FakeNewsClassifierUsingLSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>Darrell Lucus</td>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>FLYNN: Hillary Clinton, Big Woman on Campus - ...</td>\n",
       "      <td>Daniel J. Flynn</td>\n",
       "      <td>Ever get the feeling your life circles the rou...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Why the Truth Might Get You Fired</td>\n",
       "      <td>Consortiumnews.com</td>\n",
       "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15 Civilians Killed In Single US Airstrike Hav...</td>\n",
       "      <td>Jessica Purkiss</td>\n",
       "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
       "      <td>Howard Portnoy</td>\n",
       "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                              title              author  \\\n",
       "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...       Darrell Lucus   \n",
       "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...     Daniel J. Flynn   \n",
       "2   2                  Why the Truth Might Get You Fired  Consortiumnews.com   \n",
       "3   3  15 Civilians Killed In Single US Airstrike Hav...     Jessica Purkiss   \n",
       "4   4  Iranian woman jailed for fictional unpublished...      Howard Portnoy   \n",
       "\n",
       "                                                text  label  \n",
       "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
       "1  Ever get the feeling your life circles the rou...      0  \n",
       "2  Why the Truth Might Get You Fired October 29, ...      1  \n",
       "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Drop Nan Values\n",
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('label', axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18285, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18285,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.1'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Embedding\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.preprocessing.text import one_hot\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Vocabulary size\n",
    "voc_size=5000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Onehot Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages=X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FLYNN: Hillary Clinton, Big Woman on Campus - Breitbart'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages['title'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import re\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\d\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "corpus = []\n",
    "for i in range(0, len(messages)):\n",
    "    print(i)\n",
    "    review = re.sub('[^a-zA-Z]', '  ' , messages['title'][i])  \n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    \n",
    "    review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
    "    review = ''.join(review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'jackimasonhollywoodwouldlovetrumpbombnorthkorealacktranbathroomexclusvideobreitbart',\n",
       " 'benohamonwinfrenchsocialistpartipresidentinominnewyorktime',\n",
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       " 'russianresearchdiscovsecretnazimilitaribasetreasurhunterarcticphoto',\n",
       " 'usofficiseelinktrumprussia',\n",
       " 'yepaidgoverntrollsocialmediablogforumwebsit',\n",
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       " 'mindeatwayfightbingnewyorktime',\n",
       " 'majorpotentiimpactcorportaxoverhaulnewyorktime',\n",
       " 'wonderglplikedayelect',\n",
       " 'makerworldsmallestmachinawardnobelprizechemistrinewyorktime',\n",
       " 'massivantitrumpprotestunionsquarnyclivestream',\n",
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       " 'ugenerislamstatechemicattackimpactuforc',\n",
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       " 'clintoncampaignstunfbireportedlireopenprobehillariclintonemail',\n",
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       " 'bernisandersaymediatrumpgutlesspolitcoward',\n",
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       " 'dresslikewomanmeannewyorktime',\n",
       " 'ellabrennanstillfeedleadneworleannewyorktime',\n",
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       " 'trumpimmigrpoliciexplainnewyorktime',\n",
       " 'maritylermoormourndickvandykestarnewyorktime',\n",
       " 'poison',\n",
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       " 'foxbizreporthelpbashclintonrallicovertrumppackeventday',\n",
       " 'fictionpodcastworthlistennewyorktime',\n",
       " 'mikebirbigliatipmakesmallhollywoodanywhernewyorktime',\n",
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       " 'fbifindpreviousunseenhillariclintonemailweinerlaptop',\n",
       " 'yearamericanjournalistkillconspiracitheorisyriaprovenfact',\n",
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       " 'canceragencfirewithholdcarcinogenglyphosdocument',\n",
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       " 'nemesiscourgwesternworld',\n",
       " 'trumpbudgetnewfoundatamericangreatbreitbart',\n",
       " 'exclusislamstatesupportvowterrorgroupretakmosulliber',\n",
       " 'rollthundermotorcyclistreturnchonorpowmiabreitbart',\n",
       " 'interestfunfactstethoscop',\n",
       " 'chinaseekbiggerroleworldstagexijinpgodavoworldeconomforumnewyorktime',\n",
       " 'veteranpreparjoinstandrockproteststopdakotaaccesspipelin',\n",
       " 'photolatinamericacondemnvenezuelaexcessuseforcprotestbreitbart',\n",
       " 'sugarfeedcancercellmayevencreat',\n",
       " 'johnkerriurggroundmilitariaircraftkeyareasyrianewyorktime',\n",
       " 'outsidmoneyfavorhillariclintonratedonaldtrumpnewyorktime',\n",
       " 'fakenewsnewyorktimetargetbreitbartreporttruthbreitbart',\n",
       " 'blastnewyorkerexaminpsychologshrapnelnewyorktime',\n",
       " 'resistschwarzeneggcallgrassrootrevolutuexitpariagreement',\n",
       " 'virginiaofficirequestuinquiriinmatdeathjailnewyorktime',\n",
       " 'reportvoterfraudcrashcours',\n",
       " 'associpressreportadmitfakenewsstorihillariclinton',\n",
       " 'reviewgarthbrookbringrousanthemballadyankestadiumnewyorktime',\n",
       " 'sichertrainierenbeimspingiltabsoforthelmpflicht',\n",
       " 'everiassetclasscollapsneedlookwealthtermmanichickenmuchgabuy',\n",
       " 'reviewhaltcatchfiretimetravelsiliconvalleydawnnewyorktime',\n",
       " 'obamausereligitestfavormuslimchristian',\n",
       " 'wingsuitflyervstree',\n",
       " 'turkeysayairportbomberkyrgyzstanrussiauzbekistannewyorktime',\n",
       " 'turmerpowerartificidrug',\n",
       " 'facebookcaughtselltargetadvertisexcluddifferrace',\n",
       " 'gatlinburgresidreturnhomewildfirdestructnewyorktime',\n",
       " 'yearoldgirlusehumanbombnigeriaattack',\n",
       " 'shockberkeleypollcaliforniavoterwantdemocratworktrumpbreitbart',\n",
       " 'familiterrorattackvictimsuetwitterprovidresourcservicisibreitbart',\n",
       " 'philippinleadervowpardonpolicaccusmayordeathnewyorktime',\n",
       " 'fdafoundmanipulmediafavorbigpharma',\n",
       " 'jayhlehrbeginendepa',\n",
       " 'imahdiarrivsatanpracticleader',\n",
       " 'agsessiondemsenharriablrushfastmakenervoubreitbart',\n",
       " 'unenvoysayustillbackpalestinianstatenewyorktime',\n",
       " 'trumphappen',\n",
       " 'japanvotestrengthenshinzoabegoalchangconstitutnewyorktime',\n",
       " 'trumpcampcaughtcambragvotersuppresswomenblackvideo',\n",
       " 'chelseamanaskobamacutsentenctimeservnewyorktime',\n",
       " 'redsoxbroadcastjerriremithinkforeignplayerusetranslat',\n",
       " 'shiitemilitiajoiniraqmosulattack',\n",
       " 'escapreignsuperbowlcommercipolitproveinescapnewyorktime',\n",
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       " 'jarnewlevelconfrontconflicthitwashingtonnewyorktime',\n",
       " 'clintontransmitclassifiinfolawyer',\n",
       " 'hillarirussianhackguidamericanmighttrumpbreitbart',\n",
       " 'generelectcampaignsuspendwakemanchestsuicidbomb',\n",
       " 'grandslamfathersonfilmsmashhitfamili',\n",
       " 'venezuelaeconomcrisimeanleftfail',\n",
       " 'herdstampedwildboarkillthreeislamstatejihadistbreitbart',\n",
       " 'venumarbelievgenderoftenwrongnewyorktime',\n",
       " 'obamapardonlisthotelmagnatownstudionewyorktime',\n",
       " 'suspectcapturambushstylekilltwoiowacop',\n",
       " 'megynsickelligowditriumphantlicommenthillaricasereopen',\n",
       " 'familifriendlipolicifriendliestmaleprofessornewyorktime',\n",
       " 'ringostarranunciaquedejalobeatl',\n",
       " 'warlessimminclintondefeat',\n",
       " 'michiefpresentrussiagrowthreatbritishinterest',\n",
       " 'washingtonstateupendtrumptravelbannewyorktime',\n",
       " 'protectswervpolicchiefcaughtspeedgetlaughticketvideo',\n",
       " 'fakenewstrumphitmexicoexplod',\n",
       " 'gowikileak',\n",
       " 'formerciadirectorblamemillenniwikileakdocumentbreitbart',\n",
       " 'tellstorislaverinewyorktime',\n",
       " 'livewiredutchelecthighturnoutexpectcontroversiturkishmosqupollcentr',\n",
       " 'shavemeanfreedomomar',\n",
       " 'bombshelleakemailexposmuslimgotobamaadministr',\n",
       " 'nativamericanparttenlosttribejewishpeopl',\n",
       " 'blmrapperbillallegsonnastisurprishillari',\n",
       " 'kevindurantjoingoldenstatewarriornewyorktime',\n",
       " 'indiancallcenterbecommajorcenterdefraudamericanbreitbart',\n",
       " 'congressattorneygenerlynchpleadfifthsecretiranransompayment',\n",
       " 'loangeltimeeditorilosetrumpnarcissistdemagogubreitbart',\n",
       " 'swirluntruthfalsehoodcalllielienewyorktime',\n",
       " 'hillariclintonleaddonaldtrumpnewhampshirfloridapollshownewyorktime',\n",
       " 'genewilderhumaabedindonaldtrumpmondayevenbriefnewyorktime',\n",
       " 'watchblimpcrashcatchfireusopenbreitbart',\n",
       " 'toddlerloosguncarmotherdienewyorktime',\n",
       " 'asianamericanactorfightvisiblignornewyorktime',\n",
       " 'mexicanfeelenviron',\n",
       " 'insiddonaldtrumplaststandanxiounomineseekassurnewyorktime',\n",
       " 'parkservicnamediversnationlandmarknewyorktime',\n",
       " 'weinerrisewhitehousallidisgracexcongressmanhirebreitbart',\n",
       " 'trumplosegrabmusketformercongressmanreadigofullrevolut',\n",
       " 'decodnorthkoreaclaimsuccessnucleartestnewyorktime',\n",
       " 'billbelichickwitlisttestifiaaronhernandezdoublmurdercasebreitbart',\n",
       " 'lmayorsilentcitiriseviolentcrimebreitbart',\n",
       " 'theresamaynewbritishprimeministgiveborijohnsonkeypostnewyorktime',\n",
       " 'donaldtrumphillariclintoniphonwednesdayevenbriefnewyorktime',\n",
       " 'usakillmillionpeoplvictimnationsincworldwarii',\n",
       " 'policofficfacebacklashhillariphoto',\n",
       " 'shakespearremainauthentbore',\n",
       " 'georgiacandidjonossofffilmfirmfinancfacebookfactcheckfunder',\n",
       " 'effortdefeatisiuiranimpedoneanothnewyorktime',\n",
       " 'rickputbullettrumpwilsonpotusupportseescalisshootblessbreitbart',\n",
       " 'westernintelligagencrunalqaedacampnorthafrica',\n",
       " 'trumptellplanparenthoodfundstayabortgoenewyorktime',\n",
       " 'commenthillariclintonguncontrolagendaexposwikileakemailbraindiseasecalledliber',\n",
       " 'barackobamadelaysuspendelecthillariforcnewfbiemailinvestig',\n",
       " 'donaldtrumpincitfeudgpcandidfleeshadownewyorktime',\n",
       " 'germanistudentforcchantallahuakbarpunishrefustripmosqu',\n",
       " 'meteorspacejunkrocketmysteriflashhitsiberia',\n",
       " 'trumpnationsecuradviscallrussianenvoydaysanctionimposnewyorktime',\n",
       " 'projectveritaimplicdemocratoperclaimcreditromneyvideo',\n",
       " 'laidamericanrequirziplipwaygrowboldernewyorktime',\n",
       " ...]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ...]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_repr = [one_hot(word,voc_size)for word in corpus]\n",
    "onehot_repr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Embedding Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0    0 ...    0    0  827]\n",
      " [   0    0    0 ...    0    0  856]\n",
      " [   0    0    0 ...    0    0 2213]\n",
      " ...\n",
      " [   0    0    0 ...    0    0 1704]\n",
      " [   0    0    0 ...    0    0 1612]\n",
      " [   0    0    0 ...    0    0 4865]]\n"
     ]
    }
   ],
   "source": [
    "sent_length=20\n",
    "embedded_docs=pad_sequences(onehot_repr,padding='pre',maxlen=sent_length)\n",
    "print(embedded_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "         0,   0,   0,   0,   0,   0, 827])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedded_docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 20, 40)            200000    \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 100)               56400     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 256,501\n",
      "Trainable params: 256,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "##creating model\n",
    "embedding_vector_features=40\n",
    "model=Sequential()\n",
    "model.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))\n",
    "model.add(LSTM(100))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])          \n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Bidirectional' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-cdbe804f1fd2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSequential\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mEmbedding\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvoc_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0membedding_vector_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minput_length\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msent_length\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBidirectional\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'sigmoid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Bidirectional' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "## Creating model\n",
    "embedding_vector_features=40\n",
    "model1=Sequential()\n",
    "model1.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))\n",
    "model1.add(Bidirectional(LSTM(100)))\n",
    "model1.add(Dropout(0.3))\n",
    "model1.add(Dense(1,activation='sigmoid'))\n",
    "model1.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Creating model\n",
    "embedding_vector_features=40\n",
    "model1=Sequential()\n",
    "model1.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))\n",
    "model1.add(Bidirectional(LSTM(100)))\n",
    "model1.add(Dropout(0.3))\n",
    "model1.add(Dense(1,activation='sigmoid'))\n",
    "model1.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "print(model1.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18285, (18285,))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(embedded_docs),y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "X_final=np.array(embedded_docs)\n",
    "y_final=np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((18285, 20), (18285,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_final.shape,y_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "192/192 [==============================] - 6s 20ms/step - loss: 0.6874 - accuracy: 0.5514 - val_loss: 0.6840 - val_accuracy: 0.5665\n",
      "Epoch 2/10\n",
      "192/192 [==============================] - 3s 18ms/step - loss: 0.6625 - accuracy: 0.5880 - val_loss: 0.7047 - val_accuracy: 0.5476\n",
      "Epoch 3/10\n",
      "192/192 [==============================] - 3s 18ms/step - loss: 0.5528 - accuracy: 0.7161 - val_loss: 0.7625 - val_accuracy: 0.5387\n",
      "Epoch 4/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.5104 - accuracy: 0.7261 - val_loss: 0.8270 - val_accuracy: 0.5286\n",
      "Epoch 5/10\n",
      "192/192 [==============================] - 3s 18ms/step - loss: 0.5009 - accuracy: 0.7139 - val_loss: 0.8738 - val_accuracy: 0.5317\n",
      "Epoch 6/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.4821 - accuracy: 0.7247 - val_loss: 0.8796 - val_accuracy: 0.5403\n",
      "Epoch 7/10\n",
      "191/192 [============================>.] - ETA: 0s - loss: 0.4714 - accuracy: 0.7257"
     ]
    }
   ],
   "source": [
    "### Finally Training\n",
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred1=model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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