fake-news-detector.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAKE NEWS DETECTOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORT LIBRARIES\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,f1_score,precision_score,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3608</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6903</td>\n",
       "      <td>Tehran, USA</td>\n",
       "      <td>\\nI’m not an immigrant, but my grandparents ...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7341</td>\n",
       "      <td>Girl Horrified At What She Watches Boyfriend D...</td>\n",
       "      <td>Share This Baylee Luciani (left), Screenshot o...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>95</td>\n",
       "      <td>‘Britain’s Schindler’ Dies at 106</td>\n",
       "      <td>A Czech stockbroker who saved more than 650 Je...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4869</td>\n",
       "      <td>Fact check: Trump and Clinton at the 'commande...</td>\n",
       "      <td>Hillary Clinton and Donald Trump made some ina...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2909</td>\n",
       "      <td>Iran reportedly makes new push for uranium con...</td>\n",
       "      <td>Iranian negotiators reportedly have made a las...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "5        6903                                        Tehran, USA   \n",
       "6        7341  Girl Horrified At What She Watches Boyfriend D...   \n",
       "7          95                  ‘Britain’s Schindler’ Dies at 106   \n",
       "8        4869  Fact check: Trump and Clinton at the 'commande...   \n",
       "9        2909  Iran reportedly makes new push for uranium con...   \n",
       "\n",
       "                                                text label  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4  It's primary day in New York and front-runners...  REAL  \n",
       "5    \\nI’m not an immigrant, but my grandparents ...  FAKE  \n",
       "6  Share This Baylee Luciani (left), Screenshot o...  FAKE  \n",
       "7  A Czech stockbroker who saved more than 650 Je...  REAL  \n",
       "8  Hillary Clinton and Donald Trump made some ina...  REAL  \n",
       "9  Iranian negotiators reportedly have made a las...  REAL  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# READ DATASET\n",
    "df=pd.read_csv(r'C:\\Users\\aleja\\datascience\\Data Science Projects\\detecting-fake-news\\news.csv')\n",
    "\n",
    "# GET DATASET SHAPE AND FIRST TEN LINES\n",
    "df.shape\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    FAKE\n",
       "1    FAKE\n",
       "2    REAL\n",
       "3    FAKE\n",
       "4    REAL\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=df.label\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REAL    3171\n",
       "FAKE    3164\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPLIT DATASET INTO TRAINING AND TESTING SETS\n",
    "x_train,x_test,y_train,y_test=train_test_split(df['text'],labels,test_size=0.3,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INITIALIZING TfidfVectorizer\n",
    "# Filter the words that have a frequency higher than 65%\n",
    "tfidf_vectorizer=TfidfVectorizer(stop_words='english',max_df=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIT AND TRANSFORM TRAIN AND TEST SET\n",
    "# apply the vectorizer defined to the test and train set to fit the model selected\n",
    "tfidf_train=tfidf_vectorizer.fit_transform(x_train)\n",
    "tfidf_test=tfidf_vectorizer.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassiveAggressiveClassifier(max_iter=50)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# INITIALIZE PaPassiveAggressiveClassifier\n",
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.79%\n"
     ]
    }
   ],
   "source": [
    "# PREDICT ON THE TEST SET AND CALCULATE ACCURACY SCORE\n",
    "y_pred=pac.predict(tfidf_test)\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "# rec=recall_score(y_test,y_pred,pos_labels=['FAKE','REAL'])\n",
    "# prec=precision_score(y_test,y_pred)\n",
    "#f1=f1_score(y_test,y_pred)\n",
    "print(f'Accuracy: {round(acc*100,2)}%')\n",
    "# print(f'Recall: {round(rec*100,2)}%')\n",
    "# print(f'Precision: {round(prec*100,2)}%')\n",
    "#print(f'F1 score: {round(f1*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[908  66]\n",
      " [ 71 856]]\n"
     ]
    }
   ],
   "source": [
    "# BUILDING A CONFUSION MATRIX\n",
    "print(confusion_matrix(y_test,y_pred,labels=['FAKE','REAL']))"
   ]
  }
 ],
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