0.9986145322284162, 0.9972927685846328, 0.9988433288202706, 0.9983968319059777, 0.998365289619475, 0.8068398610662996, 0.19240845171673282, 0.4739727001444862, 0.22741857817491654, 0.14756448195492156, 0.1512443942231241, 0.978063474867258, 0.021360006922388392, 0.9969713830892981, 0.9974131162186716, 0.9433641245454641, 0.05562115862331674, 0.9983279350042701, 0.26890305011671356, 0.3766339250846083, 0.09246193205905924, 0.26190478461683064, 0.9990703603758602, 0.9986146633702999, 0.30369790544455944, 0.2997527721848364, 0.2011244406917612, 0.18836077426324557, 0.007039355424211642, 0.013162314049964147, 0.9805923967223289, 0.995755352152908], \"Term\": [\"2\", \"23\", \"3\", \"3\", \"4th\", \"5\", \"about\", \"about\", \"after\", \"after\", \"again\", \"against\", \"air\", \"aircraft\", \"amid\", \"amphibious\", \"an\", \"and\", \"and\", \"and\", \"and\", \"are\", \"army\", \"around\", \"as\", \"as\", \"as\", \"as\", \"attack\", \"august\", \"ballistic\", \"based\", \"be\", \"be\", \"because\", \"between\", \"bunch\", \"but\", \"but\", \"by\", \"by\", \"by\", \"can\", \"can\", \"cheating\", \"check\", \"china\", \"china\", \"china\", \"china\", \"china\", \"coincidence\", \"could\", \"country\", \"country\", \"country\", \"crisis\", \"currency\", \"d\", \"day\", \"dead\", \"defense\", \"delegation\", \"democracy\", \"democratic\", \"deputy\", \"desire\", \"development\", \"did\", \"do\", \"don\", \"during\", \"easily\", \"father\", \"fire\", \"fired\", \"firm\", \"for\", \"for\", \"for\", \"foreign\", \"forget\", \"found\", \"from\", \"from\", \"funny\", \"go\", \"good\", \"good\", \"government\", \"government\", \"government\", \"had\", \"had\", \"have\", \"have\", \"head\", \"her\", \"her\", \"high\", \"his\", \"hotel\", \"hotel\", \"house\", \"how\", \"i\", \"in\", \"in\", \"in\", \"independence\", \"independent\", \"interesting\", \"international\", \"into\", \"is\", \"is\", \"is\", \"island\", \"it\", \"it\", \"its\", \"japan\", \"just\", \"just\", \"king\", \"know\", \"leading\", \"leading\", \"like\", \"live\", \"long\", \"low\", \"m\", \"make\", \"matter\", \"maybe\", \"maybe\", \"meanwhile\", \"military\", \"military\", \"military\", \"million\", \"minister\", \"minister\", \"missile\", \"must\", \"nancy\", \"national\", \"near\", \"new\", \"no\", \"not\", \"not\", \"not\", \"now\", \"now\", \"now\", \"of\", \"of\", \"of\", \"of\", \"of\", \"official\", \"on\", \"on\", \"one\", \"or\", \"our\", \"out\", \"over\", \"own\", \"pain\", \"part\", \"political\", \"practice\", \"president\", \"prime\", \"production\", \"provocative\", \"quick\", \"record\", \"respond\", \"responsible\", \"restricted\", \"rise\", \"room\", \"russia\", \"s\", \"s\", \"s\", \"s\", \"s\", \"said\", \"secure\", \"she\", \"shipping\", \"should\", \"showing\", \"small\", \"so\", \"so\", \"sorry\", \"speaker\", \"speech\", \"state\", \"stop\", \"stop\", \"stopped\", \"strait\", \"support\", \"suspension\", \"t\", \"t\", \"talking\", \"teen\", \"tension\", \"tension\", \"territory\", \"than\", \"that\", \"that\", \"that\", \"that\", \"the\", \"the\", \"the\", \"the\", \"the\", \"their\", \"their\", \"their\", \"there\", \"there\", \"they\", \"they\", \"they\", \"thing\", \"this\", \"thought\", \"time\", \"to\", \"to\", \"to\", \"to\", \"today\", \"trip\", \"true\", \"u\", \"unelected\", \"unless\", \"until\", \"us\", \"us\", \"via\", \"video\", \"visit\", \"want\", \"war\", \"war\", \"was\", \"was\", \"watch\", \"watch\", \"we\", \"we\", \"what\", \"whatever\", \"when\", \"where\", \"why\", \"will\", \"will\", \"with\", \"with\", \"with\", \"with\", \"world\", \"world\", \"would\", \"x\", \"you\", \"you\", \"your\", \"\\u2019\", \"\\u2019\", \"\\u2019\", \"\\u2019\", \"\\u201c\", \"\\u201d\", \"\\u2026\", \"\\u2026\", \"\\u2026\", \"\\u2026\", \"\\u2026\", \"\\ud83c\\udf81\", \"\\ud83c\\udf81\", \"\\ud83e\\udd17\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 1, 5, 3, 4]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el582822844002705766415519427\", ldavis_el582822844002705766415519427_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el582822844002705766415519427\", ldavis_el582822844002705766415519427_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el582822844002705766415519427\", ldavis_el582822844002705766415519427_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1     -0.020693  0.103810       1        1  39.231831\n",
       "0     -0.054291  0.078322       2        1  23.385694\n",
       "4     -0.136923  0.162607       3        1  16.163522\n",
       "2     -0.150757 -0.299344       4        1  13.599852\n",
       "3      0.362664 -0.045395       5        1   7.619101, topic_info=        Term          Freq         Total Category  logprob  loglift\n",
       "89        on   7178.000000   7178.000000  Default  30.0000  30.0000\n",
       "131  missile   1942.000000   1942.000000  Default  29.0000  29.0000\n",
       "118    visit   2705.000000   2705.000000  Default  28.0000  28.0000\n",
       "19     china  10305.000000  10305.000000  Default  27.0000  27.0000\n",
       "8         of   9358.000000   9358.000000  Default  26.0000  26.0000\n",
       "..       ...           ...           ...      ...      ...      ...\n",
       "26         s   1126.657671  11444.259476   Topic5  -3.1757   0.2563\n",
       "103     that    448.164710   2890.619682   Topic5  -4.0975   0.7104\n",
       "98        is    481.935302   6206.894206   Topic5  -4.0249   0.0189\n",
       "112       by    207.589405   2578.595104   Topic5  -4.8671   0.0551\n",
       "93       and    217.128896   5972.587779   Topic5  -4.8222  -0.7399\n",
       "\n",
       "[274 rows x 6 columns], token_table=      Topic      Freq Term\n",
       "term                      \n",
       "626       3  0.998401    2\n",
       "476       1  0.998044   23\n",
       "198       2  0.989841    3\n",
       "198       3  0.009768    3\n",
       "128       5  0.999107  4th\n",
       "...     ...       ...  ...\n",
       "17        4  0.188361    …\n",
       "17        5  0.007039    …\n",
       "6821      2  0.013162    🎁\n",
       "6821      5  0.980592    🎁\n",
       "1309      5  0.995755    🤗\n",
       "\n",
       "[297 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 5, 3, 4])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim_models as gensimvis\n",
    "import pickle \n",
    "import pyLDAvis\n",
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "LDAvis_prepared = gensimvis.prepare(lda_model, corpus, id2word)\n",
    "LDAvis_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Yonny\\anaconda3\\python.exe\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.executable)\n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x200 at 0x213E0E260A0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_string = ','.join(list(tweets_df['clean_text'].values))\n",
    "\n",
    "wordcloud = WordCloud(background_color=\"white\", max_words=1000, contour_width=3, contour_color='steelblue')\n",
    "\n",
    "wordcloud.generate(long_string)\n",
    "\n",
    "# Visualize the word cloud\n",
    "wordcloud.to_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.4 Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import re\n",
    "import nltk \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # plot params\n",
    "# plot_size = plt.rcParams[\"figure.figsize\"] \n",
    "# plot_size[0] = 8\n",
    "# plot_size[1] = 6\n",
    "# plt.rcParams[\"figure.figsize\"] = plot_size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # number of tweets for each airline\n",
    "# sns.set(rc={'figure.figsize':(14,10)})\n",
    "# tweets_df.polarity.value_counts().plot(kind='pie', autopct='%1.0f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Distribution of sentiments across all the tweets.\n",
    "# sns.set(rc={'figure.figsize':(14,10)})\n",
    "# tweets_df.sentiment.value_counts().plot(kind='pie', autopct='%1.0f%%', colors=[\"red\", \"yellow\", \"green\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set(rc={'figure.figsize':(12,8)})\n",
    "# sentiment = tweets_df.groupby(['Polarity', 'Sentiment']).sentiment.count().unstack()\n",
    "# sentiment.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.1 Sentiment Analysis with Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' extra random image  i  focus in one very specific zone of the western coast   district  city  ta …'\n",
      " ' china  s media the military for each area of the in the strait read the in the pi …'\n",
      " 'china even cut off communication  they don  t from the us  but here clown the stage to ask to change  s mind '\n",
      " ... 'thanks smart move '\n",
      " ' people  s desire for unification with china near record low as rise survey from  s national che …'\n",
      " ' people  s desire for unification with china near record low as rise survey from  s national che …']\n"
     ]
    }
   ],
   "source": [
    "# Selecting the feature and the label\n",
    "features = tweets_df.iloc[:, 4].values\n",
    "labels = tweets_df.iloc[:, 17].values\n",
    "\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Cleaning using regular expression\n",
    "processed_features = []\n",
    "\n",
    "for sentence in range(0, len(features)):\n",
    "    # Remove all the special characters\n",
    "    processed_feature = re.sub(r'\\W', ' ', str(features[sentence]))\n",
    "\n",
    "    # remove all single characters\n",
    "    processed_feature= re.sub(r'\\s+[a-zA-Z]\\s+', ' ', processed_feature)\n",
    "\n",
    "    # Remove single characters from the start\n",
    "    processed_feature = re.sub(r'\\^[a-zA-Z]\\s+', ' ', processed_feature) \n",
    "\n",
    "    # Substituting multiple spaces with single space\n",
    "    processed_feature = re.sub(r'\\s+', ' ', processed_feature, flags=re.I)\n",
    "\n",
    "    # Removing prefixed 'b'\n",
    "    processed_feature = re.sub(r'^b\\s+', '', processed_feature)\n",
    "\n",
    "    # Converting to Lowercase\n",
    "    processed_feature = processed_feature.lower()\n",
    "\n",
    "    processed_features.append(processed_feature)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer (max_features=2500, min_df=7, max_df=0.8, stop_words=stopwords.words('english'))\n",
    "processed_features_vectorized = vectorizer.fit_transform(processed_features).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into training(80%) and testing(20%)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(processed_features_vectorized, labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, random_state=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the dataset with random forest algorirhm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "text_classifier = RandomForestClassifier(n_estimators=200, random_state=0)\n",
    "text_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction with trained model\n",
    "predictions = text_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neutral', 'positive', 'neutral', 'negative', 'positive',\n",
       "       'neutral', 'neutral', 'neutral', 'neutral', 'positive'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9320454545454545\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model trained with random forest\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer_bigram = TfidfVectorizer (max_features=2189, min_df=7, max_df=0.8, stop_words=stopwords.words('english'),ngram_range = (2,2))\n",
    "processed_features_vectorized_bigram = vectorizer_bigram.fit_transform(processed_features).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into training(80%) and testing(20%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(processed_features_vectorized_bigram, labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, random_state=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_classifier_bigram = RandomForestClassifier(n_estimators=200, random_state=0)\n",
    "text_classifier_bigram.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction with trained model\n",
    "predictions = text_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4668181818181818\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model trained with random forest\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis with SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tweets_df['clean_text']\n",
    "y = tweets_df['sentiment']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16500\n",
      "5500\n",
      "16500\n",
      "5500\n"
     ]
    }
   ],
   "source": [
    "print(X_train.size)\n",
    "print(X_test.size)\n",
    "print(y_train.size)\n",
    "print(y_test.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the input\n",
    "clf = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000, tol=1e-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer(1,2)\n",
    "#X_train = X_train.replace(np.nan, '', regex=True)\n",
    "X_train_counts = count_vect.fit_transform(X_train)\n",
    "X_train_counts = X_train_counts.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('sgdclassifier', SGDClassifier(tol=0.01))])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train_counts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_test.replace(np.nan, '', regex=True)\n",
    "# use transform not fit_transform\n",
    "X_test_counts = count_vect.transform(X_test)\n",
    "X_test_counts = X_test_counts.toarray()\n",
    "# prediction = clf.prevaluedict(X_test_counts)\n",
    "prediction = clf.predict(X_test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making prediction\n",
    "prediction = clf.predict(X_test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9241818181818182"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(prediction == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ' number of the day  2 in its learn   teach to write mandarin whats your favorite no '",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_5828/878617526.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdisplay_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'negative'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'positive'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'neutral'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Confusion Matrix'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_plot\\confusion_matrix.py\u001b[0m in \u001b[0;36mplot_confusion_matrix\u001b[1;34m(estimator, X, y_true, labels, sample_weight, normalize, display_labels, include_values, xticks_rotation, values_format, cmap, ax, colorbar)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"plot_confusion_matrix only supports classifiers\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    264\u001b[0m     cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,\n\u001b[0;32m    265\u001b[0m                           labels=labels, normalize=normalize)\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\utils\\metaestimators.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m         \u001b[1;31m# lambda, but not partial, allows help() to work with update_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[1;31m# update the docstring of the returned function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m         \u001b[0mupdate_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\pipeline.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, **predict_params)\u001b[0m\n\u001b[0;32m    416\u001b[0m         \u001b[0mXt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwith_final\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mXt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpredict_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(self, X, copy)\u001b[0m\n\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 883\u001b[1;33m         X = self._validate_data(X, reset=False,\n\u001b[0m\u001b[0;32m    884\u001b[0m                                 \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    885\u001b[0m                                 \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    419\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'no_validation'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    671\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 673\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    674\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    675\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    855\u001b[0m               dtype='datetime64[ns]')\n\u001b[0;32m    856\u001b[0m         \"\"\"\n\u001b[1;32m--> 857\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    858\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    859\u001b[0m     \u001b[1;31m# ----------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Yonny\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: ' number of the day  2 in its learn   teach to write mandarin whats your favorite no '"
     ]
    }
   ],
   "source": [
    "# plot_confusion_matrix(clf, X_test, y_test, display_labels = ['negative', 'positive','neutral'])\n",
    "# plt.suptitle('Confusion Matrix')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis with Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "x = tweets_df['clean_text']\n",
    "y = tweets_df['sentiment']\n",
    "\n",
    "# spliting\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "# training\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train, y_train)\n",
    "\n",
    "# evaluating\n",
    "print(\"Naive Bayes score: \",nb.score(x_test, y_test))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a492b5279fe2e495c47ca4f6b152dc65c94fc5038b5e698174c22ffedef5d685"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}