hatespeech.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"data_folder/tweet_dataset.csv\")\n",
    "tweet_list = []\n",
    "label_list = []\n",
    "for each in list(data.iloc[:,2]):\n",
    "    tweet_list.append(each.split())\n",
    "for each in list(data.iloc[:,1]):\n",
    "    label_list.append(each.split())\n",
    "dataset_len = len(tweet_list)\n",
    "train_tweet_list = tweet_list[:dataset_len*6//10]\n",
    "test_tweet_list = tweet_list[dataset_len*6//10:]\n",
    "train_label_list = label_list[:dataset_len*6//10]\n",
    "test_label_list = label_list[dataset_len*6//10:]\n",
    "#print(tweet_list)\n",
    "#print(set(data.iloc[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec(vocab=15377, size=100, alpha=0.025)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import Word2Vec\n",
    "model = Word2Vec(train_tweet_list,min_count = 1,sg=0)\n",
    "print(model)\n",
    "vocab = model.wv.vocab\n",
    "#vocab = vocab.toarray()\n",
    "#vocab = ['none','sexism','racism']\n",
    "vocab_vector = model[vocab]\n",
    "model.save('model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NvIMSEQ2UIpaL2uIdlIhoIMQN1GXNm8fBwE5EpZVFv/QyzvB58ZSISitO+WI/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\nhoGdiKhiGNiJiCqGgZ2IqGL+HxQEsifvOsyCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.01956367,  0.14374948, -0.27993253, -0.08090416, -0.05819423,\n",
       "        0.03509879, -0.01942267, -0.15208739,  0.03102652, -0.3336365 ,\n",
       "       -0.34257677,  0.22627914, -0.08023277, -0.24115896,  0.14834656,\n",
       "        0.15137237,  0.21476421,  0.17233242, -0.38239807, -0.16653201,\n",
       "        0.13238819,  0.36218473, -0.00964069,  0.20942046,  0.10009214,\n",
       "        0.18748273, -0.02872231, -0.09400109, -0.20383282,  0.14342627,\n",
       "       -0.15729356,  0.19261676, -0.00753925,  0.29474312,  0.05642352,\n",
       "        0.12172358,  0.22389875,  0.29731613, -0.01187667, -0.05609652,\n",
       "       -0.1693686 ,  0.15301149,  0.10824167, -0.11136031, -0.13636507,\n",
       "        0.22944883, -0.27670085,  0.15138209,  0.08663043,  0.07028219,\n",
       "        0.30322677,  0.01464205,  0.02072238,  0.22596012,  0.25158817,\n",
       "       -0.0907148 ,  0.14942132,  0.16240974, -0.00170599,  0.4022652 ,\n",
       "        0.19361246,  0.15601186,  0.10617246,  0.0214516 , -0.3670752 ,\n",
       "       -0.04768132, -0.2086651 , -0.09896099, -0.22279426, -0.17774865,\n",
       "        0.1790939 ,  0.10076404,  0.0688382 ,  0.01232329, -0.00224597,\n",
       "       -0.06937107,  0.37098017,  0.08294141,  0.00593639,  0.01588799,\n",
       "        0.06920568,  0.05905634,  0.3024078 ,  0.02013238,  0.04599744,\n",
       "        0.11868656, -0.13245231, -0.05145823,  0.2620884 ,  0.00689736,\n",
       "       -0.07516193, -0.05767205, -0.09597588,  0.05252892, -0.17785107,\n",
       "        0.17288923,  0.02528478, -0.09640027, -0.44492096,  0.46098995],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "pca = PCA(n_components = 2)\n",
    "vector = pca.fit_transform(vocab_vector)\n",
    "plt.scatter(vector[:,0],vector[:,1])\n",
    "plt.show()\n",
    "model['word']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PAX', 'ugh', 'what', 'did', 'they', 'do', 'this', 'year']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\gensim\\matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9998428225517273"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Word2Vec.load(\"model\")\n",
    "print(test_tweet_list[160])\n",
    "model.most_similar('did')[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification using Neural network\n",
    "## we are using MLPClassifier algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By taking the word vector of sentences as the average of word vector of word present in sentence as well as Word2Vec vocablury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:912: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['none' 'none' 'none' ... 'sexism' 'none' 'none']\n",
      "0.7223340040241448\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "pca = PCA(n_components = 100)\n",
    "\n",
    "X = []\n",
    "\n",
    "for each in train_tweet_list:\n",
    "    t = np.zeros(100)\n",
    "    for i in each[1:]:\n",
    "        t = t + model[i]\n",
    "    t = t / len(each)\n",
    "    X.append(t)\n",
    "#X = np.asarray(X)\n",
    "#print(len(train_label_list))\n",
    "clf = MLPClassifier(solver='adam', alpha=1e-5,hidden_layer_sizes=(100,),warm_start=True, random_state=1)\n",
    "clf.fit(X,train_label_list)\n",
    "'''\n",
    "converting test tweet dataset to word vectors\n",
    "'''\n",
    "test_vector = []\n",
    "for each in test_tweet_list:\n",
    "    t = np.zeros(100)\n",
    "    for i in each[1:]:\n",
    "        if i in vocab:\n",
    "            t = t + model[i]          # after excluding below else statement accuracy in self word vector is 0.7217149048134963\n",
    "        '''\n",
    "        else: \n",
    "            t = t + np.zeros(100)     # after including this else statement our accuracy is 0.7209410308001857\n",
    "        '''\n",
    "        '''\n",
    "            from gensim.models import FastText as ftext\n",
    "            fast_text_wv = ftext(train_tweet_list)                  this code cand be used for words not in vocab\n",
    "            word = (fast_text_wv.most_similar(i))[0][0]             some words are still giving some error\n",
    "            t = t + model[word]\n",
    "        '''\n",
    "            \n",
    "    t = t / len(each)\n",
    "    test_vector.append(t)\n",
    "pred = clf.predict(test_vector)\n",
    "print(pred)\n",
    "acc = accuracy_score(test_label_list,pred)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "By taking the word vector of sentences as the average of weighted word vector of word present in sentence as well as Word2Vec vocablury. weighted word vector can be found by multiplying word vector to its Tf-idf score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
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    {
     "name": "stdout",
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     "text": [
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    {
     "name": "stdout",
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      "computersaysno 9.591186871324563\n",
      "con 9.080361247558573\n",
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      "conspiring 9.996651979432729\n",
      "constant 8.610357618312838\n",
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      "continues 8.291903887194302\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
     "name": "stdout",
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
     "name": "stdout",
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      "resources 8.387214066998627\n",
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      "respectable 9.996651979432729\n",
      "respected 9.303504798872783\n",
      "respective 9.996651979432729\n",
      "respects 9.303504798872783\n",
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      "responded 8.492574582656454\n",
      "responding 8.74388901093736\n",
      "response 7.0522130002662875\n",
      "responses 8.89803969076462\n",
      "responsibility 8.610357618312838\n",
      "responsible 8.492574582656454\n",
      "responsive 9.591186871324563\n",
      "responsiveness 9.996651979432729\n",
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      "restaraunt 9.591186871324563\n",
      "restaraunts 9.996651979432729\n",
      "restart 9.303504798872783\n",
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      "restate 9.996651979432729\n",
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      "restaurants 6.629356149446254\n",
      "resting 9.996651979432729\n",
      "restraunt 9.996651979432729\n",
      "restrict 9.996651979432729\n",
      "restricted 9.996651979432729\n",
      "restrictions 9.303504798872783\n",
      "resturants 9.996651979432729\n",
      "result 7.393962293988344\n",
      "resulted 9.996651979432729\n",
      "resulting 9.996651979432729\n",
      "results 8.387214066998627\n",
      "resume 9.996651979432729\n",
      "resumes 9.591186871324563\n",
      "resupply 9.996651979432729\n",
      "ret 9.591186871324563\n",
      "retake 9.591186871324563\n",
      "retarded 8.610357618312838\n",
      "retards 9.591186871324563\n",
      "retelling 9.996651979432729\n",
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      "retire 9.996651979432729\n",
      "retired 9.591186871324563\n",
      "retirement 9.996651979432729\n",
      "retook 9.996651979432729\n",
      "retort 9.591186871324563\n",
      "retracted 9.080361247558573\n",
      "retracting 9.996651979432729\n",
      "retraction 9.996651979432729\n",
      "retreat 9.591186871324563\n",
      "retreating 9.996651979432729\n",
      "retribution 9.591186871324563\n",
      "retro 9.591186871324563\n",
      "return 7.694066886438683\n",
      "returned 9.591186871324563\n",
      "returning 8.89803969076462\n",
      "returns 8.89803969076462\n",
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      "revolves 9.591186871324563\n",
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      "rewritten 9.996651979432729\n",
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      "roulette 9.996651979432729\n",
      "round 5.791959360041762\n",
      "rounds 7.470923335124473\n",
      "route 9.591186871324563\n",
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      "rumadi 9.996651979432729\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
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      "subtleasasledgehammer 9.996651979432729\n",
      "subtract 9.996651979432729\n",
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      "succesess 9.996651979432729\n",
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      "successes 9.996651979432729\n",
      "successful 9.080361247558573\n",
      "successfully 9.303504798872783\n",
      "successor 9.996651979432729\n",
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      "suchathingastoomuch 9.996651979432729\n",
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      "sucked 8.492574582656454\n",
      "suckeeeeers 9.996651979432729\n",
      "sucker 9.996651979432729\n",
      "suckers 9.303504798872783\n",
      "sucking 8.291903887194302\n",
      "suckkkkers 9.996651979432729\n",
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      "suffice 9.996651979432729\n",
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      "suits 9.996651979432729\n",
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      "sulkowicz 9.996651979432729\n",
      "sulley 9.996651979432729\n",
      "sully 9.996651979432729\n",
      "sultan9421 9.591186871324563\n",
      "sultans 9.996651979432729\n",
      "sum 9.080361247558573\n",
      "summarize 9.591186871324563\n",
      "summary 9.303504798872783\n",
      "summer 9.080361247558573\n",
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      "suppr 9.996651979432729\n",
      "supre 9.591186871324563\n",
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      "surreal 9.996651979432729\n",
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      "surroundings 9.996651979432729\n",
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      "suspend 9.591186871324563\n",
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      "suspense 9.996651979432729\n",
      "suspicious 9.591186871324563\n",
      "suuuuuuuuuuuuuuuuuure 9.996651979432729\n",
      "sux 9.591186871324563\n",
      "svetlana 9.996651979432729\n",
      "svn 9.303504798872783\n",
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      "svpol 9.996651979432729\n",
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      "sw 9.591186871324563\n",
      "swaaagger 9.996651979432729\n",
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      "swater 9.996651979432729\n",
      "swating 9.996651979432729\n",
      "swear 7.224063257192947\n",
      "sweat 8.74388901093736\n",
      "sweatshirt 9.996651979432729\n",
      "sweatshop 9.996651979432729\n",
      "sweatshops 9.996651979432729\n",
      "sweden 9.591186871324563\n",
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      "sweeps 9.996651979432729\n",
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      "sweetest 9.996651979432729\n",
      "sweetheart 9.303504798872783\n",
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      "sweetie 8.89803969076462\n",
      "sweetness 9.996651979432729\n",
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      "swell 9.591186871324563\n",
      "swelled 9.996651979432729\n",
      "swellings 9.996651979432729\n",
      "swenson 9.996651979432729\n",
      "swerve 9.996651979432729\n",
      "swf 9.996651979432729\n",
      "swim 9.303504798872783\n",
      "swimmie 9.996651979432729\n",
      "swimming 9.591186871324563\n",
      "swims 9.591186871324563\n",
      "swimsuit 9.591186871324563\n",
      "swindling 9.996651979432729\n",
      "swine 9.591186871324563\n",
      "swinging 9.996651979432729\n",
      "swiping 9.996651979432729\n",
      "swirling 9.996651979432729\n",
      "switch 8.204892510204672\n",
      "switched 9.080361247558573\n",
      "switches 9.996651979432729\n",
      "switching 9.080361247558573\n",
      "switzerland 9.996651979432729\n",
      "swole 9.996651979432729\n",
      "sword 8.124849802531138\n",
      "swords 9.996651979432729\n",
      "sworn 9.996651979432729\n",
      "swung 9.996651979432729\n",
      "syd 9.080361247558573\n",
      "sydney 9.996651979432729\n",
      "sydneygirls 9.591186871324563\n",
      "sydneymardigras 9.996651979432729\n",
      "symbiotic 9.996651979432729\n",
      "symbol 9.303504798872783\n",
      "symbolic 9.996651979432729\n",
      "sympathetic 9.996651979432729\n",
      "sympathizer 9.996651979432729\n",
      "sympathy 8.74388901093736\n",
      "symphony 9.996651979432729\n",
      "symptom 9.591186871324563\n",
      "symptoms 9.996651979432729\n",
      "synagog 9.996651979432729\n",
      "synagogs 9.996651979432729\n",
      "synagogue 9.080361247558573\n",
      "sync 9.996651979432729\n",
      "syndrome 9.303504798872783\n",
      "synergistic 9.996651979432729\n",
      "synogogue 9.996651979432729\n",
      "synonym 8.89803969076462\n",
      "synonyms 9.996651979432729\n",
      "syria 6.8831366702223535\n",
      "syrian 8.610357618312838\n",
      "syriza 9.996651979432729\n",
      "syrup 9.591186871324563\n",
      "sysinstall 9.591186871324563\n",
      "system 7.393962293988344\n",
      "systematically 9.996651979432729\n",
      "systemic 9.996651979432729\n",
      "systemized 9.996651979432729\n",
      "systems 9.996651979432729\n",
      "t2 9.996651979432729\n",
      "t20 9.996651979432729\n",
      "t95 9.591186871324563\n",
      "tab 9.996651979432729\n",
      "table 7.0522130002662875\n",
      "tablet 9.591186871324563\n",
      "tabloidism 9.996651979432729\n",
      "tabloids 9.591186871324563\n",
      "tabs 9.591186871324563\n",
      "tackle 9.591186871324563\n",
      "tackled 9.591186871324563\n",
      "tackles 9.996651979432729\n",
      "tackling 9.996651979432729\n",
      "tacky 8.74388901093736\n",
      "tactic 9.303504798872783\n",
      "tactical 9.591186871324563\n",
      "tactics 8.89803969076462\n",
      "tad 9.996651979432729\n",
      "tag 8.050741830377415\n",
      "tagged 9.996651979432729\n",
      "tagging 9.996651979432729\n",
      "tags 9.591186871324563\n",
      "tah 9.996651979432729\n",
      "tahawi 9.996651979432729\n",
      "tail 8.74388901093736\n",
      "tailored 9.996651979432729\n",
      "takahashi 9.996651979432729\n",
      "take 5.565835180589414\n",
      "takedown 9.591186871324563\n",
      "taken 7.393962293988344\n",
      "takeover 9.996651979432729\n",
      "takes 7.694066886438683\n",
      "takeyourtime 9.996651979432729\n",
      "takfiris 9.996651979432729\n",
      "taking 6.905609526074413\n",
      "takingyouhome 9.080361247558573\n",
      "taksies 9.996651979432729\n",
      "tal 8.291903887194302\n",
      "talabyad 9.303504798872783\n",
      "talal2 9.303504798872783\n",
      "talamud 9.996651979432729\n",
      "tale 9.080361247558573\n",
      "talent 9.303504798872783\n",
      "talented 9.996651979432729\n",
      "talentless 9.996651979432729\n",
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      "talking 6.016970325530767\n",
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      "talladega 9.996651979432729\n",
      "taller 9.996651979432729\n",
      "talmud 9.996651979432729\n",
      "tamales 9.996651979432729\n",
      "tamer 9.996651979432729\n",
      "tamir 9.996651979432729\n",
      "tammy 9.996651979432729\n",
      "tampons 9.996651979432729\n",
      "tan 8.89803969076462\n",
      "tanesha 9.996651979432729\n",
      "tango 9.996651979432729\n",
      "tanisha 9.996651979432729\n",
      "tank 9.303504798872783\n",
      "tanked 9.996651979432729\n",
      "tanker 9.996651979432729\n",
      "tanks 8.74388901093736\n",
      "tanya 9.996651979432729\n",
      "tao 8.050741830377415\n",
      "taoist 9.996651979432729\n",
      "taoists 9.303504798872783\n",
      "tap 9.996651979432729\n",
      "tape 9.080361247558573\n",
      "tapson 8.610357618312838\n",
      "taqiya 9.996651979432729\n",
      "taqiyya 8.050741830377415\n",
      "taquiyya 8.050741830377415\n",
      "tar 9.591186871324563\n",
      "tarah 9.591186871324563\n",
      "taralovescats 9.996651979432729\n",
      "target 7.4317026219711915\n",
      "targeted 7.598756706634357\n",
      "targeting 8.74388901093736\n",
      "targets 7.9817489588904635\n",
      "tarnishes 9.996651979432729\n",
      "tarp 9.996651979432729\n",
      "tart 6.455692655395414\n",
      "tarts 8.387214066998627\n",
      "taryi 9.996651979432729\n",
      "task 9.591186871324563\n",
      "tasks 9.996651979432729\n",
      "tassie 9.996651979432729\n",
      "taste 7.645276722269251\n",
      "tasted 9.996651979432729\n",
      "tasteless 9.996651979432729\n",
      "tasteofsydney 9.080361247558573\n",
      "tasteperth 9.996651979432729\n",
      "tastes 8.74388901093736\n",
      "tasting 9.996651979432729\n",
      "tasty 9.996651979432729\n",
      "tattoo 9.303504798872783\n",
      "tattoos 9.591186871324563\n",
      "taught 8.291903887194302\n",
      "taunted 9.996651979432729\n",
      "taunting 9.996651979432729\n",
      "tax 8.74388901093736\n",
      "taxes 9.591186871324563\n",
      "taxpayer 9.996651979432729\n",
      "tay 9.996651979432729\n",
      "taylor 9.996651979432729\n",
      "tb 9.996651979432729\n",
      "tbh 7.9817489588904635\n",
      "tch 9.591186871324563\n",
      "tcot 7.9172104377528925\n",
      "te 8.74388901093736\n",
      "tea 8.89803969076462\n",
      "teach 7.9172104377528925\n",
      "teacher 9.080361247558573\n",
      "teachers 8.610357618312838\n",
      "teaches 8.610357618312838\n",
      "teaching 8.610357618312838\n",
      "team 6.441303917943315\n",
      "teamlynnandtony 9.996651979432729\n",
      "teammate 9.591186871324563\n",
      "teammates 9.996651979432729\n",
      "teamrobertandlynzey 9.996651979432729\n",
      "teams 6.007667932868453\n",
      "teamsgofrantic 9.996651979432729\n",
      "teamsteve 9.996651979432729\n",
      "teamtex 9.996651979432729\n",
      "teamvic 9.996651979432729\n",
      "teaparty 9.996651979432729\n",
      "tear 8.74388901093736\n",
      "tearing 9.996651979432729\n",
      "tears 8.204892510204672\n",
      "teasers 9.996651979432729\n",
      "teasing 9.996651979432729\n",
      "tech 6.470291454816567\n",
      "technical 8.387214066998627\n",
      "technically 8.74388901093736\n",
      "technicallychallenged 9.996651979432729\n",
      "technique 9.591186871324563\n",
      "techniques 9.996651979432729\n",
      "technological 9.996651979432729\n",
      "technologically 9.996651979432729\n",
      "technologists 9.996651979432729\n",
      "technology 9.303504798872783\n",
      "techraptr 9.996651979432729\n",
      "ted 9.303504798872783\n",
      "tedious 9.996651979432729\n",
      "tee 9.996651979432729\n",
      "teen 9.080361247558573\n",
      "teenage 9.591186871324563\n",
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      "teenagers 9.996651979432729\n",
      "teenagets 9.996651979432729\n",
      "teens 9.080361247558573\n",
      "teetering 9.996651979432729\n",
      "teeth 8.387214066998627\n",
      "tel 9.591186871324563\n",
      "telaviv 9.996651979432729\n",
      "tele 9.591186871324563\n",
      "telecast 9.996651979432729\n",
      "telegram 9.996651979432729\n",
      "telephone 9.996651979432729\n",
      "televised 9.591186871324563\n",
      "televisetherevolution 9.996651979432729\n",
      "television 8.291903887194302\n",
      "televisions 9.996651979432729\n",
      "tell 5.620894957772442\n",
      "telling 6.952129541709305\n",
      "tellingly 9.996651979432729\n",
      "tellings 9.996651979432729\n",
      "tells 7.0522130002662875\n",
      "telly 9.996651979432729\n",
      "temir 9.996651979432729\n",
      "temp 9.996651979432729\n",
      "tempered 9.996651979432729\n",
      "template 9.996651979432729\n",
      "temples 9.303504798872783\n",
      "templeton 9.996651979432729\n",
      "temporarily 9.591186871324563\n",
      "temporary 9.303504798872783\n",
      "tempt 9.996651979432729\n",
      "tempted 9.303504798872783\n",
      "tempting 9.080361247558573\n",
      "ten 7.799427402096509\n",
      "tend 8.050741830377415\n",
      "tendency 9.996651979432729\n",
      "tender 9.591186871324563\n",
      "tends 9.996651979432729\n",
      "tennessee 9.996651979432729\n",
      "tennis 7.9817489588904635\n",
      "tens 8.89803969076462\n",
      "tense 9.996651979432729\n",
      "tension 9.591186871324563\n",
      "tent 8.050741830377415\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tentacles 9.996651979432729\n",
      "tenth 9.996651979432729\n",
      "tenths 9.996651979432729\n",
      "tents 9.591186871324563\n",
      "tenure 9.996651979432729\n",
      "tenured 9.996651979432729\n",
      "tequila 9.303504798872783\n",
      "term 7.9817489588904635\n",
      "terminate 9.996651979432729\n",
      "terminology 9.591186871324563\n",
      "termites 9.996651979432729\n",
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      "terrorize 9.996651979432729\n",
      "terrorizing 9.996651979432729\n",
      "terry 9.996651979432729\n",
      "test 8.124849802531138\n",
      "testament 8.89803969076462\n",
      "tested 9.996651979432729\n",
      "testerosterone 9.996651979432729\n",
      "testicle 9.996651979432729\n",
      "testicles 9.996651979432729\n",
      "testimony 9.303504798872783\n",
      "testing 9.591186871324563\n",
      "tests 9.996651979432729\n",
      "tetris 9.080361247558573\n",
      "tex 9.996651979432729\n",
      "texa 9.996651979432729\n",
      "texan 8.492574582656454\n",
      "texans 9.996651979432729\n",
      "texas 8.610357618312838\n",
      "text 8.492574582656454\n",
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      "texts 9.303504798872783\n",
      "texture 9.996651979432729\n",
      "tf 9.080361247558573\n",
      "tfa 9.996651979432729\n",
      "tgooff 9.996651979432729\n",
      "th 8.492574582656454\n",
      "thai 9.996651979432729\n",
      "thaistyle 9.996651979432729\n",
      "than 4.806476771504395\n",
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      "thankafeminist 9.996651979432729\n",
      "thankfully 9.996651979432729\n",
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      "thatswhatloosewomensfor 9.996651979432729\n",
      "thatswhatyougetforlaughingatothers 9.996651979432729\n",
      "thatwouldbeaviolationoftherestrainingorder 9.996651979432729\n",
      "the 2.052337136185491\n",
      "theater 9.591186871324563\n",
      "theatre 9.996651979432729\n",
      "theblock 9.080361247558573\n",
      "theblockbot 9.996651979432729\n",
      "thecockroachthatjustwontdie 9.996651979432729\n",
      "thedanishgirl 9.996651979432729\n",
      "thedooristhatway 9.996651979432729\n",
      "thedream 9.996651979432729\n",
      "thedress 8.492574582656454\n",
      "theenvygap 9.996651979432729\n",
      "theft 9.591186871324563\n",
      "thegameihated 9.996651979432729\n",
      "thegoblin 9.996651979432729\n",
      "thegoodguysau 9.996651979432729\n",
      "their 4.330225291320296\n",
      "theirs 8.89803969076462\n",
      "thejet 9.996651979432729\n",
      "thelead 9.996651979432729\n",
      "them 4.387180184247768\n",
      "theme 8.74388901093736\n",
      "themed 9.996651979432729\n",
      "themes 9.996651979432729\n",
      "themoreyouknow 9.996651979432729\n",
      "themselves 6.413133040976618\n",
      "then 5.196737716652125\n",
      "thenextnicheeroticafad 9.996651979432729\n",
      "theocracy 9.303504798872783\n",
      "theocratic 9.996651979432729\n",
      "theocrats 9.996651979432729\n",
      "theological 9.996651979432729\n",
      "theoretical 9.996651979432729\n",
      "theories 8.291903887194302\n",
      "theorist 9.996651979432729\n",
      "theorize 9.996651979432729\n",
      "theory 8.204892510204672\n",
      "thepommies 9.996651979432729\n",
      "thepromogirls 9.996651979432729\n",
      "ther 9.996651979432729\n",
      "there 4.190012119336819\n",
      "therefore 8.492574582656454\n",
      "theres 9.591186871324563\n",
      "theresa 9.996651979432729\n",
      "theriault 9.591186871324563\n",
      "these 5.009626550975606\n",
      "theshade 9.996651979432729\n",
      "theshift 9.996651979432729\n",
      "thesis 9.996651979432729\n",
      "thetruth 9.996651979432729\n",
      "thevoiceau 9.591186871324563\n",
      "thewalkingdead 9.996651979432729\n",
      "thewolf 8.89803969076462\n",
      "they 3.4008714654714165\n",
      "theycantcook 9.996651979432729\n",
      "theyd 9.996651979432729\n",
      "theyllbeback 9.996651979432729\n",
      "theyneedtogo 9.996651979432729\n",
      "theyre 9.996651979432729\n",
      "theyrejustannoying 9.996651979432729\n",
      "thick 9.591186871324563\n",
      "thicker 9.996651979432729\n",
      "thief 9.996651979432729\n",
      "thiefs 9.591186871324563\n",
      "thigh 9.591186871324563\n",
      "thighing 9.996651979432729\n",
      "thighs 8.89803969076462\n",
      "thin 8.492574582656454\n",
      "thing 5.396494335268181\n",
      "things 5.614625344758847\n",
      "thingsbetterthankatandandre 9.996651979432729\n",
      "thingsiwastoldaboutmyscrewdriver 9.996651979432729\n",
      "thingy 9.996651979432729\n",
      "think 4.461288156401491\n",
      "thinker 9.996651979432729\n",
      "thinkers 9.996651979432729\n",
      "thinkin 9.996651979432729\n",
      "thinking 6.905609526074413\n",
      "thinkprogress 9.996651979432729\n",
      "thinks 6.928599044299111\n",
      "thinned 9.996651979432729\n",
      "third 7.5543049440635235\n",
      "thirds 9.996651979432729\n",
      "thirst48 9.591186871324563\n",
      "this 3.4811988220324745\n",
      "thise 9.996651979432729\n",
      "thisisislam 9.996651979432729\n",
      "tho 7.511745329644728\n",
      "thomas 9.080361247558573\n",
      "thomas93 9.996651979432729\n",
      "thoose 9.996651979432729\n",
      "thopia 9.996651979432729\n",
      "thor 7.288601778330518\n",
      "thorldemort 9.996651979432729\n",
      "thorlorealadsherewecome 9.996651979432729\n",
      "thoroughly 9.591186871324563\n",
      "those 5.230213645848514\n",
      "thots 9.996651979432729\n",
      "thou 9.996651979432729\n",
      "though 6.399339718844282\n",
      "thought 5.980268958680339\n",
      "thoughtful 9.996651979432729\n",
      "thoughts 8.492574582656454\n",
      "thousand 8.204892510204672\n",
      "thousands 7.5543049440635235\n",
      "thrall 9.996651979432729\n",
      "thread 7.9817489588904635\n",
      "threadbare 9.996651979432729\n",
      "threading 9.996651979432729\n",
      "threads 9.996651979432729\n",
      "threat 7.9172104377528925\n",
      "threaten 8.387214066998627\n",
      "threatened 8.204892510204672\n",
      "threatening 8.291903887194302\n",
      "threatens 9.996651979432729\n",
      "threats 7.393962293988344\n",
      "three 7.026237513863027\n",
      "threewordsshewantstohear 9.996651979432729\n",
      "threshold 9.591186871324563\n",
      "threw 8.610357618312838\n",
      "thrift 9.996651979432729\n",
      "thrilling 9.996651979432729\n",
      "thrills 9.996651979432729\n",
      "thrive 9.591186871324563\n",
      "throat 9.591186871324563\n",
      "throats 9.591186871324563\n",
      "throne 9.996651979432729\n",
      "throttle 9.996651979432729\n",
      "througg 9.996651979432729\n",
      "through 5.769818234164549\n",
      "throughout 9.303504798872783\n",
      "throw 7.224063257192947\n",
      "throwback 9.996651979432729\n",
      "throwing 7.9817489588904635\n",
      "thrown 9.080361247558573\n",
      "throws 8.74388901093736\n",
      "thru 8.204892510204672\n",
      "thrush 9.303504798872783\n",
      "tht 9.996651979432729\n",
      "thu 9.996651979432729\n",
      "thug 9.591186871324563\n",
      "thuggery 9.996651979432729\n",
      "thugs 7.799427402096509\n",
      "thumbs 9.996651979432729\n",
      "thunder 9.996651979432729\n",
      "thundercunt 9.996651979432729\n",
      "thunderfury 9.996651979432729\n",
      "thursday 8.89803969076462\n",
      "thus 9.996651979432729\n",
      "thx 9.303504798872783\n",
      "thxbai 9.996651979432729\n",
      "thy 9.996651979432729\n",
      "thyme 9.996651979432729\n",
      "ti 9.996651979432729\n",
      "tick 9.996651979432729\n",
      "ticket 9.080361247558573\n",
      "ticketing 9.303504798872783\n",
      "tickets 8.610357618312838\n",
      "tide 9.996651979432729\n",
      "tidies 9.996651979432729\n",
      "tie 9.996651979432729\n",
      "tied 9.303504798872783\n",
      "ties 9.080361247558573\n",
      "tight 8.74388901093736\n",
      "tighten 9.996651979432729\n",
      "tights 9.996651979432729\n",
      "tikrit 7.645276722269251\n",
      "til 8.492574582656454\n",
      "tilkef 9.996651979432729\n",
      "till 8.124849802531138\n",
      "tilt 9.996651979432729\n",
      "tim 9.080361247558573\n",
      "tim84 9.996651979432729\n",
      "time 4.733961790527843\n",
      "timeline 8.291903887194302\n",
      "timelines 9.080361247558573\n",
      "timely 9.591186871324563\n",
      "timer 9.996651979432729\n",
      "timers 9.591186871324563\n",
      "times 6.3330903333030815\n",
      "times3 9.080361247558573\n",
      "times4 9.996651979432729\n",
      "timespan 9.996651979432729\n",
      "timonen 9.996651979432729\n",
      "timwisebookclub 9.996651979432729\n",
      "tin 9.080361247558573\n",
      "tina 9.996651979432729\n",
      "tiniest 9.996651979432729\n",
      "tinned 9.591186871324563\n",
      "tins 9.591186871324563\n",
      "tint 9.996651979432729\n",
      "tiny 7.856585815936457\n",
      "tinyteeth 9.996651979432729\n",
      "tip 9.080361247558573\n",
      "tipoff 9.996651979432729\n",
      "tips 9.591186871324563\n",
      "tirade 9.996651979432729\n",
      "tired 7.598756706634357\n",
      "tiresome 9.996651979432729\n",
      "tis 9.996651979432729\n",
      "tissue 9.996651979432729\n",
      "titanic 9.591186871324563\n",
      "title 8.74388901093736\n",
      "titled 9.996651979432729\n",
      "titles 9.996651979432729\n",
      "tits 8.610357618312838\n",
      "tiz 9.996651979432729\n",
      "tl 8.204892510204672\n",
      "tldr 9.996651979432729\n",
      "tlel 9.996651979432729\n",
      "tm 9.996651979432729\n",
      "tmblr 9.996651979432729\n",
      "tmrw 9.996651979432729\n",
      "tms7 9.591186871324563\n",
      "to 2.3323053473341107\n",
      "toast 8.387214066998627\n",
      "toasted 9.996651979432729\n",
      "toaster 9.996651979432729\n",
      "toastie 9.591186871324563\n",
      "toasting 9.591186871324563\n",
      "tobs 8.89803969076462\n",
      "toby 9.996651979432729\n",
      "today 5.919114535527009\n",
      "todays 9.591186871324563\n",
      "toddler 9.996651979432729\n",
      "toddlers 9.591186871324563\n",
      "todo 9.996651979432729\n",
      "toe 9.996651979432729\n",
      "toes 9.080361247558573\n",
      "together 7.000919705878737\n",
      "toggle 9.591186871324563\n",
      "tohave 9.996651979432729\n",
      "toilet 9.303504798872783\n",
      "toilets 9.591186871324563\n",
      "token 9.080361247558573\n",
      "tokens 9.591186871324563\n",
      "told 6.530916076633002\n",
      "tolerance 9.080361247558573\n",
      "tolerant 9.080361247558573\n",
      "tolerate 8.291903887194302\n",
      "tolerated 9.591186871324563\n",
      "tolerating 9.591186871324563\n",
      "toll 9.996651979432729\n",
      "tom 9.591186871324563\n",
      "tomato 9.080361247558573\n",
      "tomatoes 9.080361247558573\n",
      "tomb 9.996651979432729\n",
      "tomorrow 7.35759464981747\n",
      "tomz 9.591186871324563\n",
      "ton 9.996651979432729\n",
      "tone 8.74388901093736\n",
      "toner 9.996651979432729\n",
      "tones 9.996651979432729\n",
      "tongs 9.996651979432729\n",
      "tonight 5.814601836791522\n",
      "tonights 9.591186871324563\n",
      "tons 9.591186871324563\n",
      "tony 6.976227093288365\n",
      "too 5.140723075097454\n",
      "toodles 9.996651979432729\n",
      "took 7.3225033300061995\n",
      "tool 8.492574582656454\n",
      "toolazy 9.996651979432729\n",
      "toolbar 9.996651979432729\n",
      "tooling 9.996651979432729\n",
      "tools 7.9172104377528925\n",
      "toooooo 9.996651979432729\n",
      "toot 9.996651979432729\n",
      "tooth 9.996651979432729\n",
      "toothpicks 9.591186871324563\n",
      "toowoomba 9.996651979432729\n",
      "top 6.8185981490847825\n",
      "topgear 9.996651979432729\n",
      "topic 9.303504798872783\n",
      "topical 9.591186871324563\n",
      "toppa 9.996651979432729\n",
      "topping 9.303504798872783\n",
      "toppings 9.996651979432729\n",
      "tops 9.303504798872783\n",
      "tor 9.996651979432729\n",
      "torah 9.080361247558573\n",
      "torch 9.303504798872783\n",
      "torched 9.996651979432729\n",
      "tori 9.996651979432729\n",
      "torn 9.996651979432729\n",
      "toronto 9.996651979432729\n",
      "torrenting 9.996651979432729\n",
      "tortiers 9.996651979432729\n",
      "tortilla 9.996651979432729\n",
      "tortillas 9.996651979432729\n",
      "tortious 9.996651979432729\n",
      "tortoise 9.591186871324563\n",
      "torture 8.610357618312838\n",
      "tortured 9.080361247558573\n",
      "torturing 9.591186871324563\n",
      "torturous 9.996651979432729\n",
      "tory 9.996651979432729\n",
      "tos 8.387214066998627\n",
      "toss 9.080361247558573\n",
      "tossed 9.591186871324563\n",
      "tosser 9.996651979432729\n",
      "tostadas 9.996651979432729\n",
      "total 7.193291598526193\n",
      "totalitarianism 9.591186871324563\n",
      "totally 6.952129541709305\n",
      "totallyneededtobetweeted 9.996651979432729\n",
      "totem 9.996651979432729\n",
      "toter 9.996651979432729\n",
      "totes 8.291903887194302\n",
      "tou 9.591186871324563\n",
      "touch 8.050741830377415\n",
      "touched 9.996651979432729\n",
      "touching 9.996651979432729\n",
      "tough 8.124849802531138\n",
      "tougher 9.996651979432729\n",
      "toure 9.996651979432729\n",
      "tourneys 9.996651979432729\n",
      "touts 9.996651979432729\n",
      "tow 9.996651979432729\n",
      "toward 9.996651979432729\n",
      "towards 7.598756706634357\n",
      "towel 9.996651979432729\n",
      "towels 9.996651979432729\n",
      "tower 9.996651979432729\n",
      "town 7.9172104377528925\n",
      "townhouse 9.996651979432729\n",
      "towns 9.996651979432729\n",
      "townsend 9.591186871324563\n",
      "toxic 8.89803969076462\n",
      "toxicity 9.591186871324563\n",
      "toxickat 9.996651979432729\n",
      "toy 9.591186871324563\n",
      "toyota 9.996651979432729\n",
      "toys 9.591186871324563\n",
      "tr 9.591186871324563\n",
      "trac 9.996651979432729\n",
      "trace 9.996651979432729\n",
      "tracey 9.996651979432729\n",
      "track 8.124849802531138\n",
      "tracking 9.591186871324563\n",
      "tracks 9.996651979432729\n",
      "traction 9.303504798872783\n",
      "trade 9.303504798872783\n",
      "trademarkfeminist 9.996651979432729\n",
      "trader 7.799427402096509\n",
      "tradesmen 9.996651979432729\n",
      "tradie 9.591186871324563\n",
      "trading 9.996651979432729\n",
      "tradingpost 9.996651979432729\n",
      "tradition 9.996651979432729\n",
      "traditional 9.303504798872783\n",
      "traffic 9.303504798872783\n",
      "tragedy 9.303504798872783\n",
      "tragic 9.591186871324563\n",
      "trail 9.591186871324563\n",
      "trailer 9.303504798872783\n",
      "train 8.387214066998627\n",
      "trained 8.89803969076462\n",
      "trainer 9.996651979432729\n",
      "trainers 9.996651979432729\n",
      "training 8.610357618312838\n",
      "trains 9.591186871324563\n",
      "trainwreck 9.996651979432729\n",
      "traitor 9.996651979432729\n",
      "traitors 9.591186871324563\n",
      "tranquiliser 9.996651979432729\n",
      "trans 8.492574582656454\n",
      "transcript 9.996651979432729\n",
      "transexual 9.996651979432729\n",
      "transfer 9.996651979432729\n",
      "transference 9.996651979432729\n",
      "transformers 9.996651979432729\n",
      "transgender 9.996651979432729\n",
      "transition 9.303504798872783\n",
      "translate 8.89803969076462\n",
      "translated 9.591186871324563\n",
      "translates 9.591186871324563\n",
      "translating 9.996651979432729\n",
      "translation 9.591186871324563\n",
      "translations 8.204892510204672\n",
      "translators 7.9817489588904635\n",
      "transparency 9.996651979432729\n",
      "transparently 9.996651979432729\n",
      "transphobic 9.996651979432729\n",
      "transport 9.303504798872783\n",
      "transportation 9.996651979432729\n",
      "trap 9.591186871324563\n",
      "trapped 9.591186871324563\n",
      "traps 9.996651979432729\n",
      "trash 7.856585815936457\n",
      "trashbros 9.996651979432729\n",
      "trashy 8.89803969076462\n",
      "trauma 9.591186871324563\n",
      "traumatized 9.996651979432729\n",
      "travel 8.89803969076462\n",
      "traveled 9.996651979432729\n",
      "traveling 9.080361247558573\n",
      "travesty 9.996651979432729\n",
      "travolta 9.996651979432729\n",
      "trawl 9.996651979432729\n",
      "tread 9.996651979432729\n",
      "treadmills 9.996651979432729\n",
      "treason 9.996651979432729\n",
      "treasure 9.591186871324563\n",
      "treat 8.610357618312838\n",
      "treated 7.694066886438683\n",
      "treatement 9.996651979432729\n",
      "treating 9.080361247558573\n",
      "treatment 8.610357618312838\n",
      "treats 9.080361247558573\n",
      "treaty 9.303504798872783\n",
      "tree 9.303504798872783\n",
      "trees 9.996651979432729\n",
      "trek 8.610357618312838\n",
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      "zionism 9.303504798872783\n",
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      "zionists 9.996651979432729\n",
      "zips 9.996651979432729\n",
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      "zoe 8.387214066998627\n",
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      "zozo 9.996651979432729\n",
      "zucchini 9.591186871324563\n",
      "zuckerberg 9.996651979432729\n",
      "zyme 9.996651979432729\n",
      "zynga 9.303504798872783\n",
      "zzzz 9.996651979432729\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"from sklearn.feature_extraction.text import TfidfVectorizer\\nvectorizer = TfidfVectorizer(stop_words=None)\\nvec = vectorizer.fit_transform(data.iloc[:,2])\\nv = dict(zip(vectorizer.get_feature_names(),vectorizer.idf_))\\nvec[0, vectorizer.vocabulary_['word']] \""
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "# our corpus\n",
    "\n",
    "cv = CountVectorizer()\n",
    "\n",
    "# convert text data into term-frequency matrix\n",
    "dat = cv.fit_transform(data.iloc[:,2])\n",
    "\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "\n",
    "# convert term-frequency matrix into tf-idf\n",
    "tfidf_matrix = tfidf_transformer.fit_transform(dat)\n",
    "\n",
    "# create dictionary to find a tfidf word each word\n",
    "word2tfidf = dict(zip(cv.get_feature_names(), tfidf_transformer.idf_))\n",
    "\n",
    "for word, score in word2tfidf.items():\n",
    "    print(word, score)\n",
    "'''from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(stop_words=None)\n",
    "vec = vectorizer.fit_transform(data.iloc[:,2])\n",
    "v = dict(zip(vectorizer.get_feature_names(),vectorizer.idf_))\n",
    "vec[0, vectorizer.vocabulary_['word']] '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'I'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-159-9a3325d1d67a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0meach\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m         \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meach\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'I'"
     ]
    }
   ],
   "source": [
    "for each in train_tweet_list:\n",
    "    t = np.zeros(100)\n",
    "    for i in each[1:]:\n",
    "        t = t + model[i] * v[i]\n",
    "    t = t / len(each)\n",
    "    X.append(t)\n",
    "#X = np.asarray(X)\n",
    "#print(len(train_label_list))\n",
    "clf = MLPClassifier(solver='adam', alpha=1e-5,hidden_layer_sizes=(100,),warm_start=True, random_state=1)\n",
    "clf.fit(X,train_label_list)\n",
    "'''\n",
    "converting test tweet dataset to word vectors\n",
    "'''\n",
    "test_vector = []\n",
    "for each in test_tweet_list:\n",
    "    t = np.zeros(100)\n",
    "    for i in each[1:]:\n",
    "        if i in vocab:\n",
    "            t = t + model[i]          # after excluding below else statement accuracy in self word vector is 0.7217149048134963\n",
    "        '''\n",
    "        else: \n",
    "            t = t + np.zeros(100)     # after including this else statement our accuracy is 0.7209410308001857\n",
    "        '''\n",
    "        '''\n",
    "            from gensim.models import FastText as ftext\n",
    "            fast_text_wv = ftext(train_tweet_list)                  this code cand be used for words not in vocab\n",
    "            word = (fast_text_wv.most_similar(i))[0][0]             some words are still giving some error\n",
    "            t = t + model[word]\n",
    "        '''\n",
    "            \n",
    "    t = t / len(each)\n",
    "    test_vector.append(t)\n",
    "pred = clf.predict(test_vector)\n",
    "print(pred)\n",
    "acc = accuracy_score(test_label_list,pred)\n",
    "print(acc)"
   ]
  }
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