tweets_emotion_detection_w2v.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emotion detection from tweets (lexicon + word2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1167: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gensim.models import KeyedVectors\n",
    "from feature_extraction import FeatureExtractionContextValenceShifting\n",
    "from feature_selection import generate_initial_features, feature_selection \n",
    "from preprocessing import fix_encoding, split_tweet_sentences, tokenize_tweets, get_lemmas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load and preprocess dataset\n",
    "\n",
    "Load the dataset contaning the tweets and their emotions. For text preprocessing first the tweet text is encoded (fixed), then the tweet is tokenized, and finally the tokens are lematized. The dataset needs to have a column with name tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id    class                                             tokens  \\\n",
      "0  1000001  neutral  [[Check, this, video, out, -, -, President, Ob...   \n",
      "1  1000002  neutral  [[need, suggestions, for, a, good, IR, filter,...   \n",
      "2  1000003  neutral  [[@surfit, :, I, just, checked, my, google, fo...   \n",
      "3  1000004  neutral  [[is, in, San, Francisco, at, Bay, to, Breaker...   \n",
      "4  1000005  neutral               [[just, landed, at, San, Francisco]]   \n",
      "5  1000006  neutral  [[San, Francisco, today, .], [Any, suggestions...   \n",
      "6  1000007  neutral  [[On, my, way, to, see, Star, Trek, @, The, Es...   \n",
      "7  1000008  neutral  [[Going, to, see, star, trek, soon, with, my, ...   \n",
      "8  1000009  neutral  [[Bill, Simmons, in, conversation, with, Malco...   \n",
      "9  1000010  neutral    [[playing, with, cURL, and, the, Twitter, API]]   \n",
      "\n",
      "                                              lemmas  \n",
      "0  [[Check, this, video, out, -, -, President, Ob...  \n",
      "1  [[need, suggestion, for, a, good, IR, filter, ...  \n",
      "2  [[@user, :, I, just, check, my, google, for, m...  \n",
      "3  [[be, in, San, Francisco, at, Bay, to, Breaker...  \n",
      "4                 [[just, land, at, San, Francisco]]  \n",
      "5  [[San, Francisco, today, .], [Any, suggestion,...  \n",
      "6  [[On, my, way, to, see, Star, Trek, @, The, Es...  \n",
      "7  [[Going, to, see, star, trek, soon, with, my, ...  \n",
      "8  [[Bill, Simmons, in, conversation, with, Malco...  \n",
      "9       [[play, with, cURL, and, the, Twitter, API]]  \n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_excel('data/merged_datasets.xlsx')\n",
    "# fix the tweet text\n",
    "dataset = fix_encoding(dataset)\n",
    "# split the tweet text into sentences\n",
    "dataset = split_tweet_sentences(dataset)\n",
    "# tokenize each sentence of the tweets\n",
    "dataset = tokenize_tweets(dataset)\n",
    "# lemmatise the tweets\n",
    "dataset = get_lemmas(dataset)\n",
    "\n",
    "dataset = dataset.drop(['emotion_intensity', 'tweet'], axis=1)\n",
    "print(dataset.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load lexicon and word2vec model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lexicon = pd.read_csv('lexicons/Ratings_Warriner_et_al.csv', usecols=[0, 1, 2, 5], index_col=0)\n",
    "lexicon.columns = ['word', 'valence', 'arousal']\n",
    "model = KeyedVectors.load_word2vec_format('glove.twitter.27B.200d.txt', binary=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_jar = 'stanford_parser/stanford-parser.jar'\n",
    "path_to_models_jar = 'stanford_parser/stanford-parser-3.9.1-models.jar'\n",
    "valence_shifter = FeatureExtractionContextValenceShifting(path_to_jar, path_to_models_jar, lexicon, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "5338\n",
      "5339\n",
      "5340\n",
      "5341\n",
      "5342\n",
      "5343\n",
      "5344\n",
      "5345\n",
      "5346\n",
      "5347\n",
      "        id    class                                             tokens  \\\n",
      "0  1000001  neutral  [[Check, this, video, out, -, -, President, Ob...   \n",
      "1  1000002  neutral  [[need, suggestions, for, a, good, IR, filter,...   \n",
      "2  1000003  neutral  [[@surfit, :, I, just, checked, my, google, fo...   \n",
      "3  1000004  neutral  [[is, in, San, Francisco, at, Bay, to, Breaker...   \n",
      "4  1000005  neutral               [[just, landed, at, San, Francisco]]   \n",
      "5  1000006  neutral  [[San, Francisco, today, .], [Any, suggestions...   \n",
      "6  1000007  neutral  [[On, my, way, to, see, Star, Trek, @, The, Es...   \n",
      "7  1000008  neutral  [[Going, to, see, star, trek, soon, with, my, ...   \n",
      "8  1000009  neutral  [[Bill, Simmons, in, conversation, with, Malco...   \n",
      "9  1000010  neutral    [[playing, with, cURL, and, the, Twitter, API]]   \n",
      "\n",
      "                                              lemmas  \\\n",
      "0  [[Check, this, video, out, -, -, President, Ob...   \n",
      "1  [[need, suggestion, for, a, good, IR, filter, ...   \n",
      "2  [[@user, :, I, just, check, my, google, for, m...   \n",
      "3  [[be, in, San, Francisco, at, Bay, to, Breaker...   \n",
      "4                 [[just, land, at, San, Francisco]]   \n",
      "5  [[San, Francisco, today, .], [Any, suggestion,...   \n",
      "6  [[On, my, way, to, see, Star, Trek, @, The, Es...   \n",
      "7  [[Going, to, see, star, trek, soon, with, my, ...   \n",
      "8  [[Bill, Simmons, in, conversation, with, Malco...   \n",
      "9       [[play, with, cURL, and, the, Twitter, API]]   \n",
      "\n",
      "                                            valences  \n",
      "0  [[0, 0, 2.1399999999999997, 0, 0, 0, 0.6900000...  \n",
      "1  [[0.9500000000000002, 1.29, 0, 0, 3.3899999999...  \n",
      "2  [[0, 0, 0, 0, 2.05, 0, 0, 0, 0, 0.980000000000...  \n",
      "3     [[1.6799999999999997, 0, 0, 0, 0, 0, 0, 0, 0]]  \n",
      "4                 [[0, 2.1100000000000003, 0, 0, 0]]  \n",
      "5                       [[0, 0, 0, 0], [0, 1.29, 0]]  \n",
      "6  [[0, 0, 1.4100000000000001, 0, 1.7699999999999...  \n",
      "7  [[0, 0, 1.7699999999999996, 2.9699999999999998...  \n",
      "8           [[0, 0, 0, 2.2199999999999998, 0, 0, 0]]  \n",
      "9                         [[3.05, 0, 0, 0, 0, 0, 0]]  \n"
     ]
    }
   ],
   "source": [
    "# set intial valences from lexicon\n",
    "dataset = valence_shifter.get_initial_valences(dataset)\n",
    "print(dataset.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id    class                                             lemmas  \\\n",
      "0  1000001  neutral  [Check, this, video, out, -, -, President, Oba...   \n",
      "1  1000002  neutral  [need, suggestion, for, a, good, IR, filter, f...   \n",
      "2  1000003  neutral  [@user, :, I, just, check, my, google, for, my...   \n",
      "3  1000004  neutral  [be, in, San, Francisco, at, Bay, to, Breakers...   \n",
      "4  1000005  neutral                   [just, land, at, San, Francisco]   \n",
      "5  1000006  neutral     [San, Francisco, today, ., Any, suggestion, ?]   \n",
      "6  1000007  neutral  [On, my, way, to, see, Star, Trek, @, The, Esq...   \n",
      "7  1000008  neutral  [Going, to, see, star, trek, soon, with, my, d...   \n",
      "8  1000009  neutral  [Bill, Simmons, in, conversation, with, Malcol...   \n",
      "9  1000010  neutral         [play, with, cURL, and, the, Twitter, API]   \n",
      "\n",
      "                                            valences  \n",
      "0  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "1  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "2  [-1.0, -1.0, -1.0, -1.0, 10.0, -1.0, -1.0, -1....  \n",
      "3  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "4  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "5  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "6  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "7  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "8  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "9  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n"
     ]
    }
   ],
   "source": [
    "featured_dataset, vocab = generate_initial_features(dataset)\n",
    "print(featured_dataset.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_selection\n",
      "[('happy', 0.0147692113994374), ('smile', 0.013133109509455707), ('sad', 0.010591469338830296), ('nervous', 0.01028263271920578), ('hilarious', 0.009752593377570035), ('depression', 0.008801832461743096), ('sadness', 0.008402469515901975), ('anger', 0.008035256650967888), ('angry', 0.00793158018516494), ('laughter', 0.007883731071264773), ('bitter', 0.007740549674882349), ('rage', 0.007735182124302216), ('panic', 0.007660474719828616), ('nightmare', 0.007308263987142625), ('cheer', 0.007249617712957684), ('offend', 0.007044573447026414), ('revenge', 0.0069133248297511), ('terrorism', 0.006742180310809721), ('unhappy', 0.006590128224346603), ('outrage', 0.006552950318321929), ('anxiety', 0.00652067445254612), ('fear', 0.0061322934392994384), ('awful', 0.006098256748499944), ('grim', 0.005688584222345526), ('horror', 0.005533732860250167), ('lively', 0.00527643037373462), ('depress', 0.005237710059735175), ('bully', 0.004976181485624869), ('glee', 0.004853546491324361), ('shocking', 0.004817777333996516), ('terror', 0.004810995469606828), ('sadly', 0.004802224522097606), ('rejoice', 0.004791366510875064), ('terrible', 0.004746842689250267), ('fuming', 0.004606415797999118), ('optimism', 0.004524052020211193), ('horrible', 0.004352225371150597), ('fume', 0.004334497619952804), ('lost', 0.004330678537150982), ('dark', 0.0041842324858499015), ('hilarity', 0.0041246760417231925), ('!', 0.004074139176195159), ('serious', 0.003951266828294047), ('irritate', 0.0039201189014883295), ('depressing', 0.0038876961827916086), ('fury', 0.003870328269874014), ('gloomy', 0.003770737681483579), ('snap', 0.0037101804873577635), ('elate', 0.003466093513679562), ('blue', 0.0034636493388979133), ('delight', 0.003428385069574441), ('sink', 0.003417205474022183), ('furious', 0.0034166231046178553), ('afraid', 0.0033947932208909155), ('i', 0.00337713808708801), ('be', 0.003350334395094895), ('love', 0.003345397950668639), ('@user', 0.0033416787606159286), ('.', 0.00324190450335759), ('insult', 0.003235585131642308), ('jquery', 0.0032104393090275456), ('breezy', 0.0031925870617481784), ('the', 0.0031879358357217924), ('a', 0.0031761240630780304), ('offense', 0.0031555056272114914), ('rag', 0.0031153852076324256), ('playful', 0.0030955315462441783), (',', 0.0030704382838423814), ('boil', 0.0030555554442712315), ('dull', 0.003052435031484199), ('to', 0.0029601829325204736), ('pout', 0.002942695339560416), ('wrath', 0.0029373854178695556), ('discourage', 0.002902376479552496), ('hearty', 0.0028011213258613667), ('sober', 0.00279244007044428), ('weary', 0.0025935386939436697), ('of', 0.002592979658381157), ('joyful', 0.0025840676391645544), ('and', 0.002565306745320089), ('you', 0.0025648513860357996), ('mourn', 0.002530805943387026), ('cheery', 0.0025191962306917283), ('offended', 0.002517442983454494), ('alarm', 0.002512537633289446), ('joyous', 0.002511013968046642), ('my', 0.0025019478742321416), ('shake', 0.0024440713707021664), ('despair', 0.0024115668220762846), ('live.ly', 0.0024057126472765214), ('have', 0.0024003814204620333), ('resent', 0.0023529772748665293), ('rabid', 0.0023501801203658816), ('gloom', 0.0023413303988226164), ('me', 0.002313947543374867), ('safeway', 0.0023130143733941016), ('grudge', 0.002300211006768252), ('that', 0.002298221923167272), ('burn', 0.0022763739554907915), ('exhilarate', 0.0022507897130712726), ('sting', 0.002239868457755952), ('cheerfully', 0.002239413323189711), ('not', 0.002237458594485813), ('cheerful', 0.002227726736480297), ('?', 0.002204873308863705), ('bright', 0.002130633754569766), ('just', 0.002127866403516446), ('it', 0.002065052782511938), ('dreadful', 0.002022596111720887), ('broadcast', 0.0020081061920867024), ('in', 0.0019703485946890773), ('relentless', 0.0019678296685628244), ('stay', 0.001966129072588891), ('worry', 0.0019234898269848556), ('for', 0.001920071176393744), ('madden', 0.0019031319515784785), ('if', 0.0018660811000317471), ('dread', 0.0018658916264334149), ('horrific', 0.001865630662048559), ('huff', 0.001850154048958005), ('watch', 0.0018426299496423697), ('shy', 0.0018354545169278077), ('amazing', 0.0018283342516035622), ('when', 0.0018212264187485241), ('with', 0.0017958353054700724), ('get', 0.0017900555885510077), ('musically', 0.0017223570877634726), ('burst', 0.0017014904647012178), ('at', 0.0016955157964398178), ('restless', 0.0016947399836070233), ('museum', 0.001681005778968916), (\"i'm\", 0.0016554061316305696), ('on', 0.0016452324418249902), (':', 0.0016219835600511275), ('chirp', 0.0016059635788899691), (\"'\", 0.0015999322754884355), ('do', 0.0015974382815208874), ('smiling', 0.001577735847136307), ('so', 0.001547358104218881), ('this', 0.0015408134582097675), ('scare', 0.0015293491198461408), ('what', 0.0015289493605473168), ('sunk', 0.0015199902618718056), ('people', 0.0014901443649319066), ('threaten', 0.001484865893639518), ('out', 0.0014775335539417768), ('sulk', 0.001458533001905749), ('lose', 0.0014414313176342648), ('but', 0.0014362308885658933), ('life', 0.00142435318204719), ('day', 0.001422592157840436), (\"it's\", 0.0014213303357619679), ('fiery', 0.001421266248514905), ('like', 0.0014102759391348106), ('feel', 0.0013688427507185569), ('go', 0.0013390725864678025), ('make', 0.0013340206993988789), ('shock', 0.001328612996686416), ('no', 0.0013268746958710385), ('as', 0.0013244313895621042), ('all', 0.001296871143927908), ('bad', 0.0012759243862858374), ('up', 0.0012638144749851496), ('now', 0.00125579490333958), ('heyday', 0.0012542608356470313), ('about', 0.0012502093091555188), ('-', 0.0012291362303360177), ('good', 0.0012109480026760524), ('your', 0.0011986324834099666), ('horrid', 0.0011949574247856825), ('exhilaration', 0.001193163529442646), ('nike', 0.0011920131760109834), ('dismal', 0.0011851330863053597), ('api', 0.0011816028359523498), ('an', 0.0011723794252770658), ('always', 0.0011462837608426889), ('eat', 0.0011430510526348923), (\"don't\", 0.001140326457889771), ('will', 0.001131227924855295), ('&', 0.001128127463953793), ('frown', 0.001119022008359462), ('know', 0.0010984710862990085), ('pakistan', 0.001093965514843026), ('from', 0.0010908148139034928), ('he', 0.001087548285898612), ('jovial', 0.0010765777135445238), ('..', 0.0010534402299862337), ('concern', 0.0010514204863981695), ('please', 0.0010513927285469911), ('start', 0.0010498450658268915), ('we', 0.001042729397108923), ('animated', 0.0010313343335452275), ('her', 0.0010303745632134734), ('new', 0.0010262085075860863), ('pine', 0.0010132806641486927), ('give', 0.0010125612992013657), ('how', 0.001010900559373173), ('can', 0.0010102097794227668), ('or', 0.0010007499822586557), ('resentment', 0.000994104349074446), ('😂', 0.0009935808548737625), ('provoke', 0.0009910828062711968), ('sparkle', 0.0009870265849860867), ('bb18', 0.0009721350067083696), ('mirth', 0.0009693483603560995), ('gm', 0.0009491096341538897), ('san', 0.0009485578369648792), ('u', 0.0009474622298668609), ('our', 0.0009439281497760629), ('should', 0.0009330038010633585), ('irritation', 0.0009271192310792754), ('melancholy', 0.0009257504069294641), ('too', 0.0009193225653938966), ('some', 0.000918589637774397), ('shudder', 0.0009104310577052156), ('off', 0.0009101934627655305), (\"that's\", 0.0009099234273462792), ('would', 0.00090685393694656), ('today', 0.0009017224748501319), ('by', 0.0008979618365965721), ('great', 0.0008926137399884583), ('wonderful', 0.0008849131590712132), ('grieve', 0.000882319195908734), ('attack', 0.0008693685964259554), ('see', 0.0008689608644297875), ('pessimist', 0.0008684767476148175), ('😤', 0.0008619602351219979), ('cheering', 0.0008605867335888344), ('time', 0.000852019401588052), ('there', 0.0008500903124256392), ('why', 0.0008488708565114885), ('lol', 0.0008402247343269993), ('victory', 0.0008393334958469112), ('look', 0.000836686395366415), ('who', 0.0008316618954951959), ('never', 0.0008159263439086314), ('irate', 0.0008026695304716195), ('eating', 0.0007936400078442432), (\"can't\", 0.0007858756660186865), ('2', 0.0007836399411907403), ('last', 0.0007832413236670593), (\"i've\", 0.0007799111817992641), ('stanford', 0.0007797279545569147), ('think', 0.0007744954515655637), ('exhilarating', 0.0007724670429060187), ('baseball', 0.0007675896343074589), ('then', 0.0007658686946462563), ('fuck', 0.0007651594832520028), ('beautiful', 0.0007646516049388448), ('still', 0.0007622567563626179), ('play', 0.000752761483896167), ('work', 0.0007525604601155832), ('them', 0.0007451404453212301), ('more', 0.0007449270206631132), ('him', 0.0007440443220928488), ('want', 0.0007412706214184552), ('trump', 0.0007401090166108512), ('intimidate', 0.00073865057538825), ('say', 0.0007300433419523204), ('come', 0.0007281207970262153), ('/', 0.0007250720512098875), ('thank', 0.0007246613994491774), ('google', 0.0007222356194741221), ('kill', 0.000715524865110509), ('twitter', 0.0007150305804038053), ('man', 0.0007143009453650062), ('terrorist', 0.0007113008015666771), ('they', 0.000710043745034296), ('tell', 0.0007074863707921352), ('sparkling', 0.00070662209705712), ('face', 0.0007058539923579753), ('very', 0.0007024381358332623), ('affliction', 0.0006999843512428503), ('fret', 0.0006951920203941064), ('again', 0.0006915249171107202), ('burning', 0.0006833298694472895), ('haunt', 0.0006692568587762875), ('hard', 0.0006659151547978132), ('country', 0.0006658608910409587), ('dash', 0.0006639955462665185), ('well', 0.0006621175963496056), ('❤', 0.0006607852098364101), ('talk', 0.0006514020553887846), ('let', 0.0006472873421758939), ('pick', 0.000643698371239592), ('tantrum', 0.0006407236836734944), ('hell', 0.0006267435313761193), ('because', 0.0006241818257294595), ('keep', 0.0006235558307811682), ('awe', 0.0006230532041712931), ('us', 0.0006195491223258004), ('dreary', 0.0006195288511042259), ('first', 0.0006178883286865907), ('animate', 0.0006159253472773031), ('even', 0.0006128316206811355), ('their', 0.0006097091412068077), ('god', 0.0006073405021576226), ('away', 0.0006042704708605454), ('happen', 0.0006042579728922716), ('lot', 0.0006033786714244821), ('tomorrow', 0.0006023172541503982), ('little', 0.0006002902940374022), ('infuriate', 0.0005955941599419619), ('his', 0.0005923838674041476), ('levity', 0.0005900375203777534), (\"didn't\", 0.0005890076515898578), ('stop', 0.0005886242518102624), ('one', 0.0005865462415135316), ('only', 0.0005805986363706776), ('cause', 0.0005794755370400604), ('provocation', 0.0005784172071151559), ('help', 0.0005777493767194931), ('way', 0.000577688374007025), ('black', 0.0005739724181062764), ('while', 0.0005730626033174694), ('accept', 0.0005704572307243097), ('back', 0.0005701240382981629), ('dentist', 0.000566099129710461), ('big', 0.0005626702196740415), ('over', 0.0005572567623530079), ('take', 0.0005568984170713951), ('tiff', 0.0005532065665048346), ('bless', 0.0005478073394065843), ('blood', 0.0005461028903005563), (\"you're\", 0.0005402642354544949), ('laugh', 0.0005402304378968025), ('girl', 0.0005337743215988516), ('find', 0.000532224374053586), ('️', 0.0005304184515819387), ('happiness', 0.0005278822797354106), ('leave', 0.0005246517387510823), ('china', 0.0005235562147525739), ('try', 0.0005231639354937985), ('ask', 0.0005211523445012538), ('juggle', 0.0005188060437837569), ('best', 0.0005181741681968287), ('morning', 0.0005170844890380686), ('guy', 0.0005164472673327682), ('aggravate', 0.0005110640165873995), ('cheerfulness', 0.0005108936569747352), ('before', 0.0005087470242361113), ('yeah', 0.0005070789356872733), ('most', 0.0005066703407361724), (\"i'll\", 0.0005052680469820949), ('raging', 0.0005043137796645603), ('india', 0.000503306140793674), (')', 0.0005031548741473602), ('deject', 0.0005022568332839841), ('through', 0.0005005484579094038), ('die', 0.0004932352849698404), ('(', 0.0004922838714899586), ('nothing', 0.0004915760246090627), ('scary', 0.0004905341716757009), ('😩', 0.000490249126668196), ('really', 0.0004896309507953468), ('after', 0.00048663336082788633), (\"he's\", 0.0004854031026246274), ('week', 0.00047916085488726386), ('until', 0.0004781335548154457), ('😭', 0.0004764304608037209), ('than', 0.00047610756425924455), ('use', 0.00047549944399947195), ('down', 0.00047329958825284275), ('every', 0.00047158894604517547), ('nicole', 0.00047067194831980754), ('right', 0.00046968738295328285), ('much', 0.00046895171294353147), ('wanna', 0.0004686024898560178), ('im', 0.00046786822717696276), ('year', 0.00046770351152444973), ('challenge', 0.00045994832573656425), ('any', 0.0004597286449670255), ('thing', 0.0004596382147001608), ('sometimes', 0.0004589281759705095), ('aesthetically', 0.0004529353238358601), ('fucking', 0.00045268052555518174), ('honestly', 0.00045092510209011275), ('customer', 0.0004504949582240854), ('oh', 0.0004471951887603817), ('could', 0.00044536179107332145), ('ever', 0.00044241466931592405), ('need', 0.0004413295952758325), ('sleep', 0.0004393047934512187), ('call', 0.00043747817819478574), ('someone', 0.00043563315254901687), ('those', 0.00043474564169962323), ('change', 0.00043414615811223956), ('shit', 0.0004293957765921522), ('bring', 0.0004245345515507407), ('sure', 0.00042338939320213533), ('anyone', 0.0004228784704751437), ('wait', 0.000422454887273478), ('fight', 0.0004223077729073554), ('sweet', 0.000420276298558735), ('afternoon', 0.0004173210581395003), ('hold', 0.00041708776345401904), ('here', 0.0004167966360112715), ('displeasure', 0.00041573522511891714), ('sorry', 0.0004135421360178526), ('these', 0.0004114669287402409), ('alone', 0.00041085670263335945), ('game', 0.0004100103460128025), ('join', 0.00040949860046780195), ('mean', 0.0004091329660204053), ('heart', 0.0004047172635406605), ('its', 0.0004045576778492534), ('same', 0.00039992957182818), ('movie', 0.00039905399209720996), (\"doesn't\", 0.00039700905843949064), ('seriously', 0.00039437102552166315), ('dream', 0.00039211500634156774), ('something', 0.0003921043934170327), (' ', 0.00039192725779308715), ('world', 0.00039145866988118136), ('end', 0.00038848977440620735), ('tonight', 0.0003882021936875867), (\"isn't\", 0.0003881531730242288), ('wake', 0.0003871586033380788), ('place', 0.0003862969418473549), ('bill', 0.0003847257399356362), ('goddamn', 0.0003815481087001871), ('move', 0.0003801782003810766), ('mhchat', 0.00037997776431884794), ('dire', 0.0003797973415850486), ('islam', 0.00037898076821072085), ('news', 0.00037838502767814343), ('francisco', 0.00037605167146792767), ('put', 0.00037557374761014467), ('mope', 0.000373959288608304), ('shiver', 0.00037387072845958513), (':)', 0.00037057848015922704), ('arrive', 0.0003696278423787252), ('video', 0.00036746902671222205), ('tweet', 0.0003661562691511856), ('coupon', 0.000365040593574675), ('next', 0.00036107732594352175), ('6', 0.00036083793296752125), ('+', 0.0003607833974622862), ('yet', 0.00035984690585891954), ('hope', 0.00035871068003815614), ('inconsolable', 0.0003559750914054186), ('blame', 0.0003551744187657984), ('house', 0.00035325200089726986), ('money', 0.00035002150699972085), ('truth', 0.0003493474671237431), ('hand', 0.00034760046162727644), ('sleepy', 0.0003472564970913882), ('rejoicing', 0.0003468447625475104), ('scar', 0.00034424124954690437), ('soul', 0.00034368200622772026), ('joy', 0.00034267970065592437), ('everyone', 0.0003424782981821726), ('damn', 0.00034153554660184946), ('thanks', 0.00034075056047696607), ('cool', 0.0003403119192629229), ('may', 0.0003395272058036874), ('murky', 0.0003388759512823867), ('write', 0.0003380389116356683), ('high', 0.0003370755242060488), ('fire', 0.0003361694995230271), ('straight', 0.00033421016286935276), ('own', 0.00033346623537807606), ('such', 0.00032971872057855625), (\"we're\", 0.0003294858479791252), ('ugh', 0.00032864606564376883), ('long', 0.0003263065621267362), ('sedate', 0.00032605267064926834), ('update', 0.0003241008051584096), ('around', 0.00032386756492474287), ('understand', 0.0003234288107089087), ('somber', 0.00032272672255155885), ('once', 0.00032212125137444305), ('season', 0.000320192202820814), ('ya', 0.0003198218125331502), ('felt', 0.0003196857365557634), ('maybe', 0.00031685653927834856), ('phone', 0.0003136546698032998), ('which', 0.000313651151834791), ('s', 0.00031343588562393756), ('fun', 0.0003131509183421274), ('full', 0.0003123661070601819), ('bc', 0.000312263314285032), ('animosity', 0.0003119742066223532), ('is', 0.0003118201568978494), ('age', 0.00031052888698890286), ('show', 0.0003098611524013367), ('night', 0.00030981015193005436), ('💙', 0.0003093408163060392), ('win', 0.0003085320432314369), ('state', 0.00030792347839104855), ('funny', 0.00030765622397528944), ('into', 0.00030736232782497164), (\"aren't\", 0.0003066308087806042), ('food', 0.0003065933356996138), ('month', 0.00030633099623291556), ('rt', 0.0003062390474642296), ('amaze', 0.0003061624600593527), ('turn', 0.00030540576206305304), ('pure', 0.00030524685573420605), ('she', 0.0003049756510132231), ('blog', 0.0003032838559393898), ('clown', 0.00030298879843124215), ('hate', 0.00030180731242017195), ('despondent', 0.0003011650417192456), ('become', 0.00030036789776366403), ('major', 0.00030021456383437354), ('others', 0.00030003063249301593)]\n"
     ]
    }
   ],
   "source": [
    "X = featured_dataset['valences'].values.tolist()\n",
    "y = featured_dataset['class'].values\n",
    "y[y == 'neutral'] = 'n'\n",
    "y[y == 'fear'] = 'f'\n",
    "y[y == 'anger'] = 'a'\n",
    "y[y == 'sadness'] = 's'\n",
    "y[y == 'joy'] = 'j'\n",
    "selected, mask = feature_selection(X, y, vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id class                                             lemmas  \\\n",
      "0  1000001     n  [Check, this, video, out, -, -, President, Oba...   \n",
      "1  1000002     n  [need, suggestion, for, a, good, IR, filter, f...   \n",
      "2  1000003     n  [@user, :, I, just, check, my, google, for, my...   \n",
      "3  1000004     n  [be, in, San, Francisco, at, Bay, to, Breakers...   \n",
      "4  1000005     n                   [just, land, at, San, Francisco]   \n",
      "5  1000006     n     [San, Francisco, today, ., Any, suggestion, ?]   \n",
      "6  1000007     n  [On, my, way, to, see, Star, Trek, @, The, Esq...   \n",
      "7  1000008     n  [Going, to, see, star, trek, soon, with, my, d...   \n",
      "8  1000009     n  [Bill, Simmons, in, conversation, with, Malcol...   \n",
      "9  1000010     n         [play, with, cURL, and, the, Twitter, API]   \n",
      "\n",
      "                                            valences  \n",
      "0  [-1.0, -1.0, -1.0, 10.0, -1.0, -1.0, -1.0, -1....  \n",
      "1  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "2  [-1.0, 10.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "3  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "4  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "5  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "6  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "7  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "8  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n",
      "9  [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1....  \n"
     ]
    }
   ],
   "source": [
    "for index, row in featured_dataset.iterrows():\n",
    "    valences = np.array(row.valences[mask])\n",
    "    featured_dataset.set_value(index=index, col='valences', value=valences)\n",
    "print(featured_dataset.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5348, 507)\n",
      "   v_0   v_1  v_2   v_3  v_4  v_5  v_6  v_7   v_8   v_9   ...     v_498  \\\n",
      "0 -1.0  -1.0 -1.0  10.0 -1.0 -1.0 -1.0 -1.0  10.0  -1.0   ...      -1.0   \n",
      "1 -1.0  -1.0 -1.0  -1.0 -1.0 -1.0 -1.0 -1.0  -1.0  10.0   ...      -1.0   \n",
      "2 -1.0  10.0 -1.0  -1.0 -1.0 -1.0 -1.0 -1.0  10.0  10.0   ...      -1.0   \n",
      "3 -1.0  -1.0 -1.0  -1.0 -1.0 -1.0 -1.0 -1.0  -1.0  10.0   ...      -1.0   \n",
      "4 -1.0  -1.0 -1.0  -1.0 -1.0 -1.0 -1.0 -1.0  -1.0  -1.0   ...      -1.0   \n",
      "\n",
      "   v_499  v_500  v_501  v_502  v_503  v_504  v_505  v_506  emotion  \n",
      "0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0        n  \n",
      "1   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0        n  \n",
      "2   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0        n  \n",
      "3   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0        n  \n",
      "4   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0   -1.0        n  \n",
      "\n",
      "[5 rows x 508 columns]\n"
     ]
    }
   ],
   "source": [
    "temp = pd.DataFrame(np.vstack(featured_dataset.valences.values))\n",
    "print(np.vstack(featured_dataset.valences.values).shape)\n",
    "temp.columns = ['v_' + str(i) for i in range(len(selected))]\n",
    "temp['emotion'] = featured_dataset['class'].values\n",
    "print(temp.head(5))\n",
    "temp.to_csv('data_final/features_emotion_detection_w2v.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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