raw_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "0425be8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
      "[nltk_data] Downloading package maxent_ne_chunker to\n",
      "[nltk_data]     /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package maxent_ne_chunker is already up-to-date!\n",
      "[nltk_data] Downloading package words to /Users/jennylee/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords, wordnet\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk import pos_tag\n",
    "from textblob.blob import TextBlob\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('vader_lexicon')\n",
    "import textstat\n",
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "\n",
    "nltk.download('maxent_ne_chunker')\n",
    "nltk.download('words')\n",
    "from nltk.tree import Tree\n",
    "from nltk import ne_chunk\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ae77c9",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f6f3745",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   news_ID                                               text\n",
       "0        0  Donald Trump just couldn t wish all Americans ...\n",
       "1        1  House Intelligence Committee Chairman Devin Nu...\n",
       "2        2  On Friday, it was revealed that former Milwauk...\n",
       "3        3  On Christmas day, Donald Trump announced that ...\n",
       "4        4  Pope Francis used his annual Christmas Day mes..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fake_news = pd.read_csv(\"DataSet_Misinfo_FAKE.csv\").rename(columns={\"Unnamed: 0\":\"news_ID\"})\n",
    "display(fake_news.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df160bed",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>The head of a conservative Republican faction ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Transgender people will be allowed for the fir...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>The special counsel investigation of links bet...</td>\n",
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       "      <td>3</td>\n",
       "      <td>Trump campaign adviser George Papadopoulos tol...</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>President Donald Trump called on the U.S. Post...</td>\n",
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      "text/plain": [
       "   news_ID                                               text\n",
       "0        0  The head of a conservative Republican faction ...\n",
       "1        1  Transgender people will be allowed for the fir...\n",
       "2        2  The special counsel investigation of links bet...\n",
       "3        3  Trump campaign adviser George Papadopoulos tol...\n",
       "4        4  President Donald Trump called on the U.S. Post..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_news = pd.read_csv(\"DataSet_Misinfo_TRUE.csv\").rename(columns={\"Unnamed: 0\":\"news_ID\"})\n",
    "display(true_news.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37f4aff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanup_df(df, genre):\n",
    "    original_rows = df.shape[0]\n",
    "    df[\"text\"] = df[\"text\"].str.replace(\"[\\n\\r]\", \" \")\n",
    "    df[\"text\"] = df[\"text\"].str.replace(\"\\(@[\\w]+\\)\", \"\")\n",
    "    df_need_clean = df[df.isna().any(axis=1)]\n",
    "    df = df.drop_duplicates(subset=[\"text\"], keep=\"last\")\n",
    "    print(f\"{original_rows - df.shape[0]} duplicated rows present in {genre} news dataset.\")\n",
    "    duplicated_rows = df.shape[0]\n",
    "    if len(df_need_clean) >= 1:\n",
    "        df = df[df['text'].notna()]\n",
    "    print(f\"{duplicated_rows - df.shape[0]} NaN row removed.\\nTotal number of rows remaining are: {df.shape[0]} rows.\")\n",
    "    display(df.head())\n",
    "    return df"
   ]
  },
  {
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   "execution_count": 5,
   "id": "87bc9f27",
   "metadata": {},
   "outputs": [
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     "text": [
      "/var/folders/c5/pphx1c0n3lb12sy85_ybqxqm0000gn/T/ipykernel_20000/2217677230.py:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"text\"] = df[\"text\"].str.replace(\"[\\n\\r]\", \" \")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "448 duplicated rows present in true news dataset.\n",
      "1 NaN row removed.\n",
      "Total number of rows remaining are: 34526 rows.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c5/pphx1c0n3lb12sy85_ybqxqm0000gn/T/ipykernel_20000/2217677230.py:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"text\"] = df[\"text\"].str.replace(\"\\(@[\\w]+\\)\", \"\")\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>The special counsel investigation of links bet...</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Trump campaign adviser George Papadopoulos tol...</td>\n",
       "    </tr>\n",
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       "      <td>President Donald Trump called on the U.S. Post...</td>\n",
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      "text/plain": [
       "   news_ID                                               text\n",
       "0        0  The head of a conservative Republican faction ...\n",
       "1        1  Transgender people will be allowed for the fir...\n",
       "2        2  The special counsel investigation of links bet...\n",
       "3        3  Trump campaign adviser George Papadopoulos tol...\n",
       "4        4  President Donald Trump called on the U.S. Post..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c5/pphx1c0n3lb12sy85_ybqxqm0000gn/T/ipykernel_20000/2217677230.py:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"text\"] = df[\"text\"].str.replace(\"[\\n\\r]\", \" \")\n",
      "/var/folders/c5/pphx1c0n3lb12sy85_ybqxqm0000gn/T/ipykernel_20000/2217677230.py:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"text\"] = df[\"text\"].str.replace(\"\\(@[\\w]+\\)\", \"\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9564 duplicated rows present in fake news dataset.\n",
      "0 NaN row removed.\n",
      "Total number of rows remaining are: 34078 rows.\n"
     ]
    },
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       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "    </tr>\n",
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       "      <td>4</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
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      ],
      "text/plain": [
       "   news_ID                                               text\n",
       "0        0  Donald Trump just couldn t wish all Americans ...\n",
       "1        1  House Intelligence Committee Chairman Devin Nu...\n",
       "2        2  On Friday, it was revealed that former Milwauk...\n",
       "3        3  On Christmas day, Donald Trump announced that ...\n",
       "4        4  Pope Francis used his annual Christmas Day mes..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_news = cleanup_df(true_news, \"true\")\n",
    "fake_news = cleanup_df(fake_news, \"fake\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d614e2c9",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ef1fbf",
   "metadata": {},
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d726648",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
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       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>71.65</td>\n",
       "      <td>32.17</td>\n",
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       "      <th>1</th>\n",
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       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>62.78</td>\n",
       "      <td>23.36</td>\n",
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       "      <td>2</td>\n",
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       "   news_ID                                               text  \\\n",
       "0        0  Donald Trump just couldn t wish all Americans ...   \n",
       "1        1  House Intelligence Committee Chairman Devin Nu...   \n",
       "2        2  On Friday, it was revealed that former Milwauk...   \n",
       "3        3  On Christmas day, Donald Trump announced that ...   \n",
       "4        4  Pope Francis used his annual Christmas Day mes...   \n",
       "\n",
       "   readability_score  reading_time  \n",
       "0              71.65         32.17  \n",
       "1              62.78         23.36  \n",
       "2              72.46         41.09  \n",
       "3              62.38         32.33  \n",
       "4              67.08         28.10  "
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       "      <td>The head of a conservative Republican faction ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Transgender people will be allowed for the fir...</td>\n",
       "      <td>40.69</td>\n",
       "      <td>31.47</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>The special counsel investigation of links bet...</td>\n",
       "      <td>58.01</td>\n",
       "      <td>33.85</td>\n",
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       "      <td>30.33</td>\n",
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       "      <td>4</td>\n",
       "      <td>President Donald Trump called on the U.S. Post...</td>\n",
       "      <td>63.09</td>\n",
       "      <td>63.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   news_ID                                               text  \\\n",
       "0        0  The head of a conservative Republican faction ...   \n",
       "1        1  Transgender people will be allowed for the fir...   \n",
       "2        2  The special counsel investigation of links bet...   \n",
       "3        3  Trump campaign adviser George Papadopoulos tol...   \n",
       "4        4  President Donald Trump called on the U.S. Post...   \n",
       "\n",
       "   readability_score  reading_time  \n",
       "0              51.58         57.10  \n",
       "1              40.69         31.47  \n",
       "2              58.01         33.85  \n",
       "3              51.58         30.33  \n",
       "4              63.09         63.36  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def assign_readability_time_scores(df):\n",
    "    readability_score = lambda x: textstat.flesch_reading_ease(str(x[\"text\"]))\n",
    "    df[\"readability_score\"] = df.apply(readability_score, axis=1)\n",
    "    reading_time = lambda x: textstat.reading_time(str(x[\"text\"]), ms_per_char=14.69)\n",
    "    df[\"reading_time\"] = df.apply(reading_time, axis=1)\n",
    "    return df\n",
    "\n",
    "fake_news = assign_readability_time_scores(fake_news)\n",
    "true_news = assign_readability_time_scores(true_news)\n",
    "display(fake_news.head())\n",
    "display(true_news.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e3e342b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_sample = fake_news.sample(n=34000)\n",
    "true_sample = true_news.sample(n=34000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0151ee3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of characters in a fake news article is 2565 characters.\n"
     ]
    },
    {
     "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>news_ID</th>\n",
       "      <th>text</th>\n",
       "      <th>readability_score</th>\n",
       "      <th>reading_time</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37210</th>\n",
       "      <td>37992</td>\n",
       "      <td>Throughout the 2000s the US, with the assistan...</td>\n",
       "      <td>30.91</td>\n",
       "      <td>5.80</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>3166</td>\n",
       "      <td>Donald Trump tweeted out yet another defense o...</td>\n",
       "      <td>56.49</td>\n",
       "      <td>56.63</td>\n",
       "      <td>4682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17855</td>\n",
       "      <td>San Juan Mayor Carmen Yulin Cruz tore a book f...</td>\n",
       "      <td>56.08</td>\n",
       "      <td>26.35</td>\n",
       "      <td>2190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12462</th>\n",
       "      <td>12781</td>\n",
       "      <td>TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...</td>\n",
       "      <td>87.35</td>\n",
       "      <td>3.38</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18762</th>\n",
       "      <td>19296</td>\n",
       "      <td>Thomas  article can be read below:My dad calle...</td>\n",
       "      <td>78.75</td>\n",
       "      <td>27.34</td>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "37210    37992  Throughout the 2000s the US, with the assistan...   \n",
       "3166      3166  Donald Trump tweeted out yet another defense o...   \n",
       "17375    17855  San Juan Mayor Carmen Yulin Cruz tore a book f...   \n",
       "12462    12781  TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...   \n",
       "18762    19296  Thomas  article can be read below:My dad calle...   \n",
       "\n",
       "       readability_score  reading_time  count  \n",
       "37210              30.91          5.80    465  \n",
       "3166               56.49         56.63   4682  \n",
       "17375              56.08         26.35   2190  \n",
       "12462              87.35          3.38    283  \n",
       "18762              78.75         27.34   2267  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of characters in a true news article is 3243 characters.\n"
     ]
    },
    {
     "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>news_ID</th>\n",
       "      <th>text</th>\n",
       "      <th>readability_score</th>\n",
       "      <th>reading_time</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13937</th>\n",
       "      <td>13937</td>\n",
       "      <td>The death toll in a militant attack on a mosqu...</td>\n",
       "      <td>65.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22032</th>\n",
       "      <td>22032</td>\n",
       "      <td>President Donald Trump won the 2016 election p...</td>\n",
       "      <td>56.18</td>\n",
       "      <td>31.91</td>\n",
       "      <td>2599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28347</th>\n",
       "      <td>28347</td>\n",
       "      <td>From the “it ain’t gonna happen” department, B...</td>\n",
       "      <td>56.18</td>\n",
       "      <td>24.43</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17174</th>\n",
       "      <td>17174</td>\n",
       "      <td>Two men were injured in a drive-by shooting at...</td>\n",
       "      <td>58.52</td>\n",
       "      <td>12.53</td>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>620</td>\n",
       "      <td>Republican lawmakers are pressing forward with...</td>\n",
       "      <td>46.57</td>\n",
       "      <td>11.77</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "13937    13937  The death toll in a militant attack on a mosqu...   \n",
       "22032    22032  President Donald Trump won the 2016 election p...   \n",
       "28347    28347  From the “it ain’t gonna happen” department, B...   \n",
       "17174    17174  Two men were injured in a drive-by shooting at...   \n",
       "620        620  Republican lawmakers are pressing forward with...   \n",
       "\n",
       "       readability_score  reading_time  count  \n",
       "13937              65.39          1.97    164  \n",
       "22032              56.18         31.91   2599  \n",
       "28347              56.18         24.43   2016  \n",
       "17174              58.52         12.53   1025  \n",
       "620                46.57         11.77    947  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_news_length(df, genre):\n",
    "    df[\"count\"] = df[\"text\"].str.len()\n",
    "    mean_count = df[\"count\"].mean()\n",
    "    print(f\"Mean number of characters in a {genre} news article is {'%02d' % mean_count} characters.\")\n",
    "    return df\n",
    "\n",
    "fake_sample = count_news_length(fake_sample, \"fake\")\n",
    "display(fake_sample.head())\n",
    "true_news = count_news_length(true_sample, \"true\")\n",
    "display(true_sample.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "449916d4",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40da63ea",
   "metadata": {},
   "source": [
    "## Data Cleaning & NLP Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "73eec76c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1161 new number of stopwords are added.\n"
     ]
    }
   ],
   "source": [
    "# Derived from: https://gist.github.com/sebleier/554280\n",
    "extend_stopwords = [\"0o\", \"0s\", \"3a\", \"3b\", \"3d\", \"6b\", \"6o\", \"a\", \"a1\", \"a2\", \"a3\", \"a4\", \"ab\", \"able\", \"about\", \"above\", \"abst\", \"ac\", \"accordance\", \"according\", \"accordingly\", \"across\", \"act\", \"actually\", \"ad\", \"added\", \"adj\", \"ae\", \"af\", \"affected\", \"affecting\", \"affects\", \"after\", \"afterwards\", \"ag\", \"again\", \"against\", \"ah\", \"ain\", \"ain't\", \"aj\", \"al\", \"all\", \"allow\", \"allows\", \"almost\", \"alone\", \"along\", \"already\", \"also\", \"although\", \"always\", \"am\", \"among\", \"amongst\", \"amoungst\", \"amount\", \"an\", \"and\", \"announce\", \"another\", \"any\", \"anybody\", \"anyhow\", \"anymore\", \"anyone\", \"anything\", \"anyway\", \"anyways\", \"anywhere\", \"ao\", \"ap\", \"apart\", \"apparently\", \"appear\", \"appreciate\", \"appropriate\", \"approximately\", \"ar\", \"are\", \"aren\", \"arent\", \"aren't\", \"arise\", \"around\", \"as\", \"a's\", \"aside\", \"ask\", \"asking\", \"associated\", \"at\", \"au\", \"auth\", \"av\", \"available\", \"aw\", \"away\", \"awfully\", \"ax\", \"ay\", \"az\", \"b\", \"b1\", \"b2\", \"b3\", \"ba\", \"back\", \"bc\", \"bd\", \"be\", \"became\", \"because\", \"become\", \"becomes\", \"becoming\", \"been\", \"before\", \"beforehand\", \"begin\", \"beginning\", \"beginnings\", \"begins\", \"behind\", \"being\", \"believe\", \"below\", \"beside\", \"besides\", \"best\", \"better\", \"between\", \"beyond\", \"bi\", \"bill\", \"biol\", \"bj\", \"bk\", \"bl\", \"bn\", \"both\", \"bottom\", \"bp\", \"br\", \"brief\", \"briefly\", \"bs\", \"bt\", \"bu\", \"but\", \"bx\", \"by\", \"c\", \"c1\", \"c2\", \"c3\", \"ca\", \"call\", \"came\", \"can\", \"cannot\", \"cant\", \"can't\", \"cause\", \"causes\", \"cc\", \"cd\", \"ce\", \"certain\", \"certainly\", \"cf\", \"cg\", \"ch\", \"changes\", \"ci\", \"cit\", \"cj\", \"cl\", \"clearly\", \"cm\", \"c'mon\", \"cn\", \"co\", \"com\", \"come\", \"comes\", \"con\", \"concerning\", \"consequently\", \"consider\", \"considering\", \"contain\", \"containing\", \"contains\", \"corresponding\", \"could\", \"couldn\", \"couldnt\", \"couldn't\", \"course\", \"cp\", \"cq\", \"cr\", \"cry\", \"cs\", \"c's\", \"ct\", \"cu\", \"currently\", \"cv\", \"cx\", \"cy\", \"cz\", \"d\", \"d2\", \"da\", \"date\", \"dc\", \"dd\", \"de\", \"definitely\", \"describe\", \"described\", \"despite\", \"detail\", \"df\", \"di\", \"did\", \"didn\", \"didn't\", \"different\", \"dj\", \"dk\", \"dl\", \"do\", \"does\", \"doesn\", \"doesn't\", \"doing\", \"don\", \"done\", \"don't\", \"down\", \"downwards\", \"dp\", \"dr\", \"ds\", \"dt\", \"du\", \"due\", \"during\", \"dx\", \"dy\", \"e\", \"e2\", \"e3\", \"ea\", \"each\", \"ec\", \"ed\", \"edu\", \"ee\", \"ef\", \"effect\", \"eg\", \"ei\", \"eight\", \"eighty\", \"either\", \"ej\", \"el\", \"eleven\", \"else\", \"elsewhere\", \"em\", \"empty\", \"en\", \"end\", \"ending\", \"enough\", \"entirely\", \"eo\", \"ep\", \"eq\", \"er\", \"es\", \"especially\", \"est\", \"et\", \"et-al\", \"etc\", \"eu\", \"ev\", \"even\", \"ever\", \"every\", \"everybody\", \"everyone\", \"everything\", \"everywhere\", \"ex\", \"exactly\", \"example\", \"except\", \"ey\", \"f\", \"f2\", \"fa\", \"far\", \"fc\", \"few\", \"ff\", \"fi\", \"fifteen\", \"fifth\", \"fify\", \"fill\", \"find\", \"fire\", \"first\", \"five\", \"fix\", \"fj\", \"fl\", \"fn\", \"fo\", \"followed\", \"following\", \"follows\", \"for\", \"former\", \"formerly\", \"forth\", \"forty\", \"found\", \"four\", \"fr\", \"from\", \"front\", \"fs\", \"ft\", \"fu\", \"full\", \"further\", \"furthermore\", \"fy\", \"g\", \"ga\", \"gave\", \"ge\", \"get\", \"gets\", \"getting\", \"gi\", \"give\", \"given\", \"gives\", \"giving\", \"gj\", \"gl\", \"go\", \"goes\", \"going\", \"gone\", \"got\", \"gotten\", \"gr\", \"greetings\", \"gs\", \"gy\", \"h\", \"h2\", \"h3\", \"had\", \"hadn\", \"hadn't\", \"happens\", \"hardly\", \"has\", \"hasn\", \"hasnt\", \"hasn't\", \"have\", \"haven\", \"haven't\", \"having\", \"he\", \"hed\", \"he'd\", \"he'll\", \"hello\", \"help\", \"hence\", \"her\", \"here\", \"hereafter\", \"hereby\", \"herein\", \"heres\", \"here's\", \"hereupon\", \"hers\", \"herself\", \"hes\", \"he's\", \"hh\", \"hi\", \"hid\", \"him\", \"himself\", \"his\", \"hither\", \"hj\", \"ho\", \"home\", \"hopefully\", \"how\", \"howbeit\", \"however\", \"how's\", \"hr\", \"hs\", \"http\", \"hu\", \"hundred\", \"hy\", \"i\", \"i2\", \"i3\", \"i4\", \"i6\", \"i7\", \"i8\", \"ia\", \"ib\", \"ibid\", \"ic\", \"id\", \"i'd\", \"ie\", \"if\", \"ig\", \"ignored\", \"ih\", \"ii\", \"ij\", \"il\", \"i'll\", \"im\", \"i'm\", \"immediate\", \"immediately\", \"importance\", \"important\", \"in\", \"inasmuch\", \"inc\", \"indeed\", \"index\", \"indicate\", \"indicated\", \"indicates\", \"information\", \"inner\", \"insofar\", \"instead\", \"interest\", \"into\", \"invention\", \"inward\", \"io\", \"ip\", \"iq\", \"ir\", \"is\", \"isn\", \"isn't\", \"it\", \"itd\", \"it'd\", \"it'll\", \"its\", \"it's\", \"itself\", \"iv\", \"i've\", \"ix\", \"iy\", \"iz\", \"j\", \"jj\", \"jr\", \"js\", \"jt\", \"ju\", \"just\", \"k\", \"ke\", \"keep\", \"keeps\", \"kept\", \"kg\", \"kj\", \"km\", \"know\", \"known\", \"knows\", \"ko\", \"l\", \"l2\", \"la\", \"largely\", \"last\", \"lately\", \"later\", \"latter\", \"latterly\", \"lb\", \"lc\", \"le\", \"least\", \"les\", \"less\", \"lest\", \"let\", \"lets\", \"let's\", \"lf\", \"like\", \"liked\", \"likely\", \"line\", \"little\", \"lj\", \"ll\", \"ll\", \"ln\", \"lo\", \"look\", \"looking\", \"looks\", \"los\", \"lr\", \"ls\", \"lt\", \"ltd\", \"m\", \"m2\", \"ma\", \"made\", \"mainly\", \"make\", \"makes\", \"many\", \"may\", \"maybe\", \"me\", \"mean\", \"means\", \"meantime\", \"meanwhile\", \"merely\", \"mg\", \"might\", \"mightn\", \"mightn't\", \"mill\", \"million\", \"mine\", \"miss\", \"ml\", \"mn\", \"mo\", \"more\", \"moreover\", \"most\", \"mostly\", \"move\", \"mr\", \"mrs\", \"ms\", \"mt\", \"mu\", \"much\", \"mug\", \"must\", \"mustn\", \"mustn't\", \"my\", \"myself\", \"n\", \"n2\", \"na\", \"name\", \"namely\", \"nay\", \"nc\", \"nd\", \"ne\", \"near\", \"nearly\", \"necessarily\", \"necessary\", \"need\", \"needn\", \"needn't\", \"needs\", \"neither\", \"never\", \"nevertheless\", \"new\", \"next\", \"ng\", \"ni\", \"nine\", \"ninety\", \"nj\", \"nl\", \"nn\", \"no\", \"nobody\", \"non\", \"none\", \"nonetheless\", \"noone\", \"nor\", \"normally\", \"nos\", \"not\", \"noted\", \"nothing\", \"novel\", \"now\", \"nowhere\", \"nr\", \"ns\", \"nt\", \"ny\", \"o\", \"oa\", \"ob\", \"obtain\", \"obtained\", \"obviously\", \"oc\", \"od\", \"of\", \"off\", \"often\", \"og\", \"oh\", \"oi\", \"oj\", \"ok\", \"okay\", \"ol\", \"old\", \"om\", \"omitted\", \"on\", \"once\", \"one\", \"ones\", \"only\", \"onto\", \"oo\", \"op\", \"oq\", \"or\", \"ord\", \"os\", \"ot\", \"other\", \"others\", \"otherwise\", \"ou\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"outside\", \"over\", \"overall\", \"ow\", \"owing\", \"own\", \"ox\", \"oz\", \"p\", \"p1\", \"p2\", \"p3\", \"page\", \"pagecount\", \"pages\", \"par\", \"part\", \"particular\", \"particularly\", \"pas\", \"past\", \"pc\", \"pd\", \"pe\", \"per\", \"perhaps\", \"pf\", \"ph\", \"pi\", \"pj\", \"pk\", \"pl\", \"placed\", \"please\", \"plus\", \"pm\", \"pn\", \"po\", \"poorly\", \"possible\", \"possibly\", \"potentially\", \"pp\", \"pq\", \"pr\", \"predominantly\", \"present\", \"presumably\", \"previously\", \"primarily\", \"probably\", \"promptly\", \"proud\", \"provides\", \"ps\", \"pt\", \"pu\", \"put\", \"py\", \"q\", \"qj\", \"qu\", \"que\", \"quickly\", \"quite\", \"qv\", \"r\", \"r2\", \"ra\", \"ran\", \"rather\", \"rc\", \"rd\", \"re\", \"readily\", \"really\", \"reasonably\", \"recent\", \"recently\", \"ref\", \"refs\", \"regarding\", \"regardless\", \"regards\", \"related\", \"relatively\", \"research\", \"research-articl\", \"respectively\", \"resulted\", \"resulting\", \"results\", \"rf\", \"rh\", \"ri\", \"right\", \"rj\", \"rl\", \"rm\", \"rn\", \"ro\", \"rq\", \"rr\", \"rs\", \"rt\", \"ru\", \"run\", \"rv\", \"ry\", \"s\", \"s2\", \"sa\", \"said\", \"same\", \"saw\", \"say\", \"saying\", \"says\", \"sc\", \"sd\", \"se\", \"sec\", \"second\", \"secondly\", \"section\", \"see\", \"seeing\", \"seem\", \"seemed\", \"seeming\", \"seems\", \"seen\", \"self\", \"selves\", \"sensible\", \"sent\", \"serious\", \"seriously\", \"seven\", \"several\", \"sf\", \"shall\", \"shan\", \"shan't\", \"she\", \"shed\", \"she'd\", \"she'll\", \"shes\", \"she's\", \"should\", \"shouldn\", \"shouldn't\", \"should've\", \"show\", \"showed\", \"shown\", \"showns\", \"shows\", \"si\", \"side\", \"significant\", \"significantly\", \"similar\", \"similarly\", \"since\", \"sincere\", \"six\", \"sixty\", \"sj\", \"sl\", \"slightly\", \"sm\", \"sn\", \"so\", \"some\", \"somebody\", \"somehow\", \"someone\", \"somethan\", \"something\", \"sometime\", \"sometimes\", \"somewhat\", \"somewhere\", \"soon\", \"sorry\", \"sp\", \"specifically\", \"specified\", \"specify\", \"specifying\", \"sq\", \"sr\", \"ss\", \"st\", \"still\", \"stop\", \"strongly\", \"sub\", \"substantially\", \"successfully\", \"such\", \"sufficiently\", \"suggest\", \"sup\", \"sure\", \"sy\", \"system\", \"sz\", \"t\", \"t1\", \"t2\", \"t3\", \"take\", \"taken\", \"taking\", \"tb\", \"tc\", \"td\", \"te\", \"tell\", \"ten\", \"tends\", \"tf\", \"th\", \"than\", \"thank\", \"thanks\", \"thanx\", \"that\", \"that'll\", \"thats\", \"that's\", \"that've\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"thence\", \"there\", \"thereafter\", \"thereby\", \"thered\", \"therefore\", \"therein\", \"there'll\", \"thereof\", \"therere\", \"theres\", \"there's\", \"thereto\", \"thereupon\", \"there've\", \"these\", \"they\", \"theyd\", \"they'd\", \"they'll\", \"theyre\", \"they're\", \"they've\", \"thickv\", \"thin\", \"think\", \"third\", \"this\", \"thorough\", \"thoroughly\", \"those\", \"thou\", \"though\", \"thoughh\", \"thousand\", \"three\", \"throug\", \"through\", \"throughout\", \"thru\", \"thus\", \"ti\", \"til\", \"tip\", \"tj\", \"tl\", \"tm\", \"tn\", \"to\", \"together\", \"too\", \"took\", \"top\", \"toward\", \"towards\", \"tp\", \"tq\", \"tr\", \"tried\", \"tries\", \"truly\", \"try\", \"trying\", \"ts\", \"t's\", \"tt\", \"tv\", \"twelve\", \"twenty\", \"twice\", \"two\", \"tx\", \"u\", \"u201d\", \"ue\", \"ui\", \"uj\", \"uk\", \"um\", \"un\", \"under\", \"unfortunately\", \"unless\", \"unlike\", \"unlikely\", \"until\", \"unto\", \"uo\", \"up\", \"upon\", \"ups\", \"ur\", \"us\", \"use\", \"used\", \"useful\", \"usefully\", \"usefulness\", \"uses\", \"using\", \"usually\", \"ut\", \"v\", \"va\", \"value\", \"various\", \"vd\", \"ve\", \"ve\", \"very\", \"via\", \"viz\", \"vj\", \"vo\", \"vol\", \"vols\", \"volumtype\", \"vq\", \"vs\", \"vt\", \"vu\", \"w\", \"wa\", \"want\", \"wants\", \"was\", \"wasn\", \"wasnt\", \"wasn't\", \"way\", \"we\", \"wed\", \"we'd\", \"welcome\", \"well\", \"we'll\", \"well-b\", \"went\", \"were\", \"we're\", \"weren\", \"werent\", \"weren't\", \"we've\", \"what\", \"whatever\", \"what'll\", \"whats\", \"what's\", \"when\", \"whence\", \"whenever\", \"when's\", \"where\", \"whereafter\", \"whereas\", \"whereby\", \"wherein\", \"wheres\", \"where's\", \"whereupon\", \"wherever\", \"whether\", \"which\", \"while\", \"whim\", \"whither\", \"who\", \"whod\", \"whoever\", \"whole\", \"who'll\", \"whom\", \"whomever\", \"whos\", \"who's\", \"whose\", \"why\", \"why's\", \"wi\", \"widely\", \"will\", \"willing\", \"wish\", \"with\", \"within\", \"without\", \"wo\", \"won\", \"wonder\", \"wont\", \"won't\", \"words\", \"world\", \"would\", \"wouldn\", \"wouldnt\", \"wouldn't\", \"www\", \"x\", \"x1\", \"x2\", \"x3\", \"xf\", \"xi\", \"xj\", \"xk\", \"xl\", \"xn\", \"xo\", \"xs\", \"xt\", \"xv\", \"xx\", \"y\", \"y2\", \"yes\", \"yet\", \"yj\", \"yl\", \"you\", \"youd\", \"you'd\", \"you'll\", \"your\", \"youre\", \"you're\", \"yours\", \"yourself\", \"yourselves\", \"you've\", \"yr\", \"ys\", \"yt\", \"z\", \"zero\", \"zi\", \"zz\", \"mr\"]\n",
    "print(f\"{len(extend_stopwords)} new number of stopwords are added.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "979ba3d9",
   "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",
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       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "      <th>readability_score</th>\n",
       "      <th>reading_time</th>\n",
       "      <th>count</th>\n",
       "      <th>filtered_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37210</th>\n",
       "      <td>37992</td>\n",
       "      <td>Throughout the 2000s the US, with the assistan...</td>\n",
       "      <td>30.91</td>\n",
       "      <td>5.80</td>\n",
       "      <td>465</td>\n",
       "      <td>2000s , assistance Baltic state , Poland , Swe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>3166</td>\n",
       "      <td>Donald Trump tweeted out yet another defense o...</td>\n",
       "      <td>56.49</td>\n",
       "      <td>56.63</td>\n",
       "      <td>4682</td>\n",
       "      <td>Donald Trump tweet defense Putin Wednesday , f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17855</td>\n",
       "      <td>San Juan Mayor Carmen Yulin Cruz tore a book f...</td>\n",
       "      <td>56.08</td>\n",
       "      <td>26.35</td>\n",
       "      <td>2190</td>\n",
       "      <td>San Juan Mayor Carmen Yulin Cruz tear book nas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12462</th>\n",
       "      <td>12781</td>\n",
       "      <td>TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...</td>\n",
       "      <td>87.35</td>\n",
       "      <td>3.38</td>\n",
       "      <td>283</td>\n",
       "      <td>TWITTER ABUZZ FLY LANDED HILLARY FACE : strang...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18762</th>\n",
       "      <td>19296</td>\n",
       "      <td>Thomas  article can be read below:My dad calle...</td>\n",
       "      <td>78.75</td>\n",
       "      <td>27.34</td>\n",
       "      <td>2267</td>\n",
       "      <td>Thomas article read : dad call Martin Luther K...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "37210    37992  Throughout the 2000s the US, with the assistan...   \n",
       "3166      3166  Donald Trump tweeted out yet another defense o...   \n",
       "17375    17855  San Juan Mayor Carmen Yulin Cruz tore a book f...   \n",
       "12462    12781  TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...   \n",
       "18762    19296  Thomas  article can be read below:My dad calle...   \n",
       "\n",
       "       readability_score  reading_time  count  \\\n",
       "37210              30.91          5.80    465   \n",
       "3166               56.49         56.63   4682   \n",
       "17375              56.08         26.35   2190   \n",
       "12462              87.35          3.38    283   \n",
       "18762              78.75         27.34   2267   \n",
       "\n",
       "                                           filtered_text  \n",
       "37210  2000s , assistance Baltic state , Poland , Swe...  \n",
       "3166   Donald Trump tweet defense Putin Wednesday , f...  \n",
       "17375  San Juan Mayor Carmen Yulin Cruz tear book nas...  \n",
       "12462  TWITTER ABUZZ FLY LANDED HILLARY FACE : strang...  \n",
       "18762  Thomas article read : dad call Martin Luther K...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>news_ID</th>\n",
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       "      <th>readability_score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13937</th>\n",
       "      <td>13937</td>\n",
       "      <td>The death toll in a militant attack on a mosqu...</td>\n",
       "      <td>65.39</td>\n",
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       "      <td>164</td>\n",
       "      <td>death toll militant attack mosque Egypt north ...</td>\n",
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       "      <td>President Donald Trump won the 2016 election p...</td>\n",
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       "      <td>President Donald Trump 2016 election promise d...</td>\n",
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       "      <td>28347</td>\n",
       "      <td>From the “it ain’t gonna happen” department, B...</td>\n",
       "      <td>56.18</td>\n",
       "      <td>24.43</td>\n",
       "      <td>2016</td>\n",
       "      <td>“ ’ gon happen ” department , Baltimore Ravens...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17174</th>\n",
       "      <td>17174</td>\n",
       "      <td>Two men were injured in a drive-by shooting at...</td>\n",
       "      <td>58.52</td>\n",
       "      <td>12.53</td>\n",
       "      <td>1025</td>\n",
       "      <td>men injure drive-by shooting South Africa Cape...</td>\n",
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       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>620</td>\n",
       "      <td>Republican lawmakers are pressing forward with...</td>\n",
       "      <td>46.57</td>\n",
       "      <td>11.77</td>\n",
       "      <td>947</td>\n",
       "      <td>Republican lawmaker press forward investigatio...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "13937    13937  The death toll in a militant attack on a mosqu...   \n",
       "22032    22032  President Donald Trump won the 2016 election p...   \n",
       "28347    28347  From the “it ain’t gonna happen” department, B...   \n",
       "17174    17174  Two men were injured in a drive-by shooting at...   \n",
       "620        620  Republican lawmakers are pressing forward with...   \n",
       "\n",
       "       readability_score  reading_time  count  \\\n",
       "13937              65.39          1.97    164   \n",
       "22032              56.18         31.91   2599   \n",
       "28347              56.18         24.43   2016   \n",
       "17174              58.52         12.53   1025   \n",
       "620                46.57         11.77    947   \n",
       "\n",
       "                                           filtered_text  \n",
       "13937  death toll militant attack mosque Egypt north ...  \n",
       "22032  President Donald Trump 2016 election promise d...  \n",
       "28347  “ ’ gon happen ” department , Baltimore Ravens...  \n",
       "17174  men injure drive-by shooting South Africa Cape...  \n",
       "620    Republican lawmaker press forward investigatio...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def tag_mapper(nltk_tag):\n",
    "    if nltk_tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif nltk_tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif nltk_tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif nltk_tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:          \n",
    "        return wordnet.NOUN\n",
    "    \n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stopwords_ls = set(stopwords.words(\"english\"))\n",
    "stopwords_ls.update(extend_stopwords)\n",
    "\n",
    "def remove_stopwords_and_lemmatize(article):\n",
    "    words = pos_tag(word_tokenize(article))\n",
    "    filtered_article = [lemmatizer.lemmatize(word, pos=tag_mapper(tag)) for word, tag in words if (word.lower() not in stopwords_ls)]\n",
    "    full_article = (\" \".join(filtered_article))\n",
    "    return full_article\n",
    "\n",
    "fake_sample[\"filtered_text\"] = fake_sample[\"text\"].apply(lambda x: remove_stopwords_and_lemmatize(x))\n",
    "true_sample[\"filtered_text\"] = true_sample[\"text\"].apply(lambda x: remove_stopwords_and_lemmatize(x))\n",
    "display(fake_sample.head())\n",
    "display(true_sample.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bfa957e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Subjectivity</th>\n",
       "      <th>Negative</th>\n",
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       "      <th>Compound</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37210</th>\n",
       "      <td>37992</td>\n",
       "      <td>Throughout the 2000s the US, with the assistan...</td>\n",
       "      <td>30.91</td>\n",
       "      <td>5.80</td>\n",
       "      <td>465</td>\n",
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       "      <td>Donald Trump tweet defense Putin Wednesday , f...</td>\n",
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       "      <td>San Juan Mayor Carmen Yulin Cruz tear book nas...</td>\n",
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       "      <td>0.244</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.656</td>\n",
       "      <td>-0.9869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12462</th>\n",
       "      <td>12781</td>\n",
       "      <td>TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...</td>\n",
       "      <td>87.35</td>\n",
       "      <td>3.38</td>\n",
       "      <td>283</td>\n",
       "      <td>TWITTER ABUZZ FLY LANDED HILLARY FACE : strang...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.792</td>\n",
       "      <td>-0.6093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18762</th>\n",
       "      <td>19296</td>\n",
       "      <td>Thomas  article can be read below:My dad calle...</td>\n",
       "      <td>78.75</td>\n",
       "      <td>27.34</td>\n",
       "      <td>2267</td>\n",
       "      <td>Thomas article read : dad call Martin Luther K...</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.630</td>\n",
       "      <td>-0.9965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "37210    37992  Throughout the 2000s the US, with the assistan...   \n",
       "3166      3166  Donald Trump tweeted out yet another defense o...   \n",
       "17375    17855  San Juan Mayor Carmen Yulin Cruz tore a book f...   \n",
       "12462    12781  TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...   \n",
       "18762    19296  Thomas  article can be read below:My dad calle...   \n",
       "\n",
       "       readability_score  reading_time  count  \\\n",
       "37210              30.91          5.80    465   \n",
       "3166               56.49         56.63   4682   \n",
       "17375              56.08         26.35   2190   \n",
       "12462              87.35          3.38    283   \n",
       "18762              78.75         27.34   2267   \n",
       "\n",
       "                                           filtered_text  Subjectivity  \\\n",
       "37210  2000s , assistance Baltic state , Poland , Swe...      0.322222   \n",
       "3166   Donald Trump tweet defense Putin Wednesday , f...      0.527758   \n",
       "17375  San Juan Mayor Carmen Yulin Cruz tear book nas...      0.623861   \n",
       "12462  TWITTER ABUZZ FLY LANDED HILLARY FACE : strang...      0.666667   \n",
       "18762  Thomas article read : dad call Martin Luther K...      0.348958   \n",
       "\n",
       "       Negative  Positive  Neutral  Compound  \n",
       "37210     0.245     0.250    0.505   -0.1779  \n",
       "3166      0.109     0.186    0.705    0.9854  \n",
       "17375     0.244     0.100    0.656   -0.9869  \n",
       "12462     0.208     0.000    0.792   -0.6093  \n",
       "18762     0.314     0.056    0.630   -0.9965  "
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
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       "      <td>0.151</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.772</td>\n",
       "      <td>-0.8860</td>\n",
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       "      <td>58.52</td>\n",
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       "      <td>1025</td>\n",
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       "      <th>620</th>\n",
       "      <td>620</td>\n",
       "      <td>Republican lawmakers are pressing forward with...</td>\n",
       "      <td>46.57</td>\n",
       "      <td>11.77</td>\n",
       "      <td>947</td>\n",
       "      <td>Republican lawmaker press forward investigatio...</td>\n",
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       "      <td>0.016</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.9153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "13937    13937  The death toll in a militant attack on a mosqu...   \n",
       "22032    22032  President Donald Trump won the 2016 election p...   \n",
       "28347    28347  From the “it ain’t gonna happen” department, B...   \n",
       "17174    17174  Two men were injured in a drive-by shooting at...   \n",
       "620        620  Republican lawmakers are pressing forward with...   \n",
       "\n",
       "       readability_score  reading_time  count  \\\n",
       "13937              65.39          1.97    164   \n",
       "22032              56.18         31.91   2599   \n",
       "28347              56.18         24.43   2016   \n",
       "17174              58.52         12.53   1025   \n",
       "620                46.57         11.77    947   \n",
       "\n",
       "                                           filtered_text  Subjectivity  \\\n",
       "13937  death toll militant attack mosque Egypt north ...      0.000000   \n",
       "22032  President Donald Trump 2016 election promise d...      0.225556   \n",
       "28347  “ ’ gon happen ” department , Baltimore Ravens...      0.232843   \n",
       "17174  men injure drive-by shooting South Africa Cape...      0.238095   \n",
       "620    Republican lawmaker press forward investigatio...      0.413333   \n",
       "\n",
       "       Negative  Positive  Neutral  Compound  \n",
       "13937     0.292     0.000    0.708   -0.7906  \n",
       "22032     0.071     0.149    0.781    0.9738  \n",
       "28347     0.151     0.077    0.772   -0.8860  \n",
       "17174     0.079     0.026    0.894   -0.5859  \n",
       "620       0.016     0.156    0.828    0.9153  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "def calculate_subjectivity(article):\n",
    "    return TextBlob(article).sentiment.subjectivity\n",
    "\n",
    "def nlp_analysis_workflow(df):\n",
    "    df[\"Subjectivity\"] = df[\"filtered_text\"].apply(calculate_subjectivity)\n",
    "    sentiment_scores = df[\"filtered_text\"].apply(sid.polarity_scores)\n",
    "    df[\"Negative\"] = sentiment_scores.apply(lambda x: x['neg'])\n",
    "    df[\"Positive\"] = sentiment_scores.apply(lambda x: x['pos'])\n",
    "    df[\"Neutral\"] = sentiment_scores.apply(lambda x: x['neu'])\n",
    "    df[\"Compound\"] = sentiment_scores.apply(lambda x: x['compound'])\n",
    "    df[\"Subjectivity\"] = df[\"filtered_text\"].apply(calculate_subjectivity)\n",
    "    return df\n",
    "\n",
    "fake_sample = nlp_analysis_workflow(fake_sample)\n",
    "true_sample = nlp_analysis_workflow(true_sample)\n",
    "display(fake_sample.head())\n",
    "display(true_sample.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5c405062",
   "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>News Type</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fake News</td>\n",
       "      <td>0.136882</td>\n",
       "      <td>0.165019</td>\n",
       "      <td>-0.147257</td>\n",
       "      <td>0.697949</td>\n",
       "      <td>0.407425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True News</td>\n",
       "      <td>0.136830</td>\n",
       "      <td>0.130691</td>\n",
       "      <td>0.065437</td>\n",
       "      <td>0.732450</td>\n",
       "      <td>0.373503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   News Type  Positive  Negative  Compound   Neutral  Subjectivity\n",
       "0  Fake News  0.136882  0.165019 -0.147257  0.697949      0.407425\n",
       "1  True News  0.136830  0.130691  0.065437  0.732450      0.373503"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nlp_summary_df = pd.DataFrame()\n",
    "nlp_summary_df[\"News Type\"] = [\"Fake News\", \"True News\"]\n",
    "nlp_summary_df[\"Positive\"] = [fake_sample[\"Positive\"].mean(), true_sample[\"Positive\"].mean()]\n",
    "nlp_summary_df[\"Negative\"] = [fake_sample[\"Negative\"].mean(), true_sample[\"Negative\"].mean()]\n",
    "nlp_summary_df[\"Compound\"] = [fake_sample[\"Compound\"].mean(), true_sample[\"Compound\"].mean()]\n",
    "nlp_summary_df[\"Neutral\"] = [fake_sample[\"Neutral\"].mean(), true_sample[\"Neutral\"].mean()]\n",
    "nlp_summary_df[\"Subjectivity\"] = [fake_sample[\"Subjectivity\"].mean(), true_sample[\"Subjectivity\"].mean()]\n",
    "display(nlp_summary_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88a53a6b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now displaying WordCloud visualization for fake news.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now displaying WordCloud visualization for true news.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_wordcloud(df, file_name):\n",
    "    word_text = \" \".join(article for article in df.filtered_text)\n",
    "\n",
    "    word_cloud = WordCloud(width=1000, height=1000, random_state=1,\n",
    "                          background_color=\"white\", collocations=False,\n",
    "                          stopwords=stopwords_ls).generate(word_text)\n",
    "    print(f\"Now displaying WordCloud visualization for {file_name} news.\")\n",
    "    plt.imshow(word_cloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.savefig(f'images/{file_name}_wordcloud.png')\n",
    "    plt.show()\n",
    "    \n",
    "generate_wordcloud(fake_sample, \"fake\")\n",
    "generate_wordcloud(true_sample, \"true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9a19b0",
   "metadata": {},
   "source": [
    "### Top 20 Words Mentioned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "71a43b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_20_words(df, column_name=\"filtered_text\", n=20):\n",
    "    cv = CountVectorizer(stop_words = 'english')\n",
    "    vec = cv.fit(df[column_name])\n",
    "    bag_of_words = vec.transform(df[column_name])\n",
    "    sum_words = bag_of_words.sum(axis=0)\n",
    "    calculate_frequency = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    calculate_frequency =sorted(calculate_frequency, key = lambda x: x[1], reverse=True)\n",
    "    top_20_words_dict = calculate_frequency[:n]\n",
    "    df_return = pd.DataFrame(top_20_words_dict, columns=[\"Word\", \"Frequency\"])\n",
    "    return df_return\n",
    "    \n",
    "true_top_20_words = get_top_20_words(true_sample)\n",
    "fake_top_20_words = get_top_20_words(fake_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "57421741",
   "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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>95451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mr</td>\n",
       "      <td>63282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>president</td>\n",
       "      <td>47766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>year</td>\n",
       "      <td>44798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>state</td>\n",
       "      <td>44042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word  Frequency\n",
       "0      trump      95451\n",
       "1         mr      63282\n",
       "2  president      47766\n",
       "3       year      44798\n",
       "4      state      44042"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>89268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>people</td>\n",
       "      <td>39169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clinton</td>\n",
       "      <td>33598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>president</td>\n",
       "      <td>30314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>state</td>\n",
       "      <td>26287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word  Frequency\n",
       "0      trump      89268\n",
       "1     people      39169\n",
       "2    clinton      33598\n",
       "3  president      30314\n",
       "4      state      26287"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(true_top_20_words.head())\n",
    "display(fake_top_20_words.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7991a6be",
   "metadata": {},
   "source": [
    "### Most Mentioned  & Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "59b55e6f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "      <th>readability_score</th>\n",
       "      <th>reading_time</th>\n",
       "      <th>count</th>\n",
       "      <th>filtered_text</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Compound</th>\n",
       "      <th>label</th>\n",
       "      <th>names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37210</th>\n",
       "      <td>37992</td>\n",
       "      <td>Throughout the 2000s the US, with the assistan...</td>\n",
       "      <td>30.91</td>\n",
       "      <td>5.80</td>\n",
       "      <td>465</td>\n",
       "      <td>2000s , assistance Baltic state , Poland , Swe...</td>\n",
       "      <td>0.322222</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.505</td>\n",
       "      <td>-0.1779</td>\n",
       "      <td>1</td>\n",
       "      <td>Baltic Poland Sweden “ Russia “ ” Russia Europ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>3166</td>\n",
       "      <td>Donald Trump tweeted out yet another defense o...</td>\n",
       "      <td>56.49</td>\n",
       "      <td>56.63</td>\n",
       "      <td>4682</td>\n",
       "      <td>Donald Trump tweet defense Putin Wednesday , f...</td>\n",
       "      <td>0.527758</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.9854</td>\n",
       "      <td>1</td>\n",
       "      <td>Donald Trump Putin Wednesday Vladimir Putin Do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17855</td>\n",
       "      <td>San Juan Mayor Carmen Yulin Cruz tore a book f...</td>\n",
       "      <td>56.08</td>\n",
       "      <td>26.35</td>\n",
       "      <td>2190</td>\n",
       "      <td>San Juan Mayor Carmen Yulin Cruz tear book nas...</td>\n",
       "      <td>0.623861</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.656</td>\n",
       "      <td>-0.9869</td>\n",
       "      <td>1</td>\n",
       "      <td>San Juan Mayor Carmen Yulin Cruz Democrat Wedn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12462</th>\n",
       "      <td>12781</td>\n",
       "      <td>TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...</td>\n",
       "      <td>87.35</td>\n",
       "      <td>3.38</td>\n",
       "      <td>283</td>\n",
       "      <td>TWITTER ABUZZ FLY LANDED HILLARY FACE : strang...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.792</td>\n",
       "      <td>-0.6093</td>\n",
       "      <td>1</td>\n",
       "      <td>TWITTER ABUZZ FLY LANDED HILLARY FACE Obama Hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18762</th>\n",
       "      <td>19296</td>\n",
       "      <td>Thomas  article can be read below:My dad calle...</td>\n",
       "      <td>78.75</td>\n",
       "      <td>27.34</td>\n",
       "      <td>2267</td>\n",
       "      <td>Thomas article read : dad call Martin Luther K...</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.630</td>\n",
       "      <td>-0.9965</td>\n",
       "      <td>1</td>\n",
       "      <td>Thomas Martin Luther King Dr. King Notice USA ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "37210    37992  Throughout the 2000s the US, with the assistan...   \n",
       "3166      3166  Donald Trump tweeted out yet another defense o...   \n",
       "17375    17855  San Juan Mayor Carmen Yulin Cruz tore a book f...   \n",
       "12462    12781  TWITTER IS ABUZZ OVER THE FLY THAT LANDED ON H...   \n",
       "18762    19296  Thomas  article can be read below:My dad calle...   \n",
       "\n",
       "       readability_score  reading_time  count  \\\n",
       "37210              30.91          5.80    465   \n",
       "3166               56.49         56.63   4682   \n",
       "17375              56.08         26.35   2190   \n",
       "12462              87.35          3.38    283   \n",
       "18762              78.75         27.34   2267   \n",
       "\n",
       "                                           filtered_text  Subjectivity  \\\n",
       "37210  2000s , assistance Baltic state , Poland , Swe...      0.322222   \n",
       "3166   Donald Trump tweet defense Putin Wednesday , f...      0.527758   \n",
       "17375  San Juan Mayor Carmen Yulin Cruz tear book nas...      0.623861   \n",
       "12462  TWITTER ABUZZ FLY LANDED HILLARY FACE : strang...      0.666667   \n",
       "18762  Thomas article read : dad call Martin Luther K...      0.348958   \n",
       "\n",
       "       Negative  Positive  Neutral  Compound  label  \\\n",
       "37210     0.245     0.250    0.505   -0.1779      1   \n",
       "3166      0.109     0.186    0.705    0.9854      1   \n",
       "17375     0.244     0.100    0.656   -0.9869      1   \n",
       "12462     0.208     0.000    0.792   -0.6093      1   \n",
       "18762     0.314     0.056    0.630   -0.9965      1   \n",
       "\n",
       "                                                   names  \n",
       "37210  Baltic Poland Sweden “ Russia “ ” Russia Europ...  \n",
       "3166   Donald Trump Putin Wednesday Vladimir Putin Do...  \n",
       "17375  San Juan Mayor Carmen Yulin Cruz Democrat Wedn...  \n",
       "12462  TWITTER ABUZZ FLY LANDED HILLARY FACE Obama Hi...  \n",
       "18762  Thomas Martin Luther King Dr. King Notice USA ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "      <th>readability_score</th>\n",
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       "    <tr>\n",
       "      <th>13937</th>\n",
       "      <td>13937</td>\n",
       "      <td>The death toll in a militant attack on a mosqu...</td>\n",
       "      <td>65.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>164</td>\n",
       "      <td>death toll militant attack mosque Egypt north ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.708</td>\n",
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       "      <td>0</td>\n",
       "      <td>Egypt Sinai MENA Friday</td>\n",
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       "      <th>22032</th>\n",
       "      <td>22032</td>\n",
       "      <td>President Donald Trump won the 2016 election p...</td>\n",
       "      <td>56.18</td>\n",
       "      <td>31.91</td>\n",
       "      <td>2599</td>\n",
       "      <td>President Donald Trump 2016 election promise d...</td>\n",
       "      <td>0.225556</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.9738</td>\n",
       "      <td>0</td>\n",
       "      <td>President Donald Trump Congress Trump Congress...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28347</th>\n",
       "      <td>28347</td>\n",
       "      <td>From the “it ain’t gonna happen” department, B...</td>\n",
       "      <td>56.18</td>\n",
       "      <td>24.43</td>\n",
       "      <td>2016</td>\n",
       "      <td>“ ’ gon happen ” department , Baltimore Ravens...</td>\n",
       "      <td>0.232843</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.772</td>\n",
       "      <td>-0.8860</td>\n",
       "      <td>0</td>\n",
       "      <td>’ ” Baltimore Ravens Steve Bisciotti NFL NFL R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17174</th>\n",
       "      <td>17174</td>\n",
       "      <td>Two men were injured in a drive-by shooting at...</td>\n",
       "      <td>58.52</td>\n",
       "      <td>12.53</td>\n",
       "      <td>1025</td>\n",
       "      <td>men injure drive-by shooting South Africa Cape...</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.894</td>\n",
       "      <td>-0.5859</td>\n",
       "      <td>0</td>\n",
       "      <td>South Africa Cape Town International Airport W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>620</td>\n",
       "      <td>Republican lawmakers are pressing forward with...</td>\n",
       "      <td>46.57</td>\n",
       "      <td>11.77</td>\n",
       "      <td>947</td>\n",
       "      <td>Republican lawmaker press forward investigatio...</td>\n",
       "      <td>0.413333</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.9153</td>\n",
       "      <td>0</td>\n",
       "      <td>U.S. U.S. House Wednesday FBI Treasury State D...</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "       news_ID                                               text  \\\n",
       "13937    13937  The death toll in a militant attack on a mosqu...   \n",
       "22032    22032  President Donald Trump won the 2016 election p...   \n",
       "28347    28347  From the “it ain’t gonna happen” department, B...   \n",
       "17174    17174  Two men were injured in a drive-by shooting at...   \n",
       "620        620  Republican lawmakers are pressing forward with...   \n",
       "\n",
       "       readability_score  reading_time  count  \\\n",
       "13937              65.39          1.97    164   \n",
       "22032              56.18         31.91   2599   \n",
       "28347              56.18         24.43   2016   \n",
       "17174              58.52         12.53   1025   \n",
       "620                46.57         11.77    947   \n",
       "\n",
       "                                           filtered_text  Subjectivity  \\\n",
       "13937  death toll militant attack mosque Egypt north ...      0.000000   \n",
       "22032  President Donald Trump 2016 election promise d...      0.225556   \n",
       "28347  “ ’ gon happen ” department , Baltimore Ravens...      0.232843   \n",
       "17174  men injure drive-by shooting South Africa Cape...      0.238095   \n",
       "620    Republican lawmaker press forward investigatio...      0.413333   \n",
       "\n",
       "       Negative  Positive  Neutral  Compound  label  \\\n",
       "13937     0.292     0.000    0.708   -0.7906      0   \n",
       "22032     0.071     0.149    0.781    0.9738      0   \n",
       "28347     0.151     0.077    0.772   -0.8860      0   \n",
       "17174     0.079     0.026    0.894   -0.5859      0   \n",
       "620       0.016     0.156    0.828    0.9153      0   \n",
       "\n",
       "                                                   names  \n",
       "13937                            Egypt Sinai MENA Friday  \n",
       "22032  President Donald Trump Congress Trump Congress...  \n",
       "28347  ’ ” Baltimore Ravens Steve Bisciotti NFL NFL R...  \n",
       "17174  South Africa Cape Town International Airport W...  \n",
       "620    U.S. U.S. House Wednesday FBI Treasury State D...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_name(article):\n",
    "    nltk_results = pos_tag(word_tokenize(article))\n",
    "    all_name_list = []\n",
    "    for result in nltk_results:\n",
    "        if \"NNP\" in result:\n",
    "            name = result[0]\n",
    "            all_name_list.append(name)\n",
    "    all_name_column = (\" \".join(all_name_list))\n",
    "    return all_name_column\n",
    "\n",
    "fake_sample[\"names\"] = fake_sample[\"filtered_text\"].apply(lambda x: count_name(x))\n",
    "true_sample[\"names\"] = true_sample[\"filtered_text\"].apply(lambda x: count_name(x))\n",
    "display(fake_sample.head())\n",
    "display(true_sample.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4da0b56e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Count_Fake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>84733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clinton</td>\n",
       "      <td>33124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hillary</td>\n",
       "      <td>21588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>donald</td>\n",
       "      <td>20538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obama</td>\n",
       "      <td>19983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word  Count_Fake\n",
       "0    trump       84733\n",
       "1  clinton       33124\n",
       "2  hillary       21588\n",
       "3   donald       20538\n",
       "4    obama       19983"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Count_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>94002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mr</td>\n",
       "      <td>63279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>president</td>\n",
       "      <td>26559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clinton</td>\n",
       "      <td>26030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>united</td>\n",
       "      <td>24092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word  Count_True\n",
       "0      trump       94002\n",
       "1         mr       63279\n",
       "2  president       26559\n",
       "3    clinton       26030\n",
       "4     united       24092"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fake_top_names = get_top_20_words(fake_sample, \"names\", 60).rename(columns={\"Frequency\": \"Count_Fake\"})\n",
    "true_top_names = get_top_20_words(true_sample, \"names\", 60).rename(columns={\"Frequency\": \"Count_True\"})\n",
    "display(fake_top_names.head())\n",
    "display(true_top_names.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ec6806c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Word</th>\n",
       "      <th>Count_Fake</th>\n",
       "      <th>Count_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>84733</td>\n",
       "      <td>94002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clinton</td>\n",
       "      <td>33124</td>\n",
       "      <td>26030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hillary</td>\n",
       "      <td>21588</td>\n",
       "      <td>6769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>donald</td>\n",
       "      <td>20538</td>\n",
       "      <td>16542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obama</td>\n",
       "      <td>19983</td>\n",
       "      <td>20887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>president</td>\n",
       "      <td>17640</td>\n",
       "      <td>26559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>america</td>\n",
       "      <td>13497</td>\n",
       "      <td>7558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>russia</td>\n",
       "      <td>13352</td>\n",
       "      <td>10914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>united</td>\n",
       "      <td>10860</td>\n",
       "      <td>24092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>house</td>\n",
       "      <td>10553</td>\n",
       "      <td>23106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>republican</td>\n",
       "      <td>8634</td>\n",
       "      <td>18791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>white</td>\n",
       "      <td>8578</td>\n",
       "      <td>13204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>news</td>\n",
       "      <td>8281</td>\n",
       "      <td>6648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>washington</td>\n",
       "      <td>7225</td>\n",
       "      <td>11063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>state</td>\n",
       "      <td>7036</td>\n",
       "      <td>13632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>syria</td>\n",
       "      <td>5904</td>\n",
       "      <td>6336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>department</td>\n",
       "      <td>5420</td>\n",
       "      <td>9184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>york</td>\n",
       "      <td>5298</td>\n",
       "      <td>11087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>john</td>\n",
       "      <td>5135</td>\n",
       "      <td>5467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>twitter</td>\n",
       "      <td>4970</td>\n",
       "      <td>6935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>american</td>\n",
       "      <td>4650</td>\n",
       "      <td>5120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>congress</td>\n",
       "      <td>4370</td>\n",
       "      <td>9193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>national</td>\n",
       "      <td>4292</td>\n",
       "      <td>6978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>party</td>\n",
       "      <td>4265</td>\n",
       "      <td>6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mr</td>\n",
       "      <td>3700</td>\n",
       "      <td>63279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>senate</td>\n",
       "      <td>3419</td>\n",
       "      <td>12318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cruz</td>\n",
       "      <td>3354</td>\n",
       "      <td>5124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sanders</td>\n",
       "      <td>3332</td>\n",
       "      <td>5459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>times</td>\n",
       "      <td>3314</td>\n",
       "      <td>4725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>china</td>\n",
       "      <td>3301</td>\n",
       "      <td>10910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>north</td>\n",
       "      <td>3298</td>\n",
       "      <td>9441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>bush</td>\n",
       "      <td>2978</td>\n",
       "      <td>4985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>court</td>\n",
       "      <td>2930</td>\n",
       "      <td>5910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>justice</td>\n",
       "      <td>2909</td>\n",
       "      <td>4609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>iran</td>\n",
       "      <td>2851</td>\n",
       "      <td>7787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Word  Count_Fake  Count_True\n",
       "0        trump       84733       94002\n",
       "1      clinton       33124       26030\n",
       "2      hillary       21588        6769\n",
       "3       donald       20538       16542\n",
       "4        obama       19983       20887\n",
       "5    president       17640       26559\n",
       "6      america       13497        7558\n",
       "7       russia       13352       10914\n",
       "8       united       10860       24092\n",
       "9        house       10553       23106\n",
       "10  republican        8634       18791\n",
       "11       white        8578       13204\n",
       "12        news        8281        6648\n",
       "13  washington        7225       11063\n",
       "14       state        7036       13632\n",
       "15       syria        5904        6336\n",
       "16  department        5420        9184\n",
       "17        york        5298       11087\n",
       "18        john        5135        5467\n",
       "19     twitter        4970        6935\n",
       "20    american        4650        5120\n",
       "21    congress        4370        9193\n",
       "22    national        4292        6978\n",
       "23       party        4265        6129\n",
       "24          mr        3700       63279\n",
       "25      senate        3419       12318\n",
       "26        cruz        3354        5124\n",
       "27     sanders        3332        5459\n",
       "28       times        3314        4725\n",
       "29       china        3301       10910\n",
       "30       north        3298        9441\n",
       "31        bush        2978        4985\n",
       "32       court        2930        5910\n",
       "33     justice        2909        4609\n",
       "34        iran        2851        7787"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_df = pd.merge(fake_top_names, true_top_names, on='Word')\n",
    "names_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ab11fc1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "human_names = [\"trump\", \"donald\", \"hillary\", \"clinton\", \"obama\"]\n",
    "countries = [\"america\", \"russia\", \"china\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d8ea0206",
   "metadata": {},
   "outputs": [],
   "source": [
    "human_names_df = pd.DataFrame(columns=[\"Word\", \"Count_Fake\", \"Count_True\"])\n",
    "for ind, row in names_df.iterrows():\n",
    "    if row[\"Word\"] in human_names:\n",
    "        human_names_df = human_names_df.append(row)\n",
    "\n",
    "countries_df = pd.DataFrame(columns=[\"Word\", \"Count_Fake\", \"Count_True\"])\n",
    "for ind, row in names_df.iterrows():\n",
    "    if row[\"Word\"] in countries:\n",
    "        countries_df = countries_df.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "145d6312",
   "metadata": {
    "scrolled": false
   },
   "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>Word</th>\n",
       "      <th>Count_Fake</th>\n",
       "      <th>Count_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>84733</td>\n",
       "      <td>94002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clinton</td>\n",
       "      <td>33124</td>\n",
       "      <td>26030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hillary</td>\n",
       "      <td>21588</td>\n",
       "      <td>6769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>donald</td>\n",
       "      <td>20538</td>\n",
       "      <td>16542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obama</td>\n",
       "      <td>19983</td>\n",
       "      <td>20887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word Count_Fake Count_True\n",
       "0    trump      84733      94002\n",
       "1  clinton      33124      26030\n",
       "2  hillary      21588       6769\n",
       "3   donald      20538      16542\n",
       "4    obama      19983      20887"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Word</th>\n",
       "      <th>Count_Fake</th>\n",
       "      <th>Count_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>america</td>\n",
       "      <td>13497</td>\n",
       "      <td>7558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>russia</td>\n",
       "      <td>13352</td>\n",
       "      <td>10914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>china</td>\n",
       "      <td>3301</td>\n",
       "      <td>10910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Word Count_Fake Count_True\n",
       "6   america      13497       7558\n",
       "7    russia      13352      10914\n",
       "29    china       3301      10910"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(human_names_df)\n",
    "display(countries_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "fc1a2a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hilary_fake = human_names_df.iloc[2][\"Count_Fake\"]\n",
    "hilary_true = human_names_df.iloc[2][\"Count_True\"]\n",
    "donald_fake = human_names_df.iloc[3][\"Count_Fake\"]\n",
    "donald_true = human_names_df.iloc[3][\"Count_True\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7afc2904",
   "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>Word</th>\n",
       "      <th>Count_Fake</th>\n",
       "      <th>Count_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>105271</td>\n",
       "      <td>110544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clinton</td>\n",
       "      <td>126859</td>\n",
       "      <td>117313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hillary</td>\n",
       "      <td>21588</td>\n",
       "      <td>6769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>donald</td>\n",
       "      <td>20538</td>\n",
       "      <td>16542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obama</td>\n",
       "      <td>19983</td>\n",
       "      <td>20887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word Count_Fake Count_True\n",
       "0    trump     105271     110544\n",
       "1  clinton     126859     117313\n",
       "2  hillary      21588       6769\n",
       "3   donald      20538      16542\n",
       "4    obama      19983      20887"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "human_names_df.iat[0,1] = human_names_df.iloc[0][\"Count_Fake\"] + donald_fake\n",
    "human_names_df.iat[0,2] = human_names_df.iloc[0][\"Count_True\"] + donald_true\n",
    "human_names_df.iat[1,1] = human_names_df.iloc[0][\"Count_Fake\"] + hilary_fake\n",
    "human_names_df.iat[1,2] = human_names_df.iloc[0][\"Count_True\"] + hilary_true\n",
    "display(human_names_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d6fded79",
   "metadata": {
    "scrolled": true
   },
   "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>Word</th>\n",
       "      <th>Count_Fake</th>\n",
       "      <th>Count_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trump</td>\n",
       "      <td>105271</td>\n",
       "      <td>110544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clinton</td>\n",
       "      <td>126859</td>\n",
       "      <td>117313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obama</td>\n",
       "      <td>19983</td>\n",
       "      <td>20887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word Count_Fake Count_True\n",
       "0    trump     105271     110544\n",
       "1  clinton     126859     117313\n",
       "4    obama      19983      20887"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "human_names_df = human_names_df.drop([human_names_df.index[2], human_names_df.index[3]])\n",
    "display(human_names_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "f137dd3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.032\n",
      "0.361\n"
     ]
    }
   ],
   "source": [
    "# ----- EXPLORATORY\n",
    "print(true_sample[\"Positive\"].quantile(0.05))\n",
    "print(fake_sample[\"Negative\"].quantile(0.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "d2be82c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.477\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(true_sample[\"Neutral\"].quantile(0.005))\n",
    "print(fake_sample[\"Neutral\"].quantile(0.995))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "d7f4a234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33031\n",
      "30708\n"
     ]
    }
   ],
   "source": [
    "print(len(true_sample[true_sample[\"Subjectivity\"] > 0.0]))\n",
    "print(len(fake_sample[fake_sample[\"Subjectivity\"] > 0.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "48a031b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33934\n",
      "32299\n"
     ]
    }
   ],
   "source": [
    "true_filtered = true_sample[(true_sample[\"Positive\"] > 0.032) | (true_sample[\"Neutral\"] > 0.586)]\n",
    "print(len(true_filtered))\n",
    "fake_filtered = fake_sample[(fake_sample[\"Negative\"] > 0.361) | (fake_sample[\"Neutral\"] < 0.962)]\n",
    "print(len(fake_filtered))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e959e29",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42d0518",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9406f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Pilsudski is a historical figure, who establis...</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Washington (and to a large degree Brussels) ar...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>The beneficiary of the resolution of the Europ...</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   news_ID                                               text\n",
       "0        0  Ukraine has put itself in a situation when ext...\n",
       "1        1  Regardless who was behind the recent attack on...\n",
       "2        2  Pilsudski is a historical figure, who establis...\n",
       "3        3  Washington (and to a large degree Brussels) ar...\n",
       "4        4  The beneficiary of the resolution of the Europ..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "russian = pd.read_csv(\"EXTRA_RussianPropagandaSubset.csv\").rename(columns={\"Unnamed: 0\":\"news_ID\"})\n",
    "display(russian.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2facc203",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1325 duplicated rows present in russian propaganda news dataset.\n",
      "1 NaN row removed.\n",
      "Total number of rows remaining are: 6043 rows.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c5/pphx1c0n3lb12sy85_ybqxqm0000gn/T/ipykernel_20000/2217677230.py:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"text\"] = df[\"text\"].str.replace(\"[\\n\\r]\", \" \")\n",
      "/var/folders/c5/pphx1c0n3lb12sy85_ybqxqm0000gn/T/ipykernel_20000/2217677230.py:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"text\"] = df[\"text\"].str.replace(\"\\(@[\\w]+\\)\", \"\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ukraine has put itself in a situation when ext...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Regardless who was behind the recent attack on...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Pilsudski is a historical figure, who establis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Washington (and to a large degree Brussels) ar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>The beneficiary of the resolution of the Europ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   news_ID                                               text\n",
       "0        0  Ukraine has put itself in a situation when ext...\n",
       "1        1  Regardless who was behind the recent attack on...\n",
       "2        2  Pilsudski is a historical figure, who establis...\n",
       "3        3  Washington (and to a large degree Brussels) ar...\n",
       "4        4  The beneficiary of the resolution of the Europ..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news_ID</th>\n",
       "      <th>text</th>\n",
       "      <th>filtered_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ukraine has put itself in a situation when ext...</td>\n",
       "      <td>Ukraine situation external force dictate solve...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Regardless who was behind the recent attack on...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Pilsudski is a historical figure, who establis...</td>\n",
       "      <td>Pilsudski historical figure , establish Polish...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Washington (and to a large degree Brussels) ar...</td>\n",
       "      <td>Washington ( large degree Brussels ) engage fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>The beneficiary of the resolution of the Europ...</td>\n",
       "      <td>beneficiary resolution European Parliament , d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   news_ID                                               text  \\\n",
       "0        0  Ukraine has put itself in a situation when ext...   \n",
       "1        1  Regardless who was behind the recent attack on...   \n",
       "2        2  Pilsudski is a historical figure, who establis...   \n",
       "3        3  Washington (and to a large degree Brussels) ar...   \n",
       "4        4  The beneficiary of the resolution of the Europ...   \n",
       "\n",
       "                                       filtered_text  \n",
       "0  Ukraine situation external force dictate solve...  \n",
       "1  attack Saudi oil infrastructure , key question...  \n",
       "2  Pilsudski historical figure , establish Polish...  \n",
       "3  Washington ( large degree Brussels ) engage fo...  \n",
       "4  beneficiary resolution European Parliament , d...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "russian = cleanup_df(russian, \"russian propaganda\")\n",
    "russian[\"filtered_text\"] = russian[\"text\"].apply(lambda x: remove_stopwords_and_lemmatize(x))\n",
    "russian.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78148045",
   "metadata": {},
   "source": [
    "### Model Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33f6d556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    34000\n",
      "0    34000\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "fake_sample[\"label\"] = 1\n",
    "true_sample[\"label\"] = 0\n",
    "df = pd.concat([fake_sample, true_sample], axis=0)\n",
    "df = df[[\"filtered_text\", \"label\"]]\n",
    "print(df[\"label\"].value_counts())\n",
    "X = df[\"filtered_text\"].fillna(' ')\n",
    "y = df[\"label\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "593af105",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54400,) (13600,)\n",
      "(54400,) (13600,)\n",
      "(54400, 199366)\n",
      "(13600, 199366)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=11)\n",
    "tfidf_vec = TfidfVectorizer()\n",
    "tfidf_train = tfidf_vec.fit_transform(X_train)\n",
    "tfidf_test = tfidf_vec.transform(X_test)\n",
    "print(X_train.shape, X_test.shape)\n",
    "print(y_train.shape, y_test.shape)\n",
    "print(tfidf_train.shape)\n",
    "print(tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380fc58e",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d2d19caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy testing on preprocessed test set: 0.9459558823529411\n"
     ]
    }
   ],
   "source": [
    "lgs_model = LogisticRegression()\n",
    "lgs_model.fit(tfidf_train, y_train)\n",
    "lgs_prediction = lgs_model.predict(tfidf_test)\n",
    "print('Accuracy testing on preprocessed test set:',accuracy_score(y_test, lgs_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "bee2c99e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.47323529 0.02727941]\n",
      " [0.02676471 0.47272059]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgs_matrix=confusion_matrix(lgs_prediction, y_test, normalize='all')\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(lgs_matrix, annot=True, ax = ax, cmap=\"Blues\")\n",
    "print(lgs_matrix)\n",
    "\n",
    "ax.set_xlabel('Predicted Labels', size=10)\n",
    "ax.set_ylabel('True Labels', size=10)\n",
    "ax.set_title('Confusion Matrix for Logistic Regression', size=15)\n",
    "ax.xaxis.set_ticklabels([0,1], size=10)\n",
    "ax.yaxis.set_ticklabels([0,1], size=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7505666b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Russian propaganda articles classified to as fake news: 1333\n",
      "Russian propaganda articles classified to as true news: 32667\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95      6807\n",
      "           1       0.95      0.95      0.95      6793\n",
      "\n",
      "    accuracy                           0.95     13600\n",
      "   macro avg       0.95      0.95      0.95     13600\n",
      "weighted avg       0.95      0.95      0.95     13600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "russian_text = tfidf_vec.transform(true_sample[\"filtered_text\"].fillna(' ').values)\n",
    "russian_prediction = lgs_model.predict(russian_text)\n",
    "print(f\"Russian propaganda articles classified to as fake news: {russian_prediction.tolist().count(1)}\")\n",
    "print(f\"Russian propaganda articles classified to as true news: {russian_prediction.tolist().count(0)}\")\n",
    "print(classification_report(lgs_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "26b19cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32667 1333\n"
     ]
    }
   ],
   "source": [
    "true_text = tfidf_vec.transform(true_sample[\"filtered_text\"].fillna(' ').values)\n",
    "true_prediction = lgs_model.predict(russian_text)\n",
    "true_correct = true_prediction.tolist().count(0)\n",
    "true_false = true_prediction.tolist().count(1)\n",
    "print(true_correct, true_false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fd28bcc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32698 1302\n"
     ]
    }
   ],
   "source": [
    "fake_text = tfidf_vec.transform(fake_sample[\"filtered_text\"].fillna(' ').values)\n",
    "fake_prediction = lgs_model.predict(fake_text)\n",
    "fake_false = fake_prediction.tolist().count(0)\n",
    "fake_correct = fake_prediction.tolist().count(1)\n",
    "print(fake_correct, fake_false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "78df77cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5997 46\n"
     ]
    }
   ],
   "source": [
    "russian_text = tfidf_vec.transform(russian[\"filtered_text\"].fillna(' ').values)\n",
    "russian_prediction = lgs_model.predict(russian_text)\n",
    "russian_false = russian_prediction.tolist().count(0)\n",
    "russian_correct = russian_prediction.tolist().count(1)\n",
    "print(russian_correct, russian_false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "aa3d5cef",
   "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>News_Type</th>\n",
       "      <th>True</th>\n",
       "      <th>Fake</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True News</td>\n",
       "      <td>32667</td>\n",
       "      <td>1333</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fake News</td>\n",
       "      <td>1302</td>\n",
       "      <td>32698</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Propaganda</td>\n",
       "      <td>46</td>\n",
       "      <td>5997</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            News_Type   True   Fake                Model\n",
       "0           True News  32667   1333  Logistic Regression\n",
       "1           Fake News   1302  32698  Logistic Regression\n",
       "2  Russian Propaganda     46   5997  Logistic Regression"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgs_df = pd.DataFrame(columns=[\"News_Type\", \"True\", \"Fake\"])\n",
    "lgs_df.loc[len(lgs_df)] = [\"True News\", true_correct, true_false]\n",
    "lgs_df.loc[len(lgs_df)] = [\"Fake News\", fake_false, fake_correct]\n",
    "lgs_df.loc[len(lgs_df)] = [\"Russian Propaganda\", russian_false, russian_correct]\n",
    "lgs_df[\"Model\"] = \"Logistic Regression\"\n",
    "lgs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "0bbfd4ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51200,) (12800,)\n",
      "(51200,) (12800,)\n",
      "(51200, 164247)\n",
      "(12800, 164247)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=11)\n",
    "tfidf_vec = TfidfVectorizer()\n",
    "tfidf_train = tfidf_vec.fit_transform(X_train)\n",
    "tfidf_test = tfidf_vec.transform(X_test)\n",
    "print(X_train.shape, X_test.shape)\n",
    "print(y_train.shape, y_test.shape)\n",
    "print(tfidf_train.shape)\n",
    "print(tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1def5948",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy testing on preprocessed test set after NLP filtering: 0.946328125\n"
     ]
    }
   ],
   "source": [
    "lgs_model = LogisticRegression()\n",
    "lgs_model.fit(tfidf_train, y_train)\n",
    "lgs_prediction = lgs_model.predict(tfidf_test)\n",
    "print('Accuracy testing on preprocessed test set after NLP filtering:',accuracy_score(y_test, lgs_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "cbf3f86d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.47242187 0.02609375]\n",
      " [0.02757812 0.47390625]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, '0'), Text(0, 1.5, '1')]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bmVmlRSXpDyRdqotJLlH6EfCPiDgo14ZZp+QxVrNiWJ9keKCBpCv2ZuA7nSGopjYnucHC5iRfKK4gGVowa3fOWM3MzGrIl9uYmZnVkAOrmZlZDXXYMdYNPzzWfdRWCItnnJt3E8yqtkE31HqptVPt5/3bD55bt7atjQ4bWM3MrJNQsTpPHVjNzCxf6lAJZ9UcWM3MLF8Fy1iLdTZmZmY5c8ZqZmb5clewmZlZDRWsK9iB1czM8lWwjLVYXxPMzMxy5ozVzMzy5a5gMzOzGipYV7ADq5mZ5csZq5mZWQ0VLGMt1tcEMzOznDmwmplZvtSlukclVUjDJT0labakcS2U203SckmHZ9bNlfSIpIckzWytLncFm5lZvurcFSypK3AeMAxoBGZImhIRj5cpdxYwrcxhhkbES5XU54zVzMzyVf+MdTAwOyLmRMQSYBIwsky5bwF/AxZWczoOrGZmlq8qA6ukMZJmZh5jSmroDTyXWW5M161qgtQbOBSYUKaFAdws6f4yx16Du4LNzGydFhETgYktFCnX1xwly78Bvh8Ry7Vm1/TuETFPUi/gFklPRsT05ipzYDUzs3x1qfvlNo1A38xyH2BeSZldgUlpUN0COFDSsoi4JiLmAUTEQkmTSbqWHVjNzKyDqv8NImYAAyX1A54HRgOfzRaIiH4rmyNdDFwfEddI2gjoEhGvp3/vB5zWUmUOrGZmlq86zwqOiGWSxpLM9u0KXBgRj0k6Nt1ebly1SQMwOc1kuwGXRcRNLdXnwGpmZoUXEVOBqSXrygbUiDgq8/ccYOe21OXAamZm+fK9gs3MzGqoYPcKdmA1M7N8OWM1MzOroYJlrMX6mmBmZpYzZ6xmZpYvdwWbmZnVUMG6gh1YzcwsX85YzczMaqhgGWuxviaYmZnlzBmrmZnly13BZmZmNeTAamZmVkMeYzUzM7PmOGM1M7N8uSvYzMyshgrWFezAamZm+XLGamZmVkMFy1iL9TXBzMwsZ85YzcwsVypYxurAamZmuXJgNTMzq6VixVWPsZqZmdWSM1YzM8uVu4LNzMxqyIHVzMyshhxYzczMaqhogdWTl8zMrPAkDZf0lKTZksa1UG43ScslHd7WfZs4sJqZWb5U5aO1w0tdgfOAA4BBwGckDWqm3FnAtLbum+XAamZmuZJU1aMCg4HZETEnIpYAk4CRZcp9C/gbsHAt9l3JgdXMzHJVbWCVNEbSzMxjTEkVvYHnMsuN6bpsG3oDhwIT2rpvKU9eMjOzXFU7eSkiJgITW6qi3G4ly78Bvh8Ry0vaU8m+q3FgNTOzomsE+maW+wDzSsrsCkxKg+oWwIGSllW472ocWM3MLFftcLnNDGCgpH7A88Bo4LPZAhHRL9Oei4HrI+IaSd1a27eUA6uZmeWrznE1IpZJGksy27crcGFEPCbp2HR76bhqq/u2VJ8Dq5mZ5ao9bhAREVOBqSXrygbUiDiqtX1b4lnBZmZmNeSM1czMclW0Wxo6sJqZWa4cWM3MzGqpWHHVgdXMzPJVtIzVk5fMzMxqyBmrmZnlqmgZqwOrmZnlyoHVzMyshhxYzczMaqlYcdWTl8zMzGrJGauZmeXKXcFmZmY15MBqZmZWQ0ULrB5jNTMzqyFnrGZmlq9iJawOrGZmlq+idQU7sJqZWa6KFlg9xlpQwz6xPQ9P/jGPXnsqJ355WLPlPjJoK96Y+TsO3fdDAAzcuhf3Thq38rHgrrMZ+9m926fRZsA/7prOwSP256Dhw/jTBRPX2B4RnPmz0zlo+DAOP/RTPPH4YwDMf+EFjj7qCxzyqQM49OARXPqXP6+232WX/oWDR+zPoQeP4Nfn/KJdzsUqI6mqR0fjjLWAunQRvxk3ihFfP5fnF7zC3ZeexPV3PsKTc+avUe70b4/kln8+sXLdf55ZyMdGn7ly+3+nncGU2x9u1/Zb57V8+XJ+dsZpnH/BRTQ0NPDZIw9n76H7sO2AASvL3H3XdJ59Zi7X3Xgzj8x6mNNPG8+lk66ia7eunPi9cWw/aAfefPMNRh9xGB/7+O5sO2AA9/3rXu647Vaunnwd3bt3Z9GiRfmdpBWeM9YC2u2D2/Df515i7vOLWLpsOVdNe4CD9t5pjXLfGD2Ea259mBdffr3scYYO/gBPN77Isy8srneTzQB49JFZ9O27NX369mW97t0ZfuAI7rj91tXK3H7brXzq4EOQxE47f4jXX3+NF19cSM+evdh+0A4AbLRRD/r378/ChQsAuOqKy/nKMWPo3r07AJtvvnn7npi1qGgZa90Cq6TtJH1f0u8k/Tb9e/t61WervK/XJjQuWBUMn1+wmN49N1m9TM9NOHifnbng6ruaPc4R+3+EK2+6v27tNCu1cMECtnzvliuXezU0sGDBgtXLLFxAw5aryjQ0bMnCkjLPP9/Ik088wY477QzAM3Pn8sD9M/nc6CP4ypc+z6OPzKrjWVibqcpHB1OXwCrp+8AkklO+D5iR/n25pHEt7DdG0kxJM5e99Fg9mtYpqMw7LUqWzz7pMH7022tZsaJ0S2K9bl0ZMWRH/n7Lg3VooVl5scY7tczElmi5zFtvvsl3jz+Ok8b9gB49egCwbPlyXnvtNf56+ZWc8N3vcdJ3jyfKHMfyUbSMtV5jrEcDO0TE0uxKSb8CHgPOLLdTREwEJgJs+OGxftevpecXvkKfhk1XLvdu2JR5L766WpldBm3FJWd+GYDN39OD/ffYgWXLVnDdHck3+f33GMRDTz7Hwma6ic3qoaFhS+a/sGouwMIFC+jVq9dqZXo1bMmC+avKLFgwn55pmaVLl/Kd44/jwBGfYt9h+2WO28An9x2GJHbcaSe6dOnC4sWL2Wyzzep8RlaJjhgcq1GvruAVwPvKrH9vus3qaOZjzzBgq55s/b7NWa9bV47YfxduuGP1rq/tDxrPdiNOZbsRpzL5/z3I8T+/YmVQBRg1fFd3A1u72+GDO/Lss3NpbHyOpUuWcNPUGxgydJ/Vyuw9dB+um3INEcGshx+iR4+N6dmzFxHB+FN+SP/+/fniUV9ebZ+hn9yX+/51LwBz5z7N0qVL2XTTTTGrh3plrMcDt0r6D/Bcum4rYAAwtk51Wmr58hWccNaVXPf7b9K1i/jztffyxJz5HHP4HgD88eq7W9x/ww3WY5+PbsfY0y9vj+aardStWzdO/uEpfH3MMaxYsZxDDj2MAQMGcuUVyXtx1JGfYc+9hnD39Ds56IBhbLDBhpx2+s8AePCB+7l+yrUMfP/7GfXpkQB86/jvsOdeQzj00MM45cc/4NMjD2K99dbjp2ecWbgsaV1WtJdC9RpnkNQFGAz0JhlfbQRmRMTySvZ3V7AVxeIZ5+bdBLOqbdCtftOEBp50U1Wf9/85e3iHCs11u441IlYA99br+GZmVgztkbFKGg78FugK/DEizizZPhL4Kclw5TLg+Ii4O902F3gdWA4si4hdW6rLN4gwM7NCk9QVOA8YRtp7KmlKRDyeKXYrMCUiQtJOwJXAdpntQyPipUrqc2A1M7NctcN492BgdkTMSeubBIwEVgbWiHgjU34j1rxKsWK+85KZmeVKqu5Rgd6smkgLSdbae8126FBJTwI3AF/JbArgZkn3SxrTWmXOWM3MLFddulSXsabBLhvwJqb3RVhZpMxua2SkETEZmCxpL5Lx1n3TTbtHxDxJvYBbJD0ZEdOba48Dq5mZ5aranuDszYWa0Qj0zSz3Aea1cLzpkraVtEVEvBQR89L1CyVNJulabjawuivYzMyKbgYwUFI/Sd2B0cCUbAFJA5QO9kraBegOLJK0kaSN0/UbAfsBj7ZUmTNWMzPLVb0nL0XEMkljgWkkl9tcGBGPSTo23T4BOAz4oqSlwNvAkekM4QaS7mFIYuZlEXFTS/U5sJqZWa7a4zrWiJgKTC1ZNyHz91nAWWX2mwPs3Ja6HFjNzCxXRbu9pAOrmZnlqmiB1ZOXzMzMasgZq5mZ5apgCasDq5mZ5atoXcEOrGZmlquCxVWPsZqZmdWSM1YzM8uVu4LNzMxqqGBx1YHVzMzy5YzVzMyshgoWVz15yczMrJacsZqZWa7cFWxmZlZDBYurDqxmZpYvZ6xmZmY1VLC46slLZmZmteSM1czMcuWuYDMzsxoqWFx1YDUzs3wVLWP1GKuZmVkNOWM1M7NcFS1jdWA1M7NcFSyuOrCamVm+nLGamZnVUMHiqicvmZmZ1ZIzVjMzy5W7gs3MzGqoYHHVXcFmZpavLlJVj0pIGi7pKUmzJY0rs32kpFmSHpI0U9Iele5byhmrmZnlqt4Zq6SuwHnAMKARmCFpSkQ8nil2KzAlIkLSTsCVwHYV7rsaZ6xmZlZ0g4HZETEnIpYAk4CR2QIR8UZERLq4ERCV7lvKgdXMzHIlqdrHmLT7tukxpqSK3sBzmeXGdF1pOw6V9CRwA/CVtuyb5a5gMzPLVZcqu4IjYiIwsYUi5WqINVZETAYmS9oL+Cmwb6X7ZjmwmplZrtrhcptGoG9muQ8wr7nCETFd0raStmjrvuCuYDMzK74ZwEBJ/SR1B0YDU7IFJA1QGuEl7QJ0BxZVsm8pZ6xmZpareiesEbFM0lhgGtAVuDAiHpN0bLp9AnAY8EVJS4G3gSPTyUxl922pPgdWMzPLlcoOY9ZWREwFppasm5D5+yzgrEr3bYkDq5mZ5arayUsdjQOrmZnlqmj3CvbkJTMzsxpyxmpmZrkqWMLaesYq6duS3q3EnyQ9IGm/9micmZkVX3vchL89VdIV/JWIeA3YD+gJfBk4s66tMjOzTkOq7tHRVBJYm5p9IHBRRDxM+Vs8mZmZdXqVjLHeL+lmoB9wsqSNgRX1bZaZmXUWRZsVXElgPRr4EDAnIt6StDlJd7CZmVnVChZXmw+s6b0Ss/oX7VuFmZnlryNOQKpGSxnrL1vYFsA+NW6LmZl1QsUKqy0E1ogY2p4NMTMzK4JKrmN9l6QfSZqYLg+UdFD9m2ZmZp2BpKoeHU0ll9tcBCwBPpEuNwKn161FZmbWqXRRdY+OppLAum1E/AJYChARb1O8LnEzM8tJ0TLWSi63WSJpQ5IJS0jaFninrq0yM7NOowPGxqpUElhPBW4C+kq6FNgdOKqejTIzM1tXtRpYI+IWSQ8AHyPpAv52RLxU95aZmVmn0BG7c6tR6c/GDQH2IOkOXg+YXLcWmZlZp9IRJyBVo9XAKun3wADg8nTV1yTtGxHfrGvLzMysU+iMGesQ4IMR0TR56c/AI3VtlZmZ2TqqksttngK2yiz3BWbVpzlmZtbZqMpHR9PSTfivIxlT3QR4QtJ96fJHgXvap3lmZlZ0nekm/Oe0WyvMzKzTKlhcbfEm/He2Z0PMzKxzKtrkpUpuwv8xSTMkvSFpiaTlkl5rj8aZmZmtayqZFXwuMBq4CtgV+CIwsJ6NMjOzzqNgCWtFs4KJiNlA14hYHhEXAXvXtVVmZtZpdJGqelRC0nBJT0maLWlcme2fkzQrfdwjaefMtrmSHpH0kKSZrdVVScb6lqTuwEOSfgG8AGxU0ZmYmZm1ot4Zq6SuwHnAMJKfPp0haUpEPJ4p9jQwJCIWSzoAmEhyFUyToZXezreSjPULabmxwJsk17F+upKDm5mZtaYdfjZuMDA7IuZExBJgEjAyWyAi7omIxenivUCftT2fSm7C/0z65/+AnwBIugI4cm0rNTMza0e9gecyy42sno2WOhq4MbMcwM2SAjg/Iia2VFmlN+Ev9fG13K9ii2ecW+8qzNrFpruNzbsJZlV7+8H6fSZXNNmnBZLGAGMyqyaWBL9yaW00c6yhJIF1j8zq3SNinqRewC2SnoyI6c21Z20Dq5mZWU1Uex1rGkRbyiIbSYYxm/QB5pVpx07AH4EDImJR5vjz0v8vlDSZpGu57YFV0i7NbSL56TgzM7OqtcPPxs0ABkrqBzxPcgnpZ7MFJG0F/B34QkT8O7N+I6BLRLye/r0fcFpLlbWUsf6yhW1PtngKZmZmHURELJM0FpgGdAUujIjHJB2bbp8AnAJsDvw+zaCXRcSuQAMwOV3XDbgsIm5qqb6Wbmk4tAbnY2Zm1qL2+KHziJgKTC1ZNyHz9zHAMWX2mwPsXLq+JR5jNTOzXBXtXsEOrGZmlqv2yFjbkwOrmZnlqmAJa0W/biNJn5d0Srq8laTB9W+amZnZuqeS63J/T3JDiM+ky6+T3HPRzMysau1xE/72VElX8EcjYhdJDwKkNyjuXud2mZlZJ1HtnZc6mkoC69L0lwECQFJPYEVdW2VmZp1GB0w6q1JJYP0dMBnoJekM4HDgR3VtlZmZdRodsTu3GpX8us2lku4HPklyO8NDIuKJurfMzMxsHdRqYE3vn/gWcF12XUQ8W8+GmZlZ51CwhLWiruAbSMZXBWwA9AOeAnaoY7vMzKyT6HQ3iIiIHbPL6a/efK1uLTIzs06laGOsbZ7lHBEPALvVoS1mZmbrvErGWL+TWewC7AK8WLcWmZlZp1KwhLWiMdaNM38vIxlz/Vt9mmNmZp1NpxpjTW8M0SMiTmqn9piZWScjihVZmw2skrqlv7q+S3s2yMzMOpfOlLHeRzKe+pCkKcBVwJtNGyPi73Vum5mZ2TqnkjHWzYBFwD6sup41AAdWMzOrWmfKWHulM4IfZVVAbRJ1bZWZmXUaKti04JYCa1egB5QdVXZgNTOzmuhMGesLEXFau7XEzMw6pYIlrC3eealgp2pmZlZ/LWWsn2y3VpiZWadVtHsFNxtYI+Ll9myImZl1Tp1pjNXMzKzuCpawtv3XbczMzKx5zljNzCxXXQo2V9YZq5mZ5Uqq7lFZHRou6SlJsyWNK7P9c5JmpY97JO1c6b6lnLGamVmu6j15Kf2ltvOAYUAjMEPSlIh4PFPsaWBIRCyWdAAwEfhohfuuxoHVzMxy1Q6X2wwGZkfEHABJk4CRwMrgGBH3ZMrfC/SpdN9S7go2M7N1mqQxkmZmHmNKivQGnsssN6brmnM0cONa7uuM1czM8lVtwhoRE0m6bputotxu5duioSSBdY+27tvEgdXMzHLVDl3BjUDfzHIfYF5pIUk7AX8EDoiIRW3ZN8tdwWZmlqt2mBU8AxgoqZ+k7sBoYMrqbdBWJL8z/oWI+Hdb9i3ljNXMzHJV7wwvIpZJGgtMI/lJ1Asj4jFJx6bbJwCnAJsDv09/H3ZZROza3L4t1efAamZmhRcRU4GpJesmZP4+Bjim0n1b4sBqZma5UsFuFuzAamZmuSpWWHVgNTOznBXt91g9K9jMzKyGnLGamVmuipWvOrCamVnOCtYT7MBqZmb58qxgMzOzGiraZJ+inY+ZmVmunLGamVmu3BVsZmZWQ8UKqw6sZmaWs6JlrB5jNTMzqyFnrGZmlquiZXgOrGZmlquidQU7sJqZWa6KFVYdWM3MLGcFS1gL17VtZmaWK2esZmaWqy4F6wx2YDUzs1wVrSvYgdXMzHIlZ6xmZma1U7SM1ZOXzMzMasgZq5mZ5cqTl8zMzGqoaF3BDqxmZparogVWj7GamZnVkAOrmZnlSlX+V1Ed0nBJT0maLWlcme3bSfqnpHcknViyba6kRyQ9JGlma3W5K9jMzHLVpc5dwZK6AucBw4BGYIakKRHxeKbYy8BxwCHNHGZoRLxUSX3OWM3MLFftkLEOBmZHxJyIWAJMAkZmC0TEwoiYASyt9nwcWM3MLFdSdY8K9Aaeyyw3pusqFcDNku6XNKa1wu4KNjOzdVoa7LIBb2JETMwWKbNbtKGK3SNinqRewC2SnoyI6c0VdmA1M7NcVXuv4DSITmyhSCPQN7PcB5jXhuPPS/+/UNJkkq7lZgOru4LNzCxXXVTdowIzgIGS+knqDowGplSyo6SNJG3c9DewH/BoS/s4YzUzs1zV+9dtImKZpLHANKArcGFEPCbp2HT7BElbAjOBdwMrJB0PDAK2ACYrGcztBlwWETe1VJ8Da4H8467pnHXmGaxYvoJDDzuCo7+6+hh7RHDWz8/g7ul3ssGGG/DTM85k+0E7MP+FF/jhyd9j0aKXkLpw+BGj+NwXvgTAk088wemnncqSd96ha7eu/OBH49lxp53yOD3rhIZ9YnvOOelwunbpwsXX3MM5F91SttxHBm3FnZecyBfGXcjk//cQA7fuxV/O+srK7f16b85P/3AD5152Rzu13NqiPe68FBFTgakl6yZk/p5P0kVc6jVg57bU5cBaEMuXL+dnZ5zG+RdcRENDA5898nD2HroP2w4YsLLM3XdN59ln5nLdjTfzyKyHOf208Vw66Sq6duvKid8bx/aDduDNN99g9BGH8bGP7862Awbw61+dzbHf+CZ77DmEu6bfyW9+dTZ/uvgv+Z2odRpduojfjBvFiK+fy/MLXuHuS0/i+jsf4ck589cod/q3R3LLP59Yue4/zyzkY6PPXLn9v9POYMrtD7dr+63z8hhrQTz6yCz69t2aPn37sl737gw/cAR33H7ramVuv+1WPnXwIUhip50/xOuvv8aLLy6kZ89ebD9oBwA22qgH/fv3Z+HCBUDSRfPGG28C8Mbrr9OzZ6/2PTHrtHb74Db897mXmPv8IpYuW85V0x7goL3X7C35xughXHPrw7z48utljzN08Ad4uvFFnn1hcb2bbGtJVT46GgfWgli4YAFbvnfLlcu9GhpYsGDB6mUWLqBhy1VlGhq2ZGFJmeefb+TJJ55gx52Sno/vjfsBvz7nF+z3ySH88pyzOO6E79TxLMxWeV+vTWhcsCoYPr9gMb17brJ6mZ6bcPA+O3PB1Xc1e5wj9v8IV950f93aadXrIlX16GjaPbBK+nIL28ZImilp5p8uaGnmtJWKMpdkqfQNFy2XeevNN/nu8cdx0rgf0KNHDwCuvOJyTvr+ydx8652c9P2TGf/jH9a24WbNKDehpfQdfPZJh/Gj317LihXlL0lcr1tXRgzZkb/f8mAdWmi1UrSMNY8x1p8AF5XbkL0W6X/L2nTxbqfX0LAl819YNfa0cMECevVavdu2V8OWLJi/qsyCBfPpmZZZunQp3zn+OA4c8Sn2HbbfyjLXXTuZ75+cBNP99j+An5zyo3qehtlKzy98hT4Nm65c7t2wKfNefHW1MrsM2opLzky+q2/+nh7sv8cOLFu2guvumAXA/nsM4qEnn2NhM93EZvVQl8AqaVZzm4CGetTZ2e3wwR159tm5NDY+R0OvBm6aegM/P/uXq5XZe+g+TLrsrww/cASPzHqYHj02pmfPXkQE40/5If379+eLR63eodCzVy9mzriP3QZ/lPv+dS9bbb1NO56VdWYzH3uGAVv1ZOv3bc68ha9wxP67cNTJF69WZvuDxq/8e+JPPs+Ndz26MqgCjBq+q7uB1wUdMe2sQr0y1gZgf6B0toCAe+pUZ6fWrVs3Tv7hKXx9zDGsWLGcQw49jAEDBnLlFZcDMOrIz7DnXkO4e/qdHHTAMDbYYENOO/1nADz4wP1cP+VaBr7//Yz6dHJf6m8d/x323GsIp4z/Kb8482csX7aM7uuvzynjT8vtHK1zWb58BSecdSXX/f6bdO0i/nztvTwxZz7HHL4HAH+8+u4W999wg/XY56PbMfb0y9ujuVaFel/H2t4UZcbdqj6o9CfgoohY450v6bKI+Gxrx3BXsBXFpruNzbsJZlV7+8Fz6xb97pvzalWf94P7b9KhInNdMtaIOLqFba0GVTMz6zw6VFSsAV9uY2ZmVkO+85KZmeWrYCmrA6uZmeWqaJOXHFjNzCxXHfDmSVVxYDUzs1wVLK568pKZmVktOWM1M7N8FSxldWA1M7NcefKSmZlZDRVt8pLHWM3MzGrIGauZmeWqYAmrA6uZmeWsYJHVgdXMzHLlyUtmZmY15MlLZmZm1ixnrGZmlquCJawOrGZmlrOCRVYHVjMzy5UnL5mZmdWQJy+ZmZmtYyQNl/SUpNmSxpXZvp2kf0p6R9KJbdm3lAOrmZnlSlU+Wj2+1BU4DzgAGAR8RtKgkmIvA8cB56zFvqtxYDUzs3zVO7LCYGB2RMyJiCXAJGBktkBELIyIGcDStu5byoHVzMxypWr/k8ZImpl5jCmpojfwXGa5MV1XiTbv68lLZma2TouIicDEFoqUy2ujwsO3eV8HVjMzy1U7zApuBPpmlvsA8+q1r7uCzcwsV/UfYmUGMFBSP0ndgdHAlAqb1+Z9nbGamVm+6pyxRsQySWOBaUBX4MKIeEzSsen2CZK2BGYC7wZWSDoeGBQRr5Xbt6X6HFjNzCxX7XHnpYiYCkwtWTch8/d8km7eivZtibuCzczMasgZq5mZ5apotzR0YDUzs1wVLK46sJqZWc4KFlkdWM3MLFdF+9k4T14yMzOrIWesZmaWK09eMjMzq6GCxVUHVjMzy1nBIqvHWM3MzGrIGauZmeWqaLOCHVjNzCxXnrxkZmZWQwWLqw6sZmaWr6JlrJ68ZGZmVkPOWM3MLGfFSlkdWM3MLFdF6wp2YDUzs1wVLK46sJqZWb6KlrF68pKZmVkNOWM1M7Nc+c5LZmZmtVSsuOrAamZm+SpYXPUYq5mZWS05YzUzs1wVbVawA6uZmeXKk5fMzMxqqVhx1WOsZmaWL1X5qKgOabikpyTNljSuzHZJ+l26fZakXTLb5kp6RNJDkma2VpczVjMzKzRJXYHzgGFAIzBD0pSIeDxT7ABgYPr4KPCH9P9NhkbES5XU54zVzMxyJVX3qMBgYHZEzImIJcAkYGRJmZHAJZG4F3iPpPeuzfk4sJqZWa5U7X/SGEkzM48xJVX0Bp7LLDem6yotE8DNku4vc+w1uCvYzMxyVe3lNhExEZjYUhXldmtDmd0jYp6kXsAtkp6MiOnNVeaM1czMiq4R6JtZ7gPMq7RMRDT9fyEwmaRruVkOrGZmVnQzgIGS+knqDowGppSUmQJ8MZ0d/DHg1Yh4QdJGkjYGkLQRsB/waEuVuSvYzMxyVe87L0XEMkljgWlAV+DCiHhM0rHp9gnAVOBAYDbwFvDldPcGYLKSRnYDLouIm1qqTxGl3cwdw/+WrdH/bbZO2nS3sXk3waxqbz94bt3C36tvr6jq836TDbt0qFtMOGM1M7NcFe1ewR5jNTMzqyFnrGZmlquCJawOrGZmlrOCRVYHVjMzy5V/Ns7MzKyGPHnJzMzMmuWM1czMclWwhNWB1czMclawyOrAamZmuSra5CWPsZqZmdWQM1YzM8tV0WYFd9ib8Fv9SRqT/kCw2TrN72XrSNwV3LmNybsBZjXi97J1GA6sZmZmNeTAamZmVkMOrJ2bx6SsKPxetg7Dk5fMzMxqyBmrmZlZDTmwdlKShkt6StJsSePybo/Z2pB0oaSFkh7Nuy1mTRxYOyFJXYHzgAOAQcBnJA3Kt1Vma+ViYHjejTDLcmDtnAYDsyNiTkQsASYBI3Nuk1mbRcR04OW822GW5cDaOfUGnsssN6brzMysSg6snVO5O3N6eriZWQ04sHZOjUDfzHIfYF5ObTEzKxQH1s5pBjBQUj9J3YHRwJSc22RmVggOrJ1QRCwDxgLTgCeAKyPisXxbZdZ2ki4H/gl8QFKjpKPzbpOZ77xkZmZWQ85YzczMasiB1czMrIYcWM3MzGrIgdXMzKyGHFjNzMxqyIHV1nmSlkt6SNKjkq6S9K4qjnWxpMPTv//Y0o8TSNpb0ifWoo65kraodH0zxzhK0rm1qNfMasuB1Yrg7Yj4UER8EFgCHJvdmP6aT5tFxDER8XgLRfYG2hxYzazYHFitaO4CBqTZ5O2SLgMekdRV0tmSZkiaJelrAEqcK+lxSTcAvZoOJOkOSbumfw+X9ICkhyXdKmkbkgB+Qpot7ympp6S/pXXMkLR7uu/mkm6W9KCk8yl/r+ayJA2WdE+67z2SPpDZ3FfSTenv6p6a2efzku5L23V+6RcLSRtJuiE9l0clHdnWJ9nMmtct7waY1YqkbiS/MXtTumow8MGIeFrSGODViNhN0vrAPyTdDHwY+ACwI9AAPA5cWHLcnsAFwF7psTaLiJclTQDeiIhz0nKXAb+OiLslbUVyZ6vtgVOBuyPiNEkjgDFtOK0n03qXSdoX+BlwWPb8gLeAGekXgzeBI4HdI2KppN8DnwMuyRxzODAvIkak7d6kDe0xs1Y4sFoRbCjpofTvu4A/kXTR3hcRT6fr9wN2aho/BTYBBgJ7AZdHxHJgnqTbyhz/Y8D0pmNFRHO//7kvMEhamZC+W9LGaR2fTve9QdLiNpzbJsCfJQ0k+QWi9TLbbomIRQCS/g7sASwDPkISaAE2BBaWHPMR4BxJZwHXR8RdbWiPmbXCgdWK4O2I+FB2RRpU3syuAr4VEdNKyh1I6z+ZpwrKQDK08vGIeLtMW9b23qE/BW6PiEPT7uc7MttKjxlpW/8cESc3d8CI+LekjwAHAj+XdHNEnLaW7TOzEh5jtc5iGvB1SesBSHq/pI2A6cDodAz2vcDQMvv+ExgiqV+672bp+teBjTPlbib5cQPSch9K/5xO0h2LpAOATdvQ7k2A59O/jyrZNkzSZpI2BA4B/gHcChwuqVdTWyVtnd1J0vuAtyLir8A5wC5taI+ZtcIZq3UWfwS2AR5QkkK+SBKMJgP7kHSP/hu4s3THiHgxHaP9u6QuJF2rw4DrgKsljQS+BRwHnCdpFsm/rekkE5x+Alwu6YH0+M+20M5Zklakf18J/IKkK/g7QGk39d3AX4ABwGURMRNA0o+Am9O2LgW+CTyT2W9H4Oy0nqXA11toj5m1kX/dxszMrIbcFWxmZlZDDqxmZmY15MBqZmZWQw6sZmZmNeTAamZmVkMOrGZmZjXkwGpmZlZDDqxmZmY19P8BSB2K0mhZfiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgs_matrix=confusion_matrix(lgs_prediction, y_test, normalize='all')\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(lgs_matrix, annot=True, ax = ax, cmap=\"Blues\")\n",
    "print(lgs_matrix)\n",
    "\n",
    "ax.set_xlabel('Predicted Labels', size=10)\n",
    "ax.set_ylabel('True Labels', size=10)\n",
    "ax.set_title('Confusion Matrix for Logistic Regression', size=15)\n",
    "ax.xaxis.set_ticklabels([0,1], size=10)\n",
    "ax.yaxis.set_ticklabels([0,1], size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468f8fa4",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "695299b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy testig on preprocessed test set: 0.9559558823529412\n"
     ]
    }
   ],
   "source": [
    "svm_model=svm.SVC(kernel='linear')\n",
    "svm_model.fit(tfidf_train, y_train)\n",
    "svm_prediction = svm_model.predict(tfidf_test)\n",
    "print('Accuracy testig on preprocessed test set:',accuracy_score(y_test, svm_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf6bcad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.47933824 0.02338235]\n",
      " [0.02066176 0.47661765]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ry+fbP0926dP0fP/PkwX1JRfy72pM3uaPyD6PR9Xyb6q1unwU82j+gErqRiKi+TrcHVJKtxbdnq4WEV8B/kR22dbzCysv1cMAKnUDEXEa8CBZj8VngZ+Qd6Wmilmc3VVE/IGsK/RNskt/fgzcnVLavdCGqVtzDFTqHpYk69YfSNaFejPw3Z4QPHMrkN2IYAWyE4fLyYYEpIYxA5UkqQ5exiJJUh0MoJIk1WGxHQPtu+kY+5bVLbxx35lFN0HqtL59iIWXqrPuTn7ff/jgmQ1rW3sW2wAqSeohopydoQZQSVKxopAEstMMoJKkYpU0Ay1nqyVJKpgZqCSpWHbhSpJUh5J24RpAJUnFKmkGWs6wL0lSwcxAJUnFsgtXkqQ6lLQL1wAqSSqWGagkSXUoaQZazrAvSVLBzEAlScWyC1eSpDqUtAvXACpJKpYZqCRJdShpAC1nqyVJKpgZqCSpWE2OgUqS1HEl7cI1gEqSilXSWbjlDPuSJBXMDFSSVCy7cCVJqkNJu3ANoJKkYpmBSpJUh5JmoOUM+5IkFcwMVJJULLtwJUmqQ0m7cA2gkqRimYFKklSHkmag5Qz7kiQVzAxUklQsu3AlSaqDAVSSpDo4BipJUs9hBipJKpZduJIk1aGkXbgGUElSscxAJUmqQ0kz0HKGfUmSCmYGKkkqVJQ0AzWASpIKZQCVJKke5YyfjoFKklQPM1BJUqHswpUkqQ4GUEmS6mAAlSSpDmUNoE4ikiSpDmagkqRilTMBNYBKkoplF64kSXWIiE49atzHiIh4OiKeiYhj2ym3ZUTMjYj9FlanGagkqVCNzkAjohdwFjAcmApMjojxKaUnWil3GnBTLfWagUqSuruhwDMppedSSh8B44CRrZQ7HPgrMKuWSg2gkqRCdbYLNyJGR8SUisfoql2sDLxcsTw1X1fZhpWBvYFzam23XbiSpGJ1sgc3pTQWGNvBPaSq5TOA76eU5tbapWwAlSQVqgtm4U4FVq1YXgWYXlVmC2Bc3pYVgV0jYk5K6Zq2KjWASpK6u8nAWhExGJgGHAT8Z2WBlNLg5r8j4iLguvaCJxhAJUkFa3QGmlKaExFjyGbX9gIuSCk9HhGH5ttrHvesZACVJBWqK26kkFKaCEysWtdq4EwpjaqlTgOoJKlY5bwRkQFUklQsb+UnSVIPYgYqSSpUWTNQA6gkqVAGUEmS6mAAlSSpHuWMn04ikiSpHmagkqRC2YUrSVIdDKCSJNWhrAHUMVBJkupgBipJKlY5E1ADqCSpWGXtwjWASpIKVdYA6hhoNzX8C+vy8NU/4bFrj+forw9vs9zm663Ge1N+x947btKy7vAvb8/9V/2IKVf+kItPHcWSS3iepa5z9113MHL3ndljl+FccN7YBbanlDjtlJPZY5fh7L/3Hjz5xOMAzHjlFQ7++lfZe49d2GfkbvzlTxe3POes35/B/nvvwQH7juTQb/43s2bN7LLj0cJFRKceRTGAdkNNTcEZxx7AyDFns+m+J7P/iM1ZZ8igVsud/J2R/P2eJ1vWrdR/WQ770jC2+vIv2GL/U+jV1MT+O2/elc1XDzZ37lxOPfmnnPWH8/jb+Ou5ceJ1PPvsM/OVuevOO3jppRcYP/FmfnLCSfzspBMA6NW7F9875liunnADf7r0ci4fd2nLc7/29YO58uoJXPHXa9l22HaM/cNZXX1o6oYMoN3QlhuswbMvv8YL015n9py5XHnTA+y+3UYLlDvsoGFcM+lhXn3j3fnW9+7Vi75L9qFXryb6LrUEr7z6dlc1XT3cY48+wqqrrc4qq65Knz5LsPMuu3HbLZPmK3PbrZPYfc+9iAg22ngT3n33HV59dRb9+w9g3fXWB2DppfsxZMgQZs3MMs1+/fq1PP/DDz8sbZdhd1XWDLRhfXMRsQ4wElgZSMB0YHxK6cl2n6hOW2nAskyd+WbL8rSZbzJ0gzXmL9N/WfbcYWNGjP4dm6//5Zb10199mzMumcQ/bziJD//9EZPueYpJ9z7VVU1XDzdr1kwGDfq4t2TgwIE8+ugj85eZWV1mELNmzqR//wEt66ZNm8pTTz7Jhhtt3LLu97/9DdeNv4Z+yyzDuRdc0sCjUIeV9HymIRloRHwfGEf2stwHTM7/viwijm3neaMjYkpETJnz2uONaFqPEK18GlPV8unH7MuPf3st8+bNv2W5Zfqy+3Ybsu7uxzNkpx+xdN8lOGjXLRvYWuljKVV/UhecYLKwMh988D5HH3UEx3z/h/Nlnod/5yhumnQ7u+62B+Mu/fMibLU6ywx0ft8A1k8pza5cGRG/Bh4Hft7ak1JKY4GxAH03HbPgvxLVZNqst1hl4Kdallce+CmmV3XDbrbealzy868DsMJy/dh56/WZM2cefXr34oXpr/Pam+8BcM0tD/O5jQczbuLkrjsA9VgDBw5ixowZLcszqzJLgIGDqsvMoP+ArMzs2bP53pFHsOtue/DF4Tu1uo9ddtudww87hMPGHNGAI1A9ytql3qgx0HnASq2s/3S+TQ005fEXWXO1/qy+0gr06d2L/XfejOtvm78bbN3dT2Cd3Y5nnd2O5+r/eZAjT72cCbc9wssz3mDohoPpu1QfALYf+lmeft4Zi+oa62+wIS+99ALTpr7M7NkfcdMN1zNs+x3mKzNsux24bvw1pJR45OGH6NdvGfr3H0BKiROP+xGDhwzhq1/7+nzPefHFF1r+vv3WWxg8eEhXHI66uUZloEcCkyLi/4CX83WrAWsCYxq0T+Xmzp3HUaddwYSzv02vpuDia+/lyedmcPB+WwNw3lV3tfncyY+9yNX/8yD3XPp95sydx8NPTeX8v97dVU1XD9e7d2+O/eFxfOuQg5k3dy4j996XNddciysvvwyA/Q/8EttsO4y77rydPXYZzlJ9+3LiSacA8NCD93PdhGtZa621OWDfkQAc/p3vss22w/jdb37FCy88T1MEn15pZX503ImFHaMWVNIElGhtPGGRVBzRBAwlm0QUwFRgckppbi3PtwtX3cUb951ZdBOkTuvbp3FTfdY65sZOfd//3+kjCgnBDZuFm1KaB9zbqPolSd1DWTNQrwOVJKkO3qNNklSoss7CNYBKkgpV0vhpAJUkFaupqZwR1AAqSSpUWTNQJxFJklQHM1BJUqGcRCRJUh1KGj8NoJKkYpmBSpJUh7IGUCcRSZJUBzNQSVKhSpqAGkAlScUqaxeuAVSSVKiSxk/HQCVJqocZqCSpUHbhSpJUh5LGTwOoJKlYZqCSJNWhpPHTSUSSJNXDDFSSVCi7cCVJqkNJ46cBVJJULDNQSZLqUNL46SQiSZLqYQYqSSqUXbiSJNWhpPHTACpJKlZZM1DHQCVJqoMZqCSpUGXNQA2gkqRClTR+GkAlScUyA5UkqQ4ljZ9OIpIkqR5moJKkQtmFK0lSHUoaPw2gkqRiNZU0gjoGKkkqVETnHrXtI0ZExNMR8UxEHNvK9pER8UhEPBQRUyJi64XVaQYqSerWIqIXcBYwHJgKTI6I8SmlJyqKTQLGp5RSRGwEXAGs0169BlBJUqG6YBLRUOCZlNJz+f7GASOBlgCaUnqvovzSQFpYpXbhSpIK1RSde0TE6LzbtfkxumoXKwMvVyxPzdfNJyL2joingOuB/15Yu81AJUmF6mwGmlIaC4xtbxetPa2Veq4Gro6IbYGTgB3b268ZqCSpu5sKrFqxvAowva3CKaU7gM9ExIrtVWoAlSQVqgtm4U4G1oqIwRGxBHAQMH7+NsSakafCEbEZsATwenuV2oUrSSpUtNrDuuiklOZExBjgJqAXcEFK6fGIODTffg6wL/BfETEb+BA4MKXU7kQiA6gkqVBNXXAfhZTSRGBi1bpzKv4+DTitI3UaQCVJhSrrvXAdA5UkqQ5moJKkQpU0AV14BhoR34mIT0bm/Ih4ICJ26orGSZK6v6aITj0Ka3cNZf47pfQOsBPQH/g68POGtkqS1GN0xc3kG6GWANrcvF2BC1NKD1eskySpR6plDPT+iLgZGAz8ICKWAeY1tlmSpJ6irLNwawmg3wA2AZ5LKX0QESuQdeNKktRpJY2fbQfQ/FZGlYaU9SxBkrT4KnIiUGe0l4H+qp1tCdhhEbdFktQDlTN8thNAU0rbd2VDJEkqk1quA/1ERPw4Isbmy2tFxO6Nb5okqSeIiE49ilLLZSwXAh8BX8iXpwInN6xFkqQepSk69yis3TWU+UxK6RfAbICU0oeUt8takrSYKWsGWstlLB9FRF+yiUNExGeAfze0VZKkHqOkk3BrCqDHAzcCq0bEX4CtgFGNbJQkSYu7hQbQlNLfI+IB4HNkXbffSSm91vCWSZJ6hLLeY6DWnzMbBmxN1o3bB7i6YS2SJPUoRU4E6oyFBtCIOBtYE7gsX3VIROyYUvp2Q1smSeoRunMGOgzYIKXUPInoYuDRhrZKkqTFXC2XsTwNrFaxvCrwSGOaI0nqaaKTj6K0dzP5CWRjnssCT0bEffnyfwD/6JrmSZK6u+54M/lfdlkrJEk9VknjZ7s3k7+9KxsiSeqZyjqJqJabyX8uIiZHxHsR8VFEzI2Id7qicZIkLa5qmYV7JnAQcCWwBfBfwFqNbJQkqecoaQJa240UUkrPRESvlNJc4MKIcBKRJGmR6I6TiJp9EBFLAA9FxC+AV4ClG9ssSVJPUdL4WdN1oF/Ny40B3ie7DnSfRjZKktRzdNufM0spvZj/+S/gRICIuBw4sIHtkiRpsVbrzeSrfX6RtqIVb04+s9G7kLrEp4YeUXQTpE778IHfNazuWrpCF0f1BlBJkhaJsl4H2t6t/DZraxPZT5pJktRp3fHnzH7VzranFnVDJEkqk/Zu5bd9VzZEktQzdccMVJKkhut2Y6CSJHUFM1BJkupQ0gS0pl9jiYj4SkQcly+vFhFDG980SZIWX7Vcv3o22Y0TvpQvvwuc1bAWSZJ6lKaITj2KUksX7n+klDaLiAcBUkpv5jeXlySp07rznYhmR0QvIAFERH9gXkNbJUnqMco6BlpLAP0dcDUwICJ+BuwH/LihrZIk9Rjd9vdAU0p/iYj7gS+S3cZvr5TSkw1vmSRJi7GFBtCIWA34AJhQuS6l9FIjGyZJ6hlKmoDW1IV7Pdn4ZwBLAYOBp4H1G9guSVIP0W1vpJBS2rByOf+VlkMa1iJJUo9S1jHQDs8eTik9AGzZgLZIklQatYyBfrdisQnYDHi1YS2SJPUoJU1AaxoDXabi7zlkY6J/bUxzJEk9TbccA81voNAvpXRMF7VHktTDBOWMoG0G0IjonVKak08akiSpIbpjBnof2XjnQxExHrgSeL95Y0rpbw1umyRJi61axkCXB14HduDj60ETYACVJHVad8xAB+QzcB/j48DZLDW0VZKkHiNKOg23vQDaC+gHrY7uGkAlSYtEd8xAX0kp/bTLWiJJ6pFKmoC2eyeikh6SJEmN114G+sUua4Ukqccq671w2wygKaU3urIhkqSeqTuOgUqS1HAlTUA7/msskiTJACpJKlgT0alHLSJiREQ8HRHPRMSxrWz/ckQ8kj/+EREbL6xOu3AlSYVqdBdu/sMoZwHDganA5IgYn1J6oqLY88CwlNKbEbELMBb4j/bqNYBKkgrVBZOIhgLPpJSeA4iIccBIoCWAppT+UVH+XmCVhVVqF64kqVBNEZ16RMToiJhS8RhdtYuVgZcrlqfm69ryDeCGhbXbDFSSVGoppbFkXa5tqfmWtBGxPVkA3Xph+zWASpIK1QWXsUwFVq1YXgWYvmA7YiPgPGCXlNLrC6vUACpJKlQX3IloMrBWRAwGpgEHAf9ZWSAiViP7mc6vppT+WUulBlBJUqEaHT9TSnMiYgxwE9kvjV2QUno8Ig7Nt58DHAesAJyd/7zanJTSFu3VawCVJBWqK2azppQmAhOr1p1T8ffBwMEdqdNZuJIk1cEMVJJUqCjpzXANoJKkQpUzfBpAJUkFK+vvgToGKklSHcxAJUmFKmf+aQCVJBWspD24BlBJUrGchStJUh3KOhmnrO2WJKlQZqCSpELZhStJUh3KGT4NoJKkgpU1A3UMVJKkOpiBSpIKVdZMzgAqSSpUWbtwDaCSpEKVM3waQCVJBStpAlrarmdJkgplBipJKlRTSTtxDaCSpEKVtQvXACpJKlSYgUqS1HFlzUCdRCRJUh3MQCVJhXISkSRJdShrF64BVJJUqLIGUMdAJUmqgxmoJKlQXsYiSVIdmsoZPw2gkqRimYFKklQHJxFJktSDmIFKkgplF64kSXVwEpEkSXUoawbqGGg3cvedd7Dnbjuz+4jhnH/u2AW2p5T4+Skns/uI4ey39x48+cTjAMx45RW+Meqr7LXHLuy952785U8Xtzzn5ptuYO89d2OTDdbh8cce7bJjkQCGf2FdHv7bj3js2p9w9Kgd2yy3+Xqr8d7kM9j7i5u0rDv8y9tx/5U/YMoVx3LxKV9jySXMFxZXEZ17FMUA2k3MnTuXU372U84+5zyuHn89N068jmefeWa+MnfdeQcvvfgCE264meNOOImTf3oCAL169+Lo/3cs10y4gT9fdjnjLru05blrrrk2v/nt79l8iy27+IjU0zU1BWd8f39GHn4Om+57CvuP2Jx1Bg9qtdzJ39mTv9/zZMu6lfovy2EHDWOrr/ySLQ74Ob2amth/5826svnqAQyg3cRjjz7CqquuziqrrkqfJZZgxK67cdutk+Yrc+stk9hjz72ICDbaeBPeffcdXn11Fv37D2Dd9dYHYOml+zFkyBBmzZoJwJDPfIY1Bg/p8uORttxgdZ6d+iovTHud2XPmcuVND7D7dhsuUO6wg4ZxzaSHefWN9+Zb37tXE32X7EOvXk307duHV159p6uarg6KTj6KYgDtJmbNnMmgT398dj5g4EBmzpw5f5lZMxk46OMyAwcOYlZVmWnTpvLUk0+y4UYbN7bB0kKs1H85ps54q2V52qy3WHnAslVllmXP7Tfi3Kvumm/99Fff5ow/3cI/J57I8zefzDvv/otJ9z7VFc1WHZoiOvUorN1dvcOI+Ho720ZHxJSImNLaGJ7alkgLrIvqD1Zqv8wH77/P9448gmOO/SH9+vVb5G2UOqK178VU9Rk+/eh9+PHvxjNv3vzrl1umL7tvtyHr7n4iQ3b+MUv3XYKDdt2ikc1VJ5Q1Ay1iVP1E4MLWNqSUxgJjAf41p5WIoDYNHDiIGa/MaFmeNXMmAwYMmK/MgIGDmDnj4zIzZ86gf15m9uzZfPfII9h1tz3YcfhOXdNoqR3TZr3FKoOWa1leecByTK/qht1svdW45NSvAbDCcv3Yeev1mDN3Ln169+KFaa/z2ltZt+41tzzM5zYazLiJU7qs/er+GhJAI+KRtjYBAxuxz55u/Q025KWXXmDq1JcZOGAgN068nlNP/9V8ZbbbfgfGXfpnRuy6G48+8jD9+i1D//4DSClxwnE/YsiQIfzXqDY7CKQuNeXxl1hz1f6svtLyTJ/1NvvvvBmjfnjxfGXW3ePElr/HnvBlbrjzcSbc9ihbbrA6Qzdcg75L9eHDf81m+6Fr88ATL3f1IahW5byKpWEZ6EBgZ+DNqvUB/KNB++zRevfuzQ9+dBzfGn0w8+bNZa+992XNNdfiissvA+CAA7/ENtsO4647bmf3XYaz1FJ9+enJpwDw4AP3c934a1lr7bU5YJ+RABx+5HfZZtthTPqfv/PzU07izTfeYMxhh/DZz67LOeeeX9hxqueYO3ceR512FRPOOoxeTU1cPP5ennxuBgfvuxUA5/317jafO/mxF7l60kPc85f/x5y5c3n46Wmc/ze/ehZXZb0ONKrHFBZJpRHnAxemlO5qZdulKaX/XFgdduGqu/jU0COKboLUaR8+8LuGRbn7nnu7U9/3Q4csW0gEbkgGmlL6RjvbFho8JUk9RznzTy9jkSSpLt7bSpJUrJKmoAZQSVKhyjqJyAAqSSpUkTeE7wwDqCSpUCWNn04ikiSpHmagkqRilTQFNYBKkgrlJCJJkupQ1klEjoFKklQHM1BJUqFKmoAaQCVJBStpBDWASpIK5SQiSZLq4CQiSZJ6EDNQSVKhSpqAmoFKkgoWnXzUsouIERHxdEQ8ExHHtrJ9nYi4JyL+HRFH11KnGagkqVCNnkQUEb2As4DhwFRgckSMTyk9UVHsDeAIYK9a6zUDlSQVKqJzjxoMBZ5JKT2XUvoIGAeMrCyQUpqVUpoMzK613QZQSVKpRcToiJhS8RhdVWRl4OWK5an5uk6xC1eSVKjOduCmlMYCYzu4i9TJ3RpAJUkFa/w03KnAqhXLqwDTO1upXbiSpEJFJ/+rwWRgrYgYHBFLAAcB4zvbbjNQSVK3llKaExFjgJuAXsAFKaXHI+LQfPs5ETEImAJ8EpgXEUcC66WU3mmrXgOoJKlQXXErv5TSRGBi1bpzKv6eQda1WzMDqCSpUGW9E5EBVJJUrJJGUAOoJKlQZf05M2fhSpJUBzNQSVKhyvp7oAZQSVKhSho/DaCSpIKVNIIaQCVJhXISkSRJPYgZqCSpUE4ikiSpDiWNnwZQSVLBShpBHQOVJKkOZqCSpEKVdRauAVSSVCgnEUmSVIeSxk8DqCSpWGXNQJ1EJElSHcxAJUkFK2cKagCVJBWqrF24BlBJUqFKGj8NoJKkYpU1A3USkSRJdTADlSQVyjsRSZJUj3LGTwOoJKlYJY2fjoFKklQPM1BJUqHKOgvXACpJKpSTiCRJqkc546cBVJJUrJLGTycRSZJUDzNQSVKhnEQkSVIdnEQkSVIdypqBOgYqSVIdDKCSJNXBLlxJUqHK2oVrAJUkFcpJRJIk1aGsGahjoJIk1cEMVJJUqJImoAZQSVLBShpBDaCSpEI5iUiSpDo4iUiSpB7EDFSSVKiSJqAGUElSwUoaQQ2gkqRClXUSkWOgkiTVwQxUklSoss7CjZRS0W1QQSJidEppbNHtkDrLz7KKYBduzza66AZIi4ifZXU5A6gkSXUwgEqSVAcDaM/mmJG6Cz/L6nJOIpIkqQ5moJIk1cEA2kNFxIiIeDoinomIY4tuj1SPiLggImZFxGNFt0U9jwG0B4qIXsBZwC7AesCXImK9Ylsl1eUiYETRjVDPZADtmYYCz6SUnkspfQSMA0YW3Capw1JKdwBvFN0O9UwG0J5pZeDliuWp+TpJUo0MoD1Ta3eedDq2JHWAAbRnmgqsWrG8CjC9oLZIUikZQHumycBaETE4IpYADgLGF9wmSSoVA2gPlFKaA4wBbgKeBK5IKT1ebKukjouIy4B7gM9GxNSI+EbRbVLP4Z2IJEmqgxmoJEl1MIBKklQHA6gkSXUwgEqSVAcDqCRJdTCAqvQiYm5EPBQRj0XElRHxiU7UdVFE7Jf/fV57N9mPiO0i4gt17OOFiFix1vVt1DEqIs5cFPuVVB8DqLqDD1NKm6SUNgA+Ag6t3Jj/+kyHpZQOTik90U6R7YAOB1BJ3YMBVN3NncCaeXZ4a0RcCjwaEb0i4vSImBwRj0TEIQCROTMinoiI64EBzRVFxG0RsUX+94iIeCAiHo6ISRGxBlmgPirPfreJiP4R8dd8H5MjYqv8uStExM0R8WBE/JHW70XcqogYGhH/yJ/7j4j4bMXmVSPixvx3XY+veM5XIuK+vF1/rD6BiIilI+L6/Fgei4gDO/oiS4LeRTdAWlQiojfZb5zemK8aCmyQUno+IkYDb6eUtoyIJYG7I+JmYFPgs8CGwEDgCeCCqnr7A+cC2+Z1LZ9SeiMizgHeSyn9Mi93KfCblNJdEbEa2Z2e1gWOB+5KKf00InYDRnfgsJ7K9zsnInYETgH2rTw+4ANgcn4C8D5wILBVSml2RJwNfBm4pKLOEcD0lNJuebuX7UB7JOUMoOoO+kbEQ/nfdwLnk3Wt3pdSej5fvxOwUfP4JrAssBawLXBZSmkuMD0ibmml/s8BdzTXlVJq6/cndwTWi2hJMD8ZEcvk+9gnf+71EfFmB45tWeDiiFiL7Bdz+lRs+3tK6XWAiPgbsDUwB9icLKAC9AVmVdX5KPDLiDgNuC6ldGcH2iMpZwBVd/BhSmmTyhV58Hi/chVweErppqpyu7Lwn3KLGspANiTy+ZTSh620pd57Zp4E3JpS2jvvNr6tYlt1nSlv68UppR+0VWFK6Z8RsTmwK3BqRNycUvppne2TeizHQNVT3AR8KyL6AETE2hGxNHAHcFA+RvppYPtWnnsPMCwiBufPXT5f/y6wTEW5m8lu0k9ebpP8zzvIulGJiF2AT3Wg3csC0/K/R1VtGx4Ry0dEX2Av4G5gErBfRAxobmtErF75pIhYCfggpfRn4JfAZh1oj6ScGah6ivOANYAHIksJXyULOlcDO5B1a/4TuL36iSmlV/Mx1L9FRBNZl+hwYAJwVUSMBA4HjgDOiohHyP5t3UE20ehE4LKIeCCv/6V22vlIRMzL/74C+AVZF+53geru5buAPwFrApemlKYARMSPgZvzts4Gvg28WPG8DYHT8/3MBr7VTnsktcFfY5EkqQ524UqSVAcDqCRJdTCASpJUBwOoJEl1MIBKklQHA6gkSXUwgEqSVAcDqCRJdfj/8SI/v1/o92MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_matrix=confusion_matrix(svm_prediction, y_test, normalize='all')\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(svm_matrix, annot=True, ax = ax, cmap=\"Blues\")\n",
    "print(svm_matrix)\n",
    "\n",
    "ax.set_xlabel('Predicted Labels', size=10)\n",
    "ax.set_ylabel('True Labels', size=10)\n",
    "ax.set_title('Confusion Matrix for Support Vector Machine', size=15)\n",
    "ax.xaxis.set_ticklabels([0,1], size=10)\n",
    "ax.yaxis.set_ticklabels([0,1], size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d93c6b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Russian propaganda articles classified to as fake news: 6043\n",
      "Russian propaganda articles classified to as true news: 0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.95      0.96      6837\n",
      "           1       0.95      0.96      0.96      6763\n",
      "\n",
      "    accuracy                           0.96     13600\n",
      "   macro avg       0.96      0.96      0.96     13600\n",
      "weighted avg       0.96      0.96      0.96     13600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "russian_text = tfidf_vec.transform(russian[\"text\"].values)\n",
    "russian_prediction = svm_model.predict(russian_text)\n",
    "print(f\"Russian propaganda articles classified to as fake news: {russian_prediction.tolist().count(1)}\")\n",
    "print(f\"Russian propaganda articles classified to as true news: {russian_prediction.tolist().count(0)}\")\n",
    "print(classification_report(svm_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7e685281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33313 687\n"
     ]
    }
   ],
   "source": [
    "svc_true_text = tfidf_vec.transform(true_sample[\"filtered_text\"].fillna(' ').values)\n",
    "svc_true_prediction = svm_model.predict(svc_true_text)\n",
    "svc_true_correct = svc_true_prediction.tolist().count(0)\n",
    "svc_true_false = svc_true_prediction.tolist().count(1)\n",
    "print(svc_true_correct, svc_true_false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "18aa1858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33220 780\n"
     ]
    }
   ],
   "source": [
    "svc_fake_text = tfidf_vec.transform(fake_sample[\"filtered_text\"].fillna(' ').values)\n",
    "svc_fake_prediction = svm_model.predict(svc_fake_text)\n",
    "svc_fake_false = svc_fake_prediction.tolist().count(0)\n",
    "svc_fake_correct = svc_fake_prediction.tolist().count(1)\n",
    "print(svc_fake_correct, svc_fake_false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "9ffea19f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6007 36\n"
     ]
    }
   ],
   "source": [
    "svc_russian_text = tfidf_vec.transform(russian[\"filtered_text\"].fillna(' ').values)\n",
    "svc_russian_prediction = svm_model.predict(svc_russian_text)\n",
    "svc_russian_false = svc_russian_prediction.tolist().count(0)\n",
    "svc_russian_correct = svc_russian_prediction.tolist().count(1)\n",
    "print(svc_russian_correct, svc_russian_false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f00343d2",
   "metadata": {
    "scrolled": true
   },
   "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>News_Type</th>\n",
       "      <th>True</th>\n",
       "      <th>Fake</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True News</td>\n",
       "      <td>33313</td>\n",
       "      <td>687</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fake News</td>\n",
       "      <td>780</td>\n",
       "      <td>33220</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Propaganda</td>\n",
       "      <td>36</td>\n",
       "      <td>6007</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            News_Type   True   Fake                   Model\n",
       "0           True News  33313    687  Support Vector Machine\n",
       "1           Fake News    780  33220  Support Vector Machine\n",
       "2  Russian Propaganda     36   6007  Support Vector Machine"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_df = pd.DataFrame(columns=[\"News_Type\", \"True\", \"Fake\"])\n",
    "svc_df.loc[len(svc_df)] = [\"True News\", svc_true_correct, svc_true_false]\n",
    "svc_df.loc[len(svc_df)] = [\"Fake News\", svc_fake_false, svc_fake_correct]\n",
    "svc_df.loc[len(svc_df)] = [\"Russian Propaganda\", svc_russian_false, svc_russian_correct]\n",
    "svc_df[\"Model\"] = \"Support Vector Machine\"\n",
    "svc_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5f6ddb",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "86f81e86",
   "metadata": {
    "scrolled": false
   },
   "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>News_Type</th>\n",
       "      <th>True</th>\n",
       "      <th>Fake</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True News</td>\n",
       "      <td>32667</td>\n",
       "      <td>1333</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fake News</td>\n",
       "      <td>1302</td>\n",
       "      <td>32698</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Propaganda</td>\n",
       "      <td>46</td>\n",
       "      <td>5997</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True News</td>\n",
       "      <td>33313</td>\n",
       "      <td>687</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fake News</td>\n",
       "      <td>780</td>\n",
       "      <td>33220</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Propaganda</td>\n",
       "      <td>36</td>\n",
       "      <td>6007</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            News_Type   True   Fake                   Model\n",
       "0           True News  32667   1333     Logistic Regression\n",
       "1           Fake News   1302  32698     Logistic Regression\n",
       "2  Russian Propaganda     46   5997     Logistic Regression\n",
       "0           True News  33313    687  Support Vector Machine\n",
       "1           Fake News    780  33220  Support Vector Machine\n",
       "2  Russian Propaganda     36   6007  Support Vector Machine"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_df = pd.concat([lgs_df, svc_df], axis=0)\n",
    "display(model_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e26365",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e261787",
   "metadata": {},
   "source": [
    "## Categorizing News Articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "47922765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ArticleId</th>\n",
       "      <th>Text</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1833</td>\n",
       "      <td>worldcom ex-boss launches defence lawyers defe...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>154</td>\n",
       "      <td>german business confidence slides german busin...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1101</td>\n",
       "      <td>bbc poll indicates economic gloom citizens in ...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976</td>\n",
       "      <td>lifestyle  governs mobile choice  faster  bett...</td>\n",
       "      <td>tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>917</td>\n",
       "      <td>enron bosses in $168m payout eighteen former e...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ArticleId                                               Text  Category\n",
       "0       1833  worldcom ex-boss launches defence lawyers defe...  business\n",
       "1        154  german business confidence slides german busin...  business\n",
       "2       1101  bbc poll indicates economic gloom citizens in ...  business\n",
       "3       1976  lifestyle  governs mobile choice  faster  bett...      tech\n",
       "4        917  enron bosses in $168m payout eighteen former e...  business"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_raw = pd.read_csv(\"csv/bbc_news_train.csv\")\n",
    "display(category_raw.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acace823",
   "metadata": {},
   "source": [
    "Load training dataset. This dataset is retrieved from [Kaggle](https://www.kaggle.com/competitions/learn-ai-bbc/data?select=BBC+News+Train.csv). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "9c9b971d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 duplicated rows present in BBC News Category news dataset.\n",
      "0 NaN row removed.\n",
      "Total number of rows remaining are: 1440 rows.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ArticleId</th>\n",
       "      <th>text</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>1101</td>\n",
       "      <td>bbc poll indicates economic gloom citizens in ...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976</td>\n",
       "      <td>lifestyle  governs mobile choice  faster  bett...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>enron bosses in $168m payout eighteen former e...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ArticleId                                               text  Category\n",
       "0       1833  worldcom ex-boss launches defence lawyers defe...  business\n",
       "1        154  german business confidence slides german busin...  business\n",
       "2       1101  bbc poll indicates economic gloom citizens in ...  business\n",
       "3       1976  lifestyle  governs mobile choice  faster  bett...      tech\n",
       "4        917  enron bosses in $168m payout eighteen former e...  business"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>ArticleId</th>\n",
       "      <th>text</th>\n",
       "      <th>Category</th>\n",
       "      <th>filtered_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1833</td>\n",
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       "      <td>worldcom ex-boss launch defence lawyer defend ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>154</td>\n",
       "      <td>german business confidence slides german busin...</td>\n",
       "      <td>business</td>\n",
       "      <td>german business confidence slides german busin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1101</td>\n",
       "      <td>bbc poll indicates economic gloom citizens in ...</td>\n",
       "      <td>business</td>\n",
       "      <td>bbc poll economic gloom citizen majority natio...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976</td>\n",
       "      <td>lifestyle  governs mobile choice  faster  bett...</td>\n",
       "      <td>tech</td>\n",
       "      <td>lifestyle governs mobile choice faster funky h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>917</td>\n",
       "      <td>enron bosses in $168m payout eighteen former e...</td>\n",
       "      <td>business</td>\n",
       "      <td>enron boss $ 168m payout eighteen enron direct...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ArticleId                                               text  Category  \\\n",
       "0       1833  worldcom ex-boss launches defence lawyers defe...  business   \n",
       "1        154  german business confidence slides german busin...  business   \n",
       "2       1101  bbc poll indicates economic gloom citizens in ...  business   \n",
       "3       1976  lifestyle  governs mobile choice  faster  bett...      tech   \n",
       "4        917  enron bosses in $168m payout eighteen former e...  business   \n",
       "\n",
       "                                       filtered_text  \n",
       "0  worldcom ex-boss launch defence lawyer defend ...  \n",
       "1  german business confidence slides german busin...  \n",
       "2  bbc poll economic gloom citizen majority natio...  \n",
       "3  lifestyle governs mobile choice faster funky h...  \n",
       "4  enron boss $ 168m payout eighteen enron direct...  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_raw = category_raw.rename(columns={\"Text\":\"text\"})\n",
    "category_raw = cleanup_df(category_raw, \"BBC News Category\")\n",
    "category_raw[\"filtered_text\"] = category_raw[\"text\"].apply(lambda x: remove_stopwords_and_lemmatize(x))\n",
    "category_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "f05a2890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1152,) (288,)\n",
      "(1152,) (288,)\n"
     ]
    }
   ],
   "source": [
    "X_category = category_raw[\"filtered_text\"].fillna(' ')\n",
    "y_category = category_raw[\"Category\"].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_category, y_category, \n",
    "                                                    stratify=y_category, test_size=0.2, random_state=11)\n",
    "tfidf_vec = TfidfVectorizer()\n",
    "tfidf_train = tfidf_vec.fit_transform(X_train)\n",
    "tfidf_test = tfidf_vec.transform(X_test)\n",
    "print(X_train.shape, X_test.shape)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813c9656",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "5cc4c943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy testing on pmulti-class linear SVC model: 0.9861111111111112\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     business       0.99      1.00      0.99        66\n",
      "entertainment       1.00      0.96      0.98        55\n",
      "     politics       0.96      1.00      0.98        51\n",
      "        sport       1.00      0.99      0.99        69\n",
      "         tech       0.98      0.98      0.98        47\n",
      "\n",
      "     accuracy                           0.99       288\n",
      "    macro avg       0.99      0.99      0.99       288\n",
      " weighted avg       0.99      0.99      0.99       288\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_model_for_category=svm.SVC(kernel='linear')\n",
    "svm_model_for_category.fit(tfidf_train, y_train)\n",
    "svm_prediction_for_category = svm_model_for_category.predict(tfidf_test)\n",
    "print('Accuracy testing on pmulti-class linear SVC model:',accuracy_score(y_test, svm_prediction_for_category))\n",
    "print(classification_report(svm_prediction_for_category, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "0dec2913",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_category_text = tfidf_vec.transform(fake_sample[\"filtered_text\"].fillna(' ').values)\n",
    "svc_category_prediction = svm_model_for_category.predict(svc_category_text)\n",
    "svc_business = svc_category_prediction.tolist().count(\"business\")\n",
    "svc_entertainment = svc_category_prediction.tolist().count(\"entertainment\")\n",
    "svc_poltiics = svc_category_prediction.tolist().count(\"politics\")\n",
    "svc_sport = svc_category_prediction.tolist().count(\"sport\")\n",
    "svc_tech = svc_category_prediction.tolist().count(\"tech\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "25a02873",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_category_text_true = tfidf_vec.transform(true_sample[\"filtered_text\"].fillna(' ').values)\n",
    "svc_category_prediction_true = svm_model_for_category.predict(svc_category_text_true)\n",
    "svc_business_true = svc_category_prediction_true.tolist().count(\"business\")\n",
    "svc_entertainment_true = svc_category_prediction_true.tolist().count(\"entertainment\")\n",
    "svc_poltiics_true = svc_category_prediction_true.tolist().count(\"politics\")\n",
    "svc_sport_true = svc_category_prediction_true.tolist().count(\"sport\")\n",
    "svc_tech_true = svc_category_prediction_true.tolist().count(\"tech\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9169d7",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "2b910b92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy testing on pmulti-class Logistic Regression Model: 0.9756944444444444\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     business       0.97      0.98      0.98        66\n",
      "entertainment       0.98      0.96      0.97        54\n",
      "     politics       0.96      0.98      0.97        52\n",
      "        sport       1.00      0.97      0.99        70\n",
      "         tech       0.96      0.98      0.97        46\n",
      "\n",
      "     accuracy                           0.98       288\n",
      "    macro avg       0.97      0.98      0.97       288\n",
      " weighted avg       0.98      0.98      0.98       288\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lgs_model_for_category=LogisticRegression()\n",
    "lgs_model_for_category.fit(tfidf_train, y_train)\n",
    "lgs_prediction_for_category = lgs_model_for_category.predict(tfidf_test)\n",
    "print('Accuracy testing on pmulti-class Logistic Regression Model:',accuracy_score(y_test, lgs_prediction_for_category))\n",
    "print(classification_report(lgs_prediction_for_category, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "f5ee0737",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgs_category_text = tfidf_vec.transform(fake_sample[\"filtered_text\"].fillna(' ').values)\n",
    "lgs_category_prediction = lgs_model_for_category.predict(lgs_category_text)\n",
    "lgs_business = lgs_category_prediction.tolist().count(\"business\")\n",
    "lgs_entertainment = lgs_category_prediction.tolist().count(\"entertainment\")\n",
    "lgs_poltiics = lgs_category_prediction.tolist().count(\"politics\")\n",
    "lgs_sport = lgs_category_prediction.tolist().count(\"sport\")\n",
    "lgs_tech = lgs_category_prediction.tolist().count(\"tech\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "9d784a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgs_category_text_true = tfidf_vec.transform(true_sample[\"filtered_text\"].fillna(' ').values)\n",
    "lgs_category_prediction_true = lgs_model_for_category.predict(lgs_category_text_true)\n",
    "lgs_business_true = lgs_category_prediction_true.tolist().count(\"business\")\n",
    "lgs_entertainment_true = lgs_category_prediction_true.tolist().count(\"entertainment\")\n",
    "lgs_poltiics_true = lgs_category_prediction_true.tolist().count(\"politics\")\n",
    "lgs_sport_true = lgs_category_prediction_true.tolist().count(\"sport\")\n",
    "lgs_tech_true = lgs_category_prediction_true.tolist().count(\"tech\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6302efe",
   "metadata": {},
   "source": [
    "### Natural Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "bbd601ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy testing on pmulti-class Natural Bayes Model: 0.9722222222222222\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     business       0.99      0.96      0.97        69\n",
      "entertainment       0.96      1.00      0.98        51\n",
      "     politics       0.94      0.98      0.96        51\n",
      "        sport       1.00      0.94      0.97        72\n",
      "         tech       0.96      1.00      0.98        45\n",
      "\n",
      "     accuracy                           0.97       288\n",
      "    macro avg       0.97      0.98      0.97       288\n",
      " weighted avg       0.97      0.97      0.97       288\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bayes_model_for_category = MultinomialNB()\n",
    "bayes_model_for_category.fit(tfidf_train, y_train)\n",
    "bayes_prediction_for_category = bayes_model_for_category.predict(tfidf_test)\n",
    "print('Accuracy testing on pmulti-class Natural Bayes Model:',accuracy_score(y_test, bayes_prediction_for_category))\n",
    "print(classification_report(bayes_prediction_for_category, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "6e481363",
   "metadata": {},
   "outputs": [],
   "source": [
    "bayes_category_text = tfidf_vec.transform(fake_sample[\"filtered_text\"].fillna(' ').values)\n",
    "bayes_category_prediction = bayes_model_for_category.predict(bayes_category_text)\n",
    "bayes_business = bayes_category_prediction.tolist().count(\"business\")\n",
    "bayes_entertainment = bayes_category_prediction.tolist().count(\"entertainment\")\n",
    "bayes_poltiics = bayes_category_prediction.tolist().count(\"politics\")\n",
    "bayes_sport = bayes_category_prediction.tolist().count(\"sport\")\n",
    "bayes_tech = bayes_category_prediction.tolist().count(\"tech\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "dd062e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "bayes_category_text_true = tfidf_vec.transform(true_sample[\"filtered_text\"].fillna(' ').values)\n",
    "bayes_category_prediction_true = bayes_model_for_category.predict(bayes_category_text_true)\n",
    "bayes_business_true = bayes_category_prediction_true.tolist().count(\"business\")\n",
    "bayes_entertainment_true = bayes_category_prediction_true.tolist().count(\"entertainment\")\n",
    "bayes_poltiics_true = bayes_category_prediction_true.tolist().count(\"politics\")\n",
    "bayes_sport_true = bayes_category_prediction_true.tolist().count(\"sport\")\n",
    "bayes_tech_true = bayes_category_prediction_true.tolist().count(\"tech\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e6fab0",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "c8335fff",
   "metadata": {
    "scrolled": false
   },
   "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>Model</th>\n",
       "      <th>Business</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Politics</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Tech</th>\n",
       "      <th>News Type</th>\n",
       "      <th>Model Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>13507</td>\n",
       "      <td>6293</td>\n",
       "      <td>10500</td>\n",
       "      <td>1505</td>\n",
       "      <td>2195</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0.98611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>14506</td>\n",
       "      <td>2770</td>\n",
       "      <td>15015</td>\n",
       "      <td>813</td>\n",
       "      <td>896</td>\n",
       "      <td>True</td>\n",
       "      <td>0.98611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>13638</td>\n",
       "      <td>4004</td>\n",
       "      <td>9564</td>\n",
       "      <td>5218</td>\n",
       "      <td>1576</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0.97569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>15730</td>\n",
       "      <td>2219</td>\n",
       "      <td>13430</td>\n",
       "      <td>1887</td>\n",
       "      <td>734</td>\n",
       "      <td>True</td>\n",
       "      <td>0.97569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natural Bayes</td>\n",
       "      <td>11210</td>\n",
       "      <td>1641</td>\n",
       "      <td>16412</td>\n",
       "      <td>3647</td>\n",
       "      <td>1090</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0.97222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Natural Bayes</td>\n",
       "      <td>13685</td>\n",
       "      <td>1147</td>\n",
       "      <td>17414</td>\n",
       "      <td>1319</td>\n",
       "      <td>435</td>\n",
       "      <td>True</td>\n",
       "      <td>0.97222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Business  Entertainment  Politics  Sport  Tech  \\\n",
       "0                  SVC     13507           6293     10500   1505  2195   \n",
       "1                  SVC     14506           2770     15015    813   896   \n",
       "2  Logistic Regression     13638           4004      9564   5218  1576   \n",
       "3  Logistic Regression     15730           2219     13430   1887   734   \n",
       "4        Natural Bayes     11210           1641     16412   3647  1090   \n",
       "5        Natural Bayes     13685           1147     17414   1319   435   \n",
       "\n",
       "  News Type  Model Accuracy  \n",
       "0      Fake         0.98611  \n",
       "1      True         0.98611  \n",
       "2      Fake         0.97569  \n",
       "3      True         0.97569  \n",
       "4      Fake         0.97222  \n",
       "5      True         0.97222  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_df = pd.DataFrame(columns=[\"Model\", \"Business\", \"Entertainment\", \"Politics\", \"Sport\", \"Tech\", \"News Type\", \"Model Accuracy\"])\n",
    "category_df.loc[len(category_df)] = [\"SVC\", svc_business, svc_entertainment, svc_poltiics, svc_sport, svc_tech, \"Fake\", 0.98611]\n",
    "category_df.loc[len(category_df)] = [\"SVC\", svc_business_true, svc_entertainment_true, svc_poltiics_true, \n",
    "                                     svc_sport_true, svc_tech_true, \"True\", 0.98611]\n",
    "category_df.loc[len(category_df)] = [\"Logistic Regression\", lgs_business, lgs_entertainment, lgs_poltiics, lgs_sport, lgs_tech, \"Fake\", 0.97569]\n",
    "category_df.loc[len(category_df)] = [\"Logistic Regression\", lgs_business_true, lgs_entertainment_true, lgs_poltiics_true, \n",
    "                                     lgs_sport_true, lgs_tech_true, \"True\", 0.97569]\n",
    "category_df.loc[len(category_df)] = [\"Natural Bayes\", bayes_business, bayes_entertainment, bayes_poltiics, bayes_sport, bayes_tech, \"Fake\", 0.97222]\n",
    "category_df.loc[len(category_df)] = [\"Natural Bayes\", bayes_business_true, bayes_entertainment_true, bayes_poltiics_true, \n",
    "                                     bayes_sport_true, bayes_tech_true, \"True\", 0.97222]\n",
    "category_df"
   ]
  }
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