Word2VecNewsClassification.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(21417, 4)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    WASHINGTON (Reuters) - The head of a conservat...\n",
       "1    WASHINGTON (Reuters) - Transgender people will...\n",
       "2    WASHINGTON (Reuters) - The special counsel inv...\n",
       "3    WASHINGTON (Reuters) - Trump campaign adviser ...\n",
       "4    SEATTLE/WASHINGTON (Reuters) - President Donal...\n",
       "Name: text, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#read the dataset with name \"Fake_Real_Data.csv\" and store it in a variable df\n",
    "df = pd.read_csv(\"True.csv\")\n",
    "\n",
    "#print the shape of dataframe\n",
    "print(df.shape)\n",
    "\n",
    "#print top 5 rows\n",
    "df[\"text\"].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_true_news = pd.read_csv(\"True.csv\")\n",
    "df_fake_news = pd.read_csv(\"Fake.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fake_news['label_num'] = 0\n",
    "df_true_news['label_num'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate fake and true news\n",
    "df = pd.concat([df_fake_news, df_true_news])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                               title  \\\n",
       "0   Donald Trump Sends Out Embarrassing New Year’...   \n",
       "1   Drunk Bragging Trump Staffer Started Russian ...   \n",
       "2   Sheriff David Clarke Becomes An Internet Joke...   \n",
       "3   Trump Is So Obsessed He Even Has Obama’s Name...   \n",
       "4   Pope Francis Just Called Out Donald Trump Dur...   \n",
       "\n",
       "                                                text subject  \\\n",
       "0  Donald Trump just couldn t wish all Americans ...    News   \n",
       "1  House Intelligence Committee Chairman Devin Nu...    News   \n",
       "2  On Friday, it was revealed that former Milwauk...    News   \n",
       "3  On Christmas day, Donald Trump announced that ...    News   \n",
       "4  Pope Francis used his annual Christmas Day mes...    News   \n",
       "\n",
       "                date  label_num  \n",
       "0  December 31, 2017          0  \n",
       "1  December 31, 2017          0  \n",
       "2  December 30, 2017          0  \n",
       "3  December 29, 2017          0  \n",
       "4  December 25, 2017          0  "
      ],
      "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>title</th>\n      <th>text</th>\n      <th>subject</th>\n      <th>date</th>\n      <th>label_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n      <td>Donald Trump just couldn t wish all Americans ...</td>\n      <td>News</td>\n      <td>December 31, 2017</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Drunk Bragging Trump Staffer Started Russian ...</td>\n      <td>House Intelligence Committee Chairman Devin Nu...</td>\n      <td>News</td>\n      <td>December 31, 2017</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n      <td>On Friday, it was revealed that former Milwauk...</td>\n      <td>News</td>\n      <td>December 30, 2017</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n      <td>On Christmas day, Donald Trump announced that ...</td>\n      <td>News</td>\n      <td>December 29, 2017</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pope Francis Just Called Out Donald Trump Dur...</td>\n      <td>Pope Francis used his annual Christmas Day mes...</td>\n      <td>News</td>\n      <td>December 25, 2017</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing columns which are not required\n",
    "df = df.drop([\"title\", \"subject\",\"date\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                text  label_num\n",
       "0  Donald Trump just couldn t wish all Americans ...          0\n",
       "1  House Intelligence Committee Chairman Devin Nu...          0\n",
       "2  On Friday, it was revealed that former Milwauk...          0\n",
       "3  On Christmas day, Donald Trump announced that ...          0\n",
       "4  Pope Francis used his annual Christmas Day mes...          0"
      ],
      "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>text</th>\n      <th>label_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Donald Trump just couldn t wish all Americans ...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>House Intelligence Committee Chairman Devin Nu...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>On Friday, it was revealed that former Milwauk...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>On Christmas day, Donald Trump announced that ...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pope Francis used his annual Christmas Day mes...</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_lg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This will take some time(nearly 15 minutes)\n",
    "df['vector'] = df['text'].apply(lambda text: nlp(text).vector)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                text  label_num  \\\n",
       "0  Donald Trump just couldn t wish all Americans ...          0   \n",
       "1  House Intelligence Committee Chairman Devin Nu...          0   \n",
       "2  On Friday, it was revealed that former Milwauk...          0   \n",
       "3  On Christmas day, Donald Trump announced that ...          0   \n",
       "4  Pope Francis used his annual Christmas Day mes...          0   \n",
       "\n",
       "                                              vector  \n",
       "0  [-1.6387042, -0.027837504, -1.6619873, -0.2017...  \n",
       "1  [-1.9558355, 0.5587237, -2.036446, 0.40390933,...  \n",
       "2  [-1.9058484, -0.04223674, -1.7389339, -0.02855...  \n",
       "3  [-1.6265213, 0.14619705, -0.5877981, 0.0992525...  \n",
       "4  [-2.1327376, 1.1017615, -2.4795847, -0.0065187...  "
      ],
      "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>text</th>\n      <th>label_num</th>\n      <th>vector</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Donald Trump just couldn t wish all Americans ...</td>\n      <td>0</td>\n      <td>[-1.6387042, -0.027837504, -1.6619873, -0.2017...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>House Intelligence Committee Chairman Devin Nu...</td>\n      <td>0</td>\n      <td>[-1.9558355, 0.5587237, -2.036446, 0.40390933,...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>On Friday, it was revealed that former Milwauk...</td>\n      <td>0</td>\n      <td>[-1.9058484, -0.04223674, -1.7389339, -0.02855...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>On Christmas day, Donald Trump announced that ...</td>\n      <td>0</td>\n      <td>[-1.6265213, 0.14619705, -0.5877981, 0.0992525...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pope Francis used his annual Christmas Day mes...</td>\n      <td>0</td>\n      <td>[-2.1327376, 1.1017615, -2.4795847, -0.0065187...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df.vector.values,\n",
    "    df.label_num,\n",
    "    test_size=0.2,\n",
    "    random_state=2022\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X_train_2d = np.stack(X_train)\n",
    "X_test_2d = np.stack(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ],
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     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaled_train_embed = scaler.fit_transform(X_train_2d)\n",
    "scaled_test_embed = scaler.transform(X_test_2d)\n",
    "\n",
    "\n",
    "clf = MultinomialNB()\n",
    "clf.fit(scaled_train_embed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.89      0.85      0.87      4628\n           1       0.85      0.89      0.87      4352\n\n    accuracy                           0.87      8980\n   macro avg       0.87      0.87      0.87      8980\nweighted avg       0.87      0.87      0.87      8980\n\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "y_pred = clf.predict(scaled_test_embed)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.97      0.97      0.97      4628\n           1       0.97      0.97      0.97      4352\n\n    accuracy                           0.97      8980\n   macro avg       0.97      0.97      0.97      8980\nweighted avg       0.97      0.97      0.97      8980\n\n"
     ]
    }
   ],
   "source": [
    "from  sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#1. creating a KNN model object\n",
    "clf = KNeighborsClassifier(n_neighbors = 5, metric = 'euclidean')\n",
    "\n",
    "#2. fit with all_train_embeddings and y_train\n",
    "clf.fit(X_train_2d, y_train)\n",
    "\n",
    "#3. get the predictions for all_test_embeddings and store it in y_pred\n",
    "y_pred = clf.predict(X_test_2d)\n",
    "\n",
    "#4. print the classfication report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}