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tandon-aman/ElectionResults11March.ipynb

Last active February 10, 2018 17:15
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ElectionResults11March.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from spacy.en import English\n",
"import codecs\n",
"import re\n",
"import os\n",
"import unidecode"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"parser = English()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting tweets from elasticsearch"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 1000\n",
"scroll size: 234\n",
"scroll size: 0\n"
]
}
],
"source": [
"tweets = []\n",
"from elasticsearch import Elasticsearch\n",
"es = Elasticsearch()\n",
"res = es.search(index=\"elections\", doc_type=\"statuses\", scroll='1m',\n",
" size=1000, \n",
" body={\"query\": {\n",
" \"match_all\": {}\n",
" }\n",
" }\n",
" )\n",
"sid = res['_scroll_id']\n",
"scroll_size = res['hits']['total']\n",
"while (scroll_size > 0):\n",
" res = es.scroll(scroll_id = sid, scroll = '1m')\n",
" sid = res['_scroll_id']\n",
" for doc in res['hits']['hits']:\n",
" tweets.append(doc['_source']['text'])\n",
" scroll_size = len(res['hits']['hits'])\n",
" print \"scroll size: \" + str(scroll_size)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24234"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(tweets)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Trying out word similarity"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy import dot\n",
"from numpy.linalg import norm\n",
"\n",
"# you can access known words from the parser's vocabulary\n",
"nasa = parser.vocab[u\"computer\"]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"allWords = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != \"nasa\"})"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 most similar words to NASA:\n",
"computer\n",
"computers\n",
"workstation\n",
"mainframe\n",
"laptop\n",
"microcomputer\n",
"pc\n",
"microprocessor\n",
"microcontroller\n",
"pda\n"
]
}
],
"source": [
"# sort by similarity to NASA\n",
"allWords.sort(key=lambda w: cosine(w.vector, nasa.vector))\n",
"allWords.reverse()\n",
"print(\"Top 10 most similar words to NASA:\")\n",
"for word in allWords[:10]: \n",
" print(word.orth_)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import langdetect\n",
"\n",
"def retriveEnglistSentenceOnly(corpus):\n",
" english_sentence = []\n",
" for sentence in corpus:\n",
" try:\n",
" if langdetect.detect(sentence) == 'en':\n",
" english_sentence.append(sentence)\n",
" except:\n",
" english_sentence.append(sentence)\n",
" return english_sentence"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3min 43s, sys: 10.8 s, total: 3min 54s\n",
"Wall time: 3min 57s\n"
]
}
],
"source": [
"%%time\n",
"tweets = retriveEnglistSentenceOnly(tweets)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating the tweets file"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tweets_filepath = os.path.join('tweets_all.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Saving all the tweets in file"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3 s, sys: 180 ms, total: 3.18 s\n",
"Wall time: 3.53 s\n"
]
}
],
"source": [
"%%time\n",
"with codecs.open(tweets_filepath, 'w', encoding='utf_8') as f:\n",
" for tweet in tweets:\n",
" #print \"Orig: \"+tweet\n",
" #print 'Orig New '+unidecode.unidecode(tweet)\n",
" tweet = unidecode.unidecode(tweet)\n",
" #removing out the Retweet word, @abc, url and hash from hashtag\n",
" tweet = re.sub(r\"http\\S+\", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = re.sub(r\"#\", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = re.sub(r\"@\", \" \", tweet,flags=re.IGNORECASE)#|RT|@\\S+|-|'|%|\\'|\\(|\\)|;|:| |'s|’|\\\"|&amp|’s|\\U000\\S+\n",
" tweet = re.sub(r\"RT\", \" \", tweet,flags=re.IGNORECASE)\n",
" #tweet = re.sub(r\"@\\S+\", \" \", tweet,flags=re.IGNORECASE)\n",
" #for removing the >\n",
" tweet = re.sub(r\"&\\S+\", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = re.sub(r\"-\", \"\",tweet)\n",
" tweet = re.sub(r\" \", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = re.sub(r\"%\", \" \",tweet)\n",
" tweet = re.sub(r\"'s\", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = re.sub(r\"\\'\", \"\",tweet)\n",
" tweet = re.sub(r\"\\\\\", \"\",tweet)\n",
" tweet = re.sub(r\"/\", \"\",tweet)\n",
" tweet = re.sub(r\"\\(\", \" \",tweet)\n",
" tweet = re.sub(r\"\\)\", \" \",tweet)\n",
" tweet = re.sub(r\";\", \" \",tweet)\n",
" tweet = re.sub(r\":\", \" \",tweet)\n",
" tweet = re.sub(r\"[.]{2,}\",\" \", tweet)\n",
" tweet = re.sub(r\"\\\"\", \" \",tweet)\n",
" tweet = re.sub(r\"<\", \" \",tweet)\n",
" tweet = re.sub(r\">\", \" \",tweet)\n",
" tweet = re.sub(r\"\\[\", \" \",tweet)\n",
" tweet = re.sub(r\"\\]\", \" \",tweet)\n",
" tweet = re.sub(r\"\\{\", \" \",tweet)\n",
" tweet = re.sub(r\"\\}\", \" \",tweet)\n",
" tweet = re.sub(r\"\\|\", \" \",tweet)\n",
" tweet = re.sub(r\"=\", \" \",tweet)\n",
" tweet = re.sub(r\"~\", \" \",tweet)\n",
" tweet = re.sub(r\"\\`\", \" \",tweet)\n",
" tweet = re.sub(r\"\\^\", \" \",tweet)\n",
" tweet = re.sub(r\"\\+\", \" \",tweet)\n",
" tweet = re.sub(r\"!\", \" \",tweet)\n",
" tweet = re.sub(r\"\\*\", \" \",tweet)\n",
" \n",
" tweet = re.sub(r\" [a-z0-9] \", \" \",tweet)\n",
" # tweet = re.sub(r\"^[a-z0-9] \", \" \",tweet)\n",
" # tweet = re.sub(r\" [a-z0-9]$\", \" \",tweet)\n",
" #tweet = re.sub(r\"[a-z]\", \"\",tweet)\n",
" tweet = re.sub(r\"&amp\", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = re.sub(r\"\\\\U\\S+\", \" \", tweet,flags=re.IGNORECASE)\n",
" tweet = \" \".join(tweet.split())\n",
" #print \"Axed: \"+tweet\n",
" f.write(tweet+'\\n')"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23873"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(tweets)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Applying the tokenization & stemming ad again saving in some file"
]
},
{
"cell_type": "code",
"execution_count": 357,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def punct_space_stopword(token):\n",
" \"\"\"\n",
" helper function to eliminate tokens\n",
" that are pure punctuation or whitespace\n",
" \"\"\"\n",
" \n",
" return token.is_punct or token.is_space or token.is_stop or (len(str(token.lemma)) <= 2)\n",
"\n",
"def line_tweet(filename):\n",
" \"\"\"\n",
" generator function to read in tweets from the file\n",
" and un-escape the original line breaks in the text\n",
" \"\"\"\n",
" \n",
" with codecs.open(filename, encoding='utf_8') as f:\n",
" for tweet in f:\n",
" yield tweet.replace('\\\\n', '\\n')\n",
" \n",
"def lemmatized_sentence_corpus(filename):\n",
" \"\"\"\n",
" generator function to use spaCy to parse tweet,\n",
" lemmatize the text, and yield sentences\n",
" \"\"\"\n",
" \n",
" for parsed_tweet in parser.pipe(line_tweet(filename),\n",
" batch_size=10000, n_threads=4):\n",
" \n",
" for sent in parsed_tweet.sents:\n",
" yield u' '.join([token.lemma_ for token in sent\n",
" if not punct_space_stopword(token)])"
]
},
{
"cell_type": "code",
"execution_count": 358,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from gensim.models import Phrases\n",
"from gensim.models.word2vec import LineSentence\n",
"import pandas as pd\n",
"import itertools as it"
]
},
{
"cell_type": "code",
"execution_count": 359,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"unigram_sentences_filepath = os.path.join('unigram_sentences_all.txt')"
]
},
{
"cell_type": "code",
"execution_count": 360,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 24.5 s, sys: 857 ms, total: 25.4 s\n",
"Wall time: 26.2 s\n"
]
}
],
"source": [
"%%time\n",
"with codecs.open(unigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
" for sentence in lemmatized_sentence_corpus(tweets_filepath):\n",
" f.write(sentence + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 361,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unigram_sentences = LineSentence(unigram_sentences_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"thnk india not need rg anymore dear cong choose leader wisely person electionresults\n",
"\n",
"bjp 260 sp 73 bsp 36 pun cong 62 akali 26 aap 22 ukhnd bjp 57 cong 09 goa cong 9 bjp 7 4 electionresults\n",
"\n",
"credit go narendramodi sir amitshah sir feel happy proud electionresults\n",
"\n",
"guess donkin donut outlet jhasanjay electionresults\n",
"\n",
"harry_steven\n",
"\n",
"close early race goa\n",
"\n",
"clear\n",
"\n",
"watch space\n",
"\n",
"electionresults httweets\n",
"\n",
"feel sorry people make fun delhite vote aap. electionresults\n",
"\n"
]
}
],
"source": [
"for unigram_sentence in it.islice(unigram_sentences, 200, 210):\n",
" print u' '.join(unigram_sentence)\n",
" print u''"
]
},
{
"cell_type": "code",
"execution_count": 363,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ArvindKejriwal and together they will make you history. ElectionResults\n",
"\n",
"And BJP4India poised at 300 seats in UP. What phenomenal win Some of us saw it, some refused to sagarikaghose ElectionResults\n",
"\n",
"This is called perfect planning and better execution in marketing terms. ElectionResults BJP4India did it again.\n",
"\n",
"doubtinggaurav Not fan of Yogi Adityanath but preferable to Dev_Fadnavis like dhimmi ElectionResults\n",
"\n",
"Congress may still be able to keep afloat in the national political scene but for how long ElectionResults\n",
"\n",
"Now INCIndia will sta bragging about Punjab and Goa after losing badly in UP. ElectionResults\n",
"\n",
"Hi AAPstrd Anyone Alive In Twitter ? ElectionResults PunjabElection2017\n",
"\n",
"SickularLibtard BJP Wins EVM Scam BJP Looses Demonetization Referendum. ElectionResults SattaKaGulaal sardanarohit\n",
"\n",
"ichiragpaswan Finaly corruption nepotism will be eliminated. Time has come for uttar pradesh to be Uttam pradesh. ElectionResults\n",
"\n",
"nihar2610 Voters of rest of India sma er than voters of Delhi. ElectionResults\n",
"\n"
]
}
],
"source": [
"sntnc = LineSentence(tweets_filepath)\n",
"\n",
"for unigram_sentence in it.islice(sntnc, 200, 210):\n",
" print u' '.join(unigram_sentence)\n",
" print u''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating Bigram Model "
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bigram_model_filepath = os.path.join('bigram_model_all')"
]
},
{
"cell_type": "code",
"execution_count": 365,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram_model = Phrases(unigram_sentences)\n",
"bigram_model.save(bigram_model_filepath)\n",
"\n",
"#loading the model\n",
"bigram_model = Phrases.load(bigram_model_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 366,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram_sentences_filepath = os.path.join('bigram_sentences_all.txt')"
]
},
{
"cell_type": "code",
"execution_count": 367,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.59 s, sys: 130 ms, total: 1.72 s\n",
"Wall time: 1.77 s\n"
]
}
],
"source": [
"%%time\n",
"with codecs.open(bigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
" for unigram_sentence in unigram_sentences: \n",
" bigram_sentence = u' '.join(bigram_model[unigram_sentence])\n",
" f.write(bigram_sentence + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 368,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hop change\n",
"\n",
"man day stamp ballot_paper toh evm pr dikhta hi nhi saala vote kahan gya\n",
"\n",
"electionresults\n",
"\n",
"great win bjp development speaks bjp4india electionresults election2017\n",
"\n",
"pm_modi capable fight arvind_kejriwal\n",
"\n",
"capt_amarinder singh man. electionresults punjabelection2017\n",
"\n",
"zeenews uttarakhand electionresults 2017_live harish_rawat lose_haridwar rural bjp head_massive win\n",
"\n",
"aap berozgars catchnews predict aap victory delhi municipal election reference exit_poll beware electionresults\n",
"\n",
"56_rocks feel_proud share electionresults elections2017 uttar_pradesh storieswelove\n",
"\n",
"vishnuksaboo see electionresults officeofrg_yadavakhilesh mayawati vote bjp4india sattakagulaal modivanibh\n",
"\n",
"jokeoftheday cong win punjabelection2017 officeofrg lol rahulgandhi believe electionresults\n",
"\n",
"today headline news_channel like electionresults elections2017 punjabelection2017 ut\n",
"\n",
"bjp need think abt dere alliance wid akali_dal asap clearly nt work dey need maharashtra way pu\n",
"\n",
"officeofrg_yadavakhilesh face guy deserve lose\n",
"\n",
"communal politics se desh nahi jeeta jaata\n",
"\n",
"kyaukhaadlega scene et office watch electionresults\n",
"\n",
"big defeat prashant_kishore\n",
"\n",
"electionresults modi congress\n",
"\n",
"akshayraina1\n",
"\n",
"kish92\n",
"\n",
"srinidhi_gk shivangi_dkl\n",
"\n",
"uttarkhand cm lose seat contest\n",
"\n",
"electionresults spineless candidate hoga people\n",
"\n",
"cnnnews18_resultswithnews18\n",
"\n",
"victory people_faith believe navjot_singh sidhu electionresults\n",
"\n",
"virendersehwag_karun126 nair bjp join_300 club\n",
"\n",
"electionresults elections2017\n",
"\n",
"sidhu careful attack bjp modi\n",
"\n",
"keep door open\n",
"\n",
"electionresults\n",
"\n",
"priyaakulkarni2 sattakagulaal electionresults cycle_puncture vanvaas\n",
"\n",
"victory bjp4up triumph\n",
"\n",
"narendramodi_amitshah\n",
"\n",
"historic_day kejriwal reject\n",
"\n",
"electionresults elections2017 punjab\n",
"\n",
"lol not thing ur voter repent voting\n",
"\n",
"electionresults\n",
"\n",
"navjot sidhu evade reply want dy cm punjab electionresults httweets elections2017\n",
"\n",
"mr_lolwa electionresults\n",
"\n",
"learning elections2017 punjabelection2017 uttarpradesh bjp sp aap congress electionresults\n",
"\n",
"narendramodi effect obviously fact fact electionresults\n",
"\n",
"muh_pe_bolunga\n",
"\n",
"voter sp_bsp aap congress electionresults\n",
"\n",
"pretikakhanna aap cm_candidate ielvisgomes trail 4th position_cuncolim constituency goa livemint electionresults\n",
"\n",
"swarupphd goa_manipur result confuse\n",
"\n",
"sure ahead\n",
"\n",
"medium tally diff eci result electionresults\n",
"\n",
"time grab double shot espresso turn wifi router\n",
"\n",
"electionresults\n",
"\n",
"21century_kant bjp headqua\n",
"\n",
"er right electionresults results2017\n",
"\n",
"arvindkejriwal come tweet\n",
"\n",
"leader bold speech time failure\n",
"\n",
"electionresults\n",
"\n",
"financialxpress electionresults cm_harishrawat lose election haridwar_rural uttarakhand\n",
"\n",
"bjp suppo\n",
"\n",
"er state rule bjp electionresults\n",
"\n",
"election_result congress like electionresults elections2017 punjabelection2017 manipur\n",
"\n",
"electionresults arvindkejriwal leave gujarat\n",
"\n",
"electionresults tiebreaker trend incindia bjp4india ec show 88 manipur\n",
"\n",
"livemint\n",
"\n",
"electionresults elections2017 people manipur suppo_ed ilom sharmilla arvindkejriwal rally\n",
"\n",
"jokes_apa\n",
"\n",
"let_moment thank_rahul gandhi_ente aining_election campaign\n",
"\n",
"bjplivenews lucknow celebration bjp office huge_victory assembly_election 2017\n",
"\n",
"electionresults\n",
"\n",
"goa\n",
"\n",
"sure great poker\n",
"\n",
"electionresults\n",
"\n",
"belike_jammie bjp star_campaigner\n",
"\n",
"electionresults\n",
"\n",
"56_rocks feel_proud share electionresults elections2017 uttar_pradesh\n",
"\n",
"lal_krishna advani_happy man today\n",
"\n",
"hope india president\n",
"\n",
"electionresults\n",
"\n",
"wittyfeedlive electionresults punjabelection2017 pa_ie lead 116117 sad bjp 18 congress 75 aap 20 03\n",
"\n",
"electionresults hand pappu take cycle akhilesh hit elephant mayavati get drown pond lotus\n",
"\n",
"weldon congress punjab electionresults punjabelection2017 election2017\n",
"\n",
"repubiicofindia good_bye appeasement hello democracy upwinsbjp electionresults\n",
"\n",
"nickhunterr ohh behencho can_not stop laugh\n",
"\n",
"electionresults\n",
"\n",
"clearly divisive force defeat\n",
"\n",
"politics base development nt\n",
"\n",
"electionresults\n",
"\n",
"pura bjp voting development electionresults elections2017\n",
"\n",
"thechiraggrover electionresults\n",
"\n",
"way bjp4india\n",
"\n",
"uttar_pradesh\n",
"\n",
"uniball_india win\n",
"\n",
"win electionresults win gift hampers uniball india contestale\n",
"\n",
"chanakya bjp4india prove mettle kingdom new_height electionresults\n",
"\n",
"people not vote narendramodi .rahul gandhi\n",
"\n",
"storieswelove electionresults\n",
"\n",
"mobile friendly interactive election electionresults upelection2017\n",
"\n",
"11.20 trend goaelection2017 result live_updates\n",
"\n",
"congratulation people bjp reject castebased politics completely\n",
"\n",
"electionresults\n",
"\n",
"tejasyagnik electionresults surgicalstrike bjp win_deoband\n",
"\n",
"muslim concentrated seat amitshah narendramodi bjp4india\n",
"\n",
"result come check lead uttar_pradesh bingelectionslive electionresults\n",
"\n",
"vivek16_ 403 304 mirror image number\n",
"\n",
"victory bjp4india congratulation\n",
"\n",
"electionresults elections2017\n",
"\n",
"bindu_5 beginning modi era electionresults\n",
"\n",
"democracy get strong\n",
"\n",
"electionresults\n",
"\n",
"electionresults modi win go\n",
"\n",
"rishika625 communal polarisation new normal indian_politics\n",
"\n",
"electionresults bjp upelection2017\n",
"\n",
"gharwapasi nitish_kumar unhappy lalu\n",
"\n",
"electionresults verdict2017\n",
"\n",
"wittyfeedlive electionresults uttarakhandelection2017 pa_ie lead 7070 bjp 52 congress 14 04\n",
"\n",
"electionresults shameless yadavakhilesh rahulgandhicong\n",
"\n",
"tejasyagnik narendramodi_amitshah magic gorakhpur bjp lead_10 seats time electionresults\n",
"\n",
"hea_iest thanks give tremendous mendate bjp4india join narendramodi_ji vision development electionresults\n",
"\n",
"evm_tampering reason bjp win machine perfectly fine punjab electionresults\n",
"\n",
"chanakya bjp4india prove mettle pa new_height electionresults electionresults2017\n",
"\n",
"rocksboiling electionresults incindia atleast understand impo_ance punjab border state strategic location nat\n",
"\n",
"chotaa_bheem electionresults devband muslim_area\n",
"\n",
"win bjp\n",
"\n",
"stay_tune current result right bingelectionslive electionresults\n",
"\n",
"wanna know congress comeback punjab\n",
"\n",
"late update electionresults bingelectionslive\n",
"\n",
"jhasanjay ban congress ishrat connection\n",
"\n",
"guy decimate elections2017 electionresults\n",
"\n",
"chill water burnol people use mint base toothpaste electionresults\n",
"\n",
"electionresults wave public belief develop govt modivsall narendramodi upelection2017 suppo bjp\n",
"\n",
"aap sweep goa seat\n",
"\n",
"arvindkejriwal excuse\n",
"\n",
"eggsonface aaptards\n",
"\n"
]
}
],
"source": [
"bigram_sentences = LineSentence(bigram_sentences_filepath)\n",
"for bigram_sentence in it.islice(bigram_sentences, 2020, 2150):\n",
" print u' '.join(bigram_sentence)\n",
" print u''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating Trigram Model"
]
},
{
"cell_type": "code",
"execution_count": 369,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_model_filepath = os.path.join('trigram_model_all')"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_model = Phrases(bigram_sentences)\n",
"trigram_model.save(trigram_model_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_model = Phrases.load(trigram_model_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 372,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_sentence_filepath = os.path.join('trigram_sentences.txt')"
]
},
{
"cell_type": "code",
"execution_count": 373,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.52 s, sys: 127 ms, total: 1.65 s\n",
"Wall time: 1.67 s\n"
]
}
],
"source": [
"%%time\n",
"with codecs.open(trigram_sentence_filepath, 'w', encoding='utf-8') as f:\n",
" for sentence in bigram_sentences:\n",
" trigram_sentence = u' '.join(trigram_model[sentence])\n",
" f.write(trigram_sentence+'\\n')"
]
},
{
"cell_type": "code",
"execution_count": 374,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigram_sentences = LineSentence(trigram_sentence_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 375,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"elections2017\n",
"\n",
"cnbctv18live captain amarinder singh helps congress regain power\n",
"\n",
"punjab resultswithnews18 electionresults\n",
"\n",
"shefvaidya cm sattakagulaal electionresults\n",
"\n",
"india today repo er abuse goa cm record electionresults elections2017 punjab uttarakhand uttarpradesh\n",
"\n",
"atleast day delhi people cm work delhi election2017 electionresults punjabelection2017\n",
"\n",
"congress credit rahul ji punjab victory blame capt amrinder singh loss uttarakhand\n",
"\n",
"electionresults\n",
"\n",
"resultswithndtv\n",
"\n",
"result pm narendramodi unifying figure m_lekhi ndtv electionresults\n",
"\n",
"manojtiwarimp sir ji time change delti punjab goa give answer aap electionresults\n",
"\n",
"electionresults hea ly congratulation modi ji team invincible victory\n",
"\n",
"electionresults save tigers save congress sp bsp\n",
"\n",
"ra_bie\n",
"\n",
"today chanakya group survey agency analyst astrologer electionresults\n",
"\n",
"cycle sp pe break haath congress hi lagata hai\n",
"\n",
"thank manojtiwarimp zeenews electionresults\n",
"\n",
"indian presstitute\n",
"\n",
"shame sham electionresults\n",
"\n",
"barkha rajdeep rana et staff right\n",
"\n",
"electionresults\n",
"\n",
"thequintlab electionresults fever take thequint\n",
"\n",
"check election special page\n",
"\n",
"congratulation evey bjp4india member guy\n",
"\n",
"electionresults\n",
"\n",
"bjp win uttarakhand\n",
"\n",
"say bjp truly win nation electionresults elections2017 modified modivsall\n",
"\n",
"bhaavnaarora electionresults super exciting\n",
"\n",
"look like hope manipur goa\n",
"\n",
"wave tsunami electionresults\n",
"\n"
]
}
],
"source": [
"for unigram_sentence in it.islice(unigram_sentences, 220, 250):\n",
" print u' '.join(unigram_sentence)\n",
" print u''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%time\n",
"for parsed_tweet in parser.pipe(line_tweet(bigram_sentences_filepath),\n",
" batch_size=10000, n_threads=4):\n",
" \n",
" for sent in parsed_tweet.sents:\n",
" noun_word = [token.lemma_ for token in sent\n",
" if token.pos_ == \"NOUN\"]\n",
" if len(noun_word) > 0:\n",
" print noun_word"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%time\n",
"pnoun = []\n",
"for parsed_tweet in parser.pipe(line_tweet(bigram_sentences_filepath),\n",
" batch_size=10000, n_threads=4):\n",
" \n",
" for sent in parsed_tweet.sents:\n",
" pnoun.extend([token.lemma_ for token in sent\n",
" if token.pos_ == \"PROPN\"])\n",
" \n",
"print len(set(pnoun))\n",
"print len(pnoun)\n",
"print set(pnoun)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for parsed_tweet in parser.pipe(line_tweet(bigram_sentences_filepath),\n",
" batch_size=10000, n_threads=4):\n",
" \n",
" for num, entity in enumerate(parsed_tweet.ents):\n",
" print 'Entity {}:'.format(num + 1), entity, '-', entity.label_\n",
" print ''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Trying to find out similar words "
]
},
{
"cell_type": "code",
"execution_count": 379,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from gensim.models import Word2Vec\n",
"\n",
"trigram_sentences = LineSentence(trigram_sentence_filepath)\n",
"word2vec_filepath = os.path.join('word2vec_model_all')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training the word2vec model"
]
},
{
"cell_type": "code",
"execution_count": 380,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.87 s, sys: 176 ms, total: 3.04 s\n",
"Wall time: 1.99 s\n"
]
}
],
"source": [
"%%time\n",
"word2vec = Word2Vec(trigram_sentences, size=200, window=5, min_count=25, sg=1, workers=4)"
]
},
{
"cell_type": "code",
"execution_count": 381,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"word2vec.save(word2vec_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 382,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 31.3 s, sys: 1.81 s, total: 33.1 s\n",
"Wall time: 21.5 s\n"
]
}
],
"source": [
"%%time\n",
"for i in range(1,12):\n",
" word2vec.train(trigram_sentences)\n",
" word2vec.save(word2vec_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 383,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"word2vec = Word2Vec.load(word2vec_filepath)\n",
"word2vec.init_sims()"
]
},
{
"cell_type": "code",
"execution_count": 384,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1,007 terms in the word2vec vocabulary.\n"
]
}
],
"source": [
"print u'{:,} terms in the word2vec vocabulary.'.format(len(word2vec.wv.vocab.keys()))"
]
},
{
"cell_type": "code",
"execution_count": 385,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Word2Vec.build_vocab of <gensim.models.word2vec.Word2Vec object at 0x10d9d1110>>"
]
},
"execution_count": 385,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"word2vec.build_vocab"
]
},
{
"cell_type": "code",
"execution_count": 386,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_related_terms(token, topn=10):\n",
" \"\"\"\n",
" look up the topn most similar terms to token\n",
" and print them as a formatted list\n",
" \"\"\"\n",
"\n",
" for word, similarity in word2vec.most_similar(positive=[token], topn=topn):\n",
"\n",
" print u'{:20} {}'.format(word, round(similarity, 3))"
]
},
{
"cell_type": "code",
"execution_count": 471,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 493,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def listToDict(data):\n",
" data_dict = {}\n",
" for pair in data:\n",
" data_dict[pair[0]] = pair[1]\n",
" return data_dict"
]
},
{
"cell_type": "code",
"execution_count": 497,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"aajtak 0.392\n",
"indiatoday 0.387\n",
"rajdeep 0.378\n",
"cnnnews18 0.359\n",
"abpnewstv 0.312\n",
"lady 0.302\n",
"anti 0.299\n",
"bjp_join_300 0.294\n",
"truth 0.293\n",
"listen 0.284\n"
]
},
{
"data": {
"image/png": 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cc4+OHDmiCxcuKDExUeXLl9ehQ4fUvHlz/eMf/3CbIzs7W08//bRSUlK0aNEi9enTR6+8\n8oo++eQTTZ8+Xe+//75cLpfq16+v4cOHq0uXLrr//vvl58cBWgAAUPo52nhM01SNGjX04YcfKiQk\npOCIoGEYCg8P16pVqxQcHKwPP/ywyG1kZWVp3Lhxmj9/vmbPnq0JEyYoJyen0JoWLVro9ddf18cf\nf2y5jSZNmqhKlSoaP368EhISlJqaqvPnz6t69eqqXr26kpKSlJycrBo1aqh69eo6f/68zpw5470g\nAAAAfJjjh8jy8vIkSX5+fgoI+L8DnufPn5f0v1L5e0fyTNOUy+WSy+WSn5+f2/pq1aopODi44Ll+\na/fu3crLy9Pq1as1depUBQQEqFy5cjp58qSSkpIKimJycrKSkpIUGBioSpUqXcnLBgAAuGo4/pZ0\nbm6u7r77bpUvX16VKlWSYRiSpNTUVHXv3l0hISHq3r2722Pz8vLk5+en0NBQjRkzRg8//LDy8vI0\nadIkBQUFFWznt89n5fz58/rrX/+q3Nxc3X///apcubJGjhypAQMGKCIiQqNGjZLL5dKjjz6qxMRE\nPfnkk7wdDQAAygzDNE3T6SEkaerUqVq1apXmzZunNWvW6Oeff9aUKVMkSV988YVeeOGFgrUul0vJ\nycnq3bu3xo8ff0nP89prr+lf//pXwc8BAQGFfr5Sb45+V9Uq1fDa9gAA+LWTZ5LUvH9jRUfXKXJN\nRESw0tKyS3CqqwO5uIuMDPv9RfKhwlhaUBgBAMWJwnj5yMWdp4WR91UBAABgi8IIAAAAWxRGAAAA\n2KIwAgAAwNYVF8YzZ85oy5YtOnLkiBfGAQAAgK+5pMK4atUq9e3bt+Ci2lu3blV8fLweeOABJSQk\naOzYsXK5XMUyKAAAAJzhcWFcu3atxowZo++//16nTp2SJD377LM6d+6cevfurVatWmnFihVatGhR\nsQ0LAACAkudxYVywYIGqVq2q9evXKyoqSnv37tWPP/6ozp07a/LkyXrnnXfUsGFDLV++vDjnBQAA\nQAnzuDDu379fXbt2VfXq1SVJmzdvliTdcccdBWtatGihw4cPe3dCAAAAOMrjwmiapgIDAwt+/vzz\nz2UYhm699daC23Jzc1WhQgXvTggAAABHBXi6sF69etq2bZtcLpd++ukn7d27Vw0bNlTlypUlSenp\n6Vq/fr3q1atXXLMCAADAAR4fYezVq5f27dunhIQE9enTRy6XS/369ZMkrVy5Uj169FBKSor69+9f\nbMMCAACg5Hl8hDExMVE5OTmaM2eO/P39NXToUN1zzz2SpB9//FFpaWl68skndffddxfbsAAAACh5\nhmma5pVuJCUlRSEhIQoODvbGTFe1KQ/PUJXwqk6PAQAopU5nnFKXh+MVHV2nyDUREcFKS8suwamu\nDuTiLjIyzKN1Hh9htH+ySG9splToO7qbMjJynB7D54SHVyAXC+RijVzckYm1sppLzZpRTo+AMqbI\nwjh27NjL3uiUKVMu+7FXu7p16/K3Fwv8rc4auVgjF3dkYo1cgJJRZGFcsWLFZW+0LBdGAACA0qbI\nwrhu3bpCP6empuqxxx5T5cqV9fjjj6tp06aqWLGicnJytHv3bs2YMUOpqal66623in1oAAAAlJwi\nC2PdunUL/fzqq68qICBA8+fPV0RERMHtYWFhatOmjZo0aaKePXtq6tSpmjlzZvFNDAAAgBLl8XUY\nP/30U91+++2FyuKvhYaGqkOHDtqyZYvXhgMAAIDzPC6Mfn5+SktLs12TnJysoKCgKx4KAAAAvsPj\nwti8eXOtW7dOX331leX969at04YNG9S2bVuvDQcAAADneXzh7gMHDqh///46d+6c2rZtq0aNGik0\nNFSZmZnauXOntm7dqqpVq2rp0qWqVatWcc/tsw4ePFwmrwn2e8rqtdJ+D7lYIxd3ZGKNXNzVrBml\nqlXDudyQBS7D5M7TC3df0je9fPfdd3rhhRe0Y8eOwhsxDN12220aN26coqOjL23SUualZ/upamXP\nwgcAwJtOpWaqQ/dxatQolmJkgcLozuvf9HL8+HHVr19fCxcuVHJysvbv36+MjAyFh4frpptuUtWq\nfB2eJFWtHKbqkeFOjwEAAOA1HhfG++67T40aNdIrr7yi6tWrq3r16sU5FwAAAHyExx96SUlJKfNv\nNwMAAJRFHhfGli1basuWLcrNzS3OeQAAAOBjPH5Lum/fvnr++efVtWtX3Xbbbapdu7bKly9vufa+\n++7z2oAAAABwlseF8Yknnij472XLlhW5zjAMCiMAAEAp4nFhnDx5skfrDMO47GEAAADgezwujL17\n9y7OOQAAAOCjPC6MF2VlZWn9+vX6/vvvlZOTo4iICN1www3q2LGjQkJCimNGAAAAOOiSCuPGjRs1\nduxYZWRkuN1XsWJFTZkyRfHx8V4bDgAAAM7zuDDu3btXI0aMUEBAgB544AE1a9ZM1apVU0ZGhrZv\n366FCxdq5MiRWrp0qWJjY4tzZgAAAJQgjwvja6+9Jj8/Py1evFgNGjQodF/btm3VuXNn9e/fX2+8\n8YamTZvm9UEBAADgDI8v3L1z50516tTJrSxedNNNN6lTp07avn2714YDAACA8zwujNnZ2YqMjLRd\nU6VKFaWnp1/xUL7o2LFjTo8AAADgCI8LY3R0tL766iu5XC7L+/Pz87Vt2zbVrl3ba8P5iuTkZCUk\nJEiS1q9frzFjxjg8EQAAQMnxuDD26NFDBw4c0DPPPKPs7OxC96Wmpurpp5/WgQMH1L17d68PWdy2\nbNmiTp06qWPHjpo2bZpiY2M1atQotWnTRu+++67eeecd5eXl6emnn1ZWVpZOnDjh9MgAAAAlxuMP\nvTzwwAP6/PPPtXz5cq1Zs0YNGjRQWFiYkpOT9eOPP+rcuXNq2rSpHnzwweKct1jUrVtXw4YN0969\ne/Xxxx/LMAwlJiaqadOmWr9+vZ577jnNnTtXf/vb37R8+XKnxwUAAChRHhfGwMBAzZ07V7Nnz9aK\nFSu0c+fOgvtq166tXr166Y9//KMCAwOLZdDi9MYbb8gwDDVt2lSffvqpTNNUZGSkgoODdeHCBb7u\nEAAAlGmXdOHuoKAgDRs2TMOGDVNWVpbOnj2rkJAQhYaGFtd8JaJevXqaM2eOTp8+rczMTBmGUeif\nKlWqqFatWho1apTatm1LgQQAAGWKYZqm6cnC+Ph4dejQQe3bt9ctt9xyVR5JLAnzZ/xR1SPDnR4D\nAFAGJadkqGGbJ9SoUazS0rJ//wFlTEREMLn8RmRkmEfrPD7CGBYWpnfffVeLFy9WhQoV1KpVK3Xo\n0EEdOnRQjRo1LntQAAAA+DaPC+OqVauUkpKif//73/riiy/05ZdfavPmzZKkG2+8UR07dlT79u3V\ntGlT3rIFAAAoRTx+S/q3XC6X9uzZU1Ag9+zZo/z8fFWsWFHbtm3z9pxXDd6SBgA4hbek7fGWtDtP\n35L2+DqMv2WapkzTVFBQkMLDwxUUFCRJpfabXgAAAMoqj9+Szs3N1a5du7R9+3bt2LFD3377rXJy\nciRJVatWVfv27dWyZUu1atWq2IYFAABAyfO4MDZv3lx5eXkyTVNRUVHq0KGDWrRooZYtW+r6668v\nzhkBAADgII/fks7Pz5dpmgoODtZNN92km2++Wc2bN6csAgAAlHIeH2Hctm2btm3bpq1bt2rbtm3a\nsGGDTNNUpUqV1Lx5c7Vs2VItW7ZUTExMcc4LAACAEnZJ12Hs1KmTOnXqJEk6ffp0QYn85ptvCgpk\nRESEtm7dWmwDAwAAoGRd0lcD/lqVKlXUtm1blStXTgEBAcrKytIvv/yitLQ0b84HAAAAh11SYTx3\n7px27typr776Sl999ZW+++47uVwulStXTjfffLPuu+8+tWvXrrhmBQAAgAM8LoyJiYn69ttvdf78\neUlSjRo11KdPH7Vr106tW7dWaGhosQ0JAAAA53hcGL/55hs1bdpU7dq1U/v27flwCwAAQBnhcWHc\nunWrwsI8+/qYsuxUaqbTIwAAyih+B6G4XNKnpPH7egycrIyMHKfH8Dnh4RXIxQK5WCMXd2RijVzc\n1awZ5fQIKIUu+1PSsFa3bl2+2NwCX/hujVyskYs7MrFGLkDJ8PibXgAAAFA2URgBAABgy+PCuHbt\nWiUnJxfnLAAAAPBBHhfGiRMnasKECcU5CwAAAHyQx4Xx3Llzuvbaa4tzFgAAAPggjwtjnz599OGH\nH+rAgQPFOQ8AAAB8jMeX1QkPD5ck9ezZU3Xq1FHt2rVVvnx5y7UzZ870znRXoaNHj3JNMAvp6Vwr\nzQq5WCMXd2RirazlUrNmlAICuCIeSp7He91rr71W8N9HjhzRkSNHimOeq94//vW5wqtWc3oMAEAp\nk3HqpB5p30LR0XWcHgVlkMeFccOGDcU5R6kRXrWaKlXnKvsAAKD08Lgw1q5duzjnAAAAgI+65BMh\njhw5otTUVLlcLpmmKUkyTVN5eXk6c+aMPv/8c/3973/3+qAAAABwhseFMTU1VUOHDtXevXst7zcM\no6BAUhgBAABKD48vq/PKK69o7969uvHGG9WvXz+FhoaqSZMmuvfee9WiRQuZpqlbb71VK1asKM55\nAQAAUMI8PsL473//W/Xq1dOKFSvk7++v06dPKzc3V88++6wkaeXKlXrmmWeKbVAAAAA4w+MjjCdP\nnlTbtm3l7+8vSapfv76+/fbbgvt79uyppk2batasWd6fEgAAAI7xuDCWL19eQUFBBT/XqVNH6enp\nSk5OLritUaNG+vrrr707IQAAABzlcWG89tprCx1RvOaaayRJ+/btK7jt7Nmzys3N9eJ4AAAAcJrH\nhbFbt27asWOHnnrqKR0/flwxMTGKjIzU9OnTdejQIW3dulUfffRRQZEEAABA6eBxYRwwYIA6d+6s\nDz/8UDt27FBAQICGDRum/fv3q1u3bho8eLCysrL00EMPFee8AAAAKGEef0q6XLlymj59unbt2qWa\nNWtKkvr166eKFStq9erVCgoK0t1336327dsX27AAAAAoeZf8TS9xcXGFfk5ISFBCQoLXBgIAAIBv\nKbIwZmVlXfZGQ0NDL/uxAAAA8C1FFsbmzZvLMIxL2phpmjIMQ999990VD1bcjh07pujoaNs1pmnq\nl19+UVRUVAlNBQAA4HuKLIwtWrQoyTlK1Nq1a7V48WLNnz/fdt1zzz2nypUra/jw4YqPj9ff/vY3\ntWzZsoSmBAAA8A1FFsYFCxaU5Bwl6s0339Thw4fVunVrRUVFqXLlyoqMjFStWrU0fPhwxcbGavHi\nxVq1apUCAwPVrl07SdIbb7yh0aNHq0+fPnrsscccfhUAAAAlw+PL6pQmiYmJatSokW644Qbdcccd\nmj17ttua6tWrq3Pnzho4cKAaN24sSbrnnnv07LPP6l//+ldJjwwAAOCYIo8wDhs2TN26ddOdd95Z\n8LOn5zTOnDnTO9MVk1+/jho1akiS/Pz8dO7cOaWmphb5uMjISLlcLuXl5RX7jAAAAL6iyMK4ceNG\n1a9fv9DPpcW1116rH374oeBDOpLUqVMnTZgwQcnJyQoNDZVhGGrQoIHeeust3XLLLQWPNQzjkj8M\nBAAAcDUzTNM0re44fvy4KlasqLCwsIKfPVW7dm3vTHcV+vOCj1WpOp+qBgB415nkExpwU11FR9f5\n3bUREcFKS8sugamuLuTiLjIyzKN1RR5h/G3pK8slEAAAoCy75G96yc3N1c8//6zz588XuSY2NvaK\nhgIAAIDv8LgwnjlzRuPGjdPGjRtVxLvYknTVXLgbAAAAnvG4ME6ePFkbNmxQnTp1dNNNNykoKMhy\nHR8IAQAAKF08Loxffvml4uLitHjxYvn5lcnLNwIAAJRJHje/3Nxc3XzzzZRFAACAMsbj9nfbbbdp\n586dxTkLAAAAfJDHhXHs2LE6efKkRo4cqd27dys1NVVZWVmW/wAAAKD08PgcxooVK6phw4Zas2aN\n1qxZY/nhlovfnMKnpAEAAEqPS/qU9CeffKIKFSro2muvVXBwcHHOBQAAAB/hcWH85JNPdMMNN2jx\n4sUFXxcIAACA0u+SPiXdrl07yiIAAEAZ43FhbNasmfbv31+cswAAAMAHefyW9FNPPaUBAwZoypQp\nGjx4sGpGLMZ2AAAgAElEQVTWrFmcc121Mk6ddHoEAEAp9L/fL3WdHgNllGHafTH0rwwZMkTHjx/X\nsWPHZBiGAgICVKFCBcu1X3/9tVeHvJocPHhYGRk5To/hc8LDK5CLBXKxRi7uyMRaWculZs0oBQT8\n/rGeiIhgpaVll8BEVxdycRcZ6dmphh4fYTx69KgkKSoq6vImKiPq1q3LzmiBP6TWyMUaubgjE2vk\nApQMjwvjpk2binMOAAAA+Ci+GBoAAAC2ijzCOH/+fMXFxalx48aSpHnz5ll+u4uV++67zzvTAQAA\nwHFFFsbJkydr+PDhBYVxypQpHm3QMAwKIwAAQCliWxgbNGhQ6GdPeHoUEgAAAFeHIgtj7969bX8G\nAABA2eDxp6St5ObmKikpSVWrVlVISIi3ZrqqHT16tExdE8xT6ell61ppniIXa+TijkyskYu1X+fi\n6bUbATu/uwdt3LhRGzZs0P3336/Y2FhJkmmaevHFF7Vw4UKdO3dO/v7+6tSpkyZOnKhKlSoV+9C+\n7E8vfKTgsKpOjwEAgLIzT+nvj8UrOrqO06PgKmdbGMePH69ly5ZJktq3b19QGKdNm6bZs2fLMAy1\nadNGkrR+/XodPHhQK1asUGBgYDGP7buCw6oqJKKG02MAAAB4TZHXYdy0aZOWLVumBg0aaM6cOerY\nsaMkKTk5WW+//bYkadKkSZozZ47mzJmj6dOn69ChQ5o3b17JTA4AAIASUWRhfP/991WxYkXNnz9f\nbdq0UVBQkCRp7dq1ysvLU926ddW3b9+C9Z06dVJcXJzWrVtX/FMDAACgxBRZGHfv3q0OHTooNDS0\n0O1btmyRJMXHx7s9pkmTJgXfOQ0AAIDSocjCmJ6erho1Cp+L53K5tGPHDhmGodatW7s9ply5cjp/\n/rz3pwQAAIBjiiyMoaGhOnPmTKHbdu/erbNnzyogIEAtWrRwe8xPP/1U5j8lDQAAUNoUWRgbN26s\nLVu2yOVyFdy2evVqSVLr1q0VHBxcaH1qaqq++OKLgq8SBAAAQOlQZGH8wx/+oOPHj2vkyJHavn27\nFi5cqKVLl0qSBg0aVGhtZmam/vznPys7O1s9evQo3okBAABQooq8DuPtt9+ugQMHatGiRYU++dy/\nf3+1b9++4OeRI0fqs88+U3Z2trp06aJOnToV78QAAAAoUbYX7h43bpw6d+6szZs368KFC2rbtq06\ndOhQaM2+fftUvnx5Pfjgg3rkkUeKc1YAAAA44He/GrBVq1Zq1apVkfcvX77c7dI7AAAAKD2KPIfR\nU5RFAACA0u2KCyMAAABKNwojAAAAbJXpwrh8+XIlJibaromNjdWJEydKaCIAAADfU6YLo2EYOnny\npHr27KlOnTpp8ODBcrlcWrZsmdq1a6dHH31UhmHol19+UbNmzZSWlqb3339fgwcPdnp0AACAEvO7\nn5IuzUzTVEhIiIYMGaL09HRNnjxZJ0+e1IsvvqjXXntNNWvW1KZNm1SjRg3dfvvtWrFihdatW6eh\nQ4c6PToAAECJKdNHGCUpOztbCxcuVOPGjRUQEKD8/Hz5+/vLNM2Cr0U0DEMPPPCAZs+erdOnTys+\nPt7hqQEAAEpOmT7CaBiGjhw5oipVqmjq1KmqWrWqTp8+rdGjR+vJJ59Uo0aNFBISIkmqX7++goOD\nNWDAAIenBgAAKFllujD26tVLvXr1cru9cePGhW43TVMJCQkKCwvTvffeW5IjAgAAOK5MF0ZPGYah\nNWvWOD0GAACAI8r8OYwAAACwR2EEAACALQojAAAAbFEYAQAAYIvCCAAAAFsURgAAANiiMAIAAMAW\nhREAAAC2KIwAAACwRWEEAACALQojAAAAbFEYAQAAYIvCCAAAAFsBTg9Q2mRnnnJ6BAAAJPE7Cd5j\nmKZpOj1EaXLw4GFlZOQ4PYbPCQ+vQC4WyMUaubgjE2vkYu3XudSsGaWAAI4PSVJERLDS0rKdHsOn\nREaGebSOPcjL6taty85ogT+k1sjFGrm4IxNr5GKNXOBtnMMIAAAAWxRGAAAA2KIwAgAAwBaFEQAA\nALYojAAAALBFYQQAAIAtLqvjZUePHuWaYBbS07lWmhVysUYu7sjEGrlY80YuXL8Rv8ae4GUT1vxF\nYR5eBBMAAF+UmZKpUW3GKTq6jtOjwEdQGL0sLDJMFWuEOz0GAACA13AOIwAAAGxRGAEAAGCLwggA\nAABbFEYAAADYojACAADAFoURAAAAtiiMAAAAsEVhBAAAgC0KIwAAAGxRGAEAAGCLwggAAABbFEYA\nAADYojD+juPHjzs9AgAAgKOuisJ4/PhxxcbGlvjzzpkzR6+++mqJPy8AAIAvCXB6gKJs2bJF48eP\nV35+vpo1ayZJ+tOf/qR9+/ZpwoQJSk9P19tvv62QkBCdPXtWr7/+ulasWKEvv/xSZ8+elZ+fnxYt\nWqTNmzfrtddeU7ly5fTEE09o+fLl6tixo3JycjRr1ix98cUX6tChg/75z3/q73//u9LS0hQWFqa3\n3npLb775plwul2JiYjR9+nR9/vnn2rx5sxYuXKglS5Y4nBAAAEDJ8NkjjHXr1tWwYcMUHx+vNWvW\nyDAMPfvssxo1apReffVVGYahvLw8LV26VHFxcVq8eLEMw1BISIg+/PBDpaSk6MCBA5oyZYouXLig\nnJwczZ8/X126dNHmzZv1xRdfSJJWrlyp4OBg1ahRQ7/88ovuvvtu3XPPPapQoYISExPVqVMnDR48\nWC1bttTHH3+s5cuXa8CAAQ6nAwAAUHJ89gjjG2+8IcMw1LRpU3366ac6ceKE8vPzZRiGAgL+N3Z+\nfr7y8/NlmqYMw5AkVatWTYZhqHz58srLy1N+fr4mTZqkkJAQnT59Wi1bttTkyZMVFBSk++67T9Om\nTdM999yj8uXL68knn1RgYKBeffVVBQYGFppn4MCBmjJlijIyMpSQkFDieQAAADjFZ48w1qtXTxs2\nbNCGDRuUmZmp8uXL6/nnn9dLL72k4cOHyzRN5efn695779Xu3bs1cOBAt20YhqGnn35a48aN05NP\nPqmAgACFhISoSZMmatSokdq1a6eTJ0+qa9euKleunFatWqVp06YpICBAcXFxio2N1WeffaZ169bp\ntttuU35+vnr16qVy5co5kAgAAIAzDNM0TaeHuBwrVqzQihUrNH/+fKdHKeSRJX9UxRrhTo8BAMBl\nS0/K0NAbn1B0dB2nR/GqiIhgpaVlOz2GT4mMDPNonc++Jf17evXqpV69ejk9BgAAQKnns29JAwAA\nwDdQGAEAAGCLwggAAABbFEYAAADYojACAADAFoURAAAAtiiMAAAAsEVhBAAAgC0KIwAAAGxRGAEA\nAGCLwggAAABbFEYAAADYCnB6gNImMyXT6REAALgimSmZ0o1OTwFfYpimaTo9RGly8OBhZWTkOD2G\nzwkPr0AuFsjFGrm4IxNr5GLNG7nUrBmlgIDSdVwpIiJYaWnZTo/hUyIjwzxaV7r2BB9Qt25ddkYL\n/CG1Ri7WyMUdmVgjF2vkAm/jHEYAAADYojACAADAFoURAAAAtiiMAAAAsEVhBAAAgC0KIwAAAGxx\nWR0vO3r0KNcEs5CezrXSrJCLNXJxRybWyMUauVgrK7kUxzU0KYxetnHsGFULDXV6DAAAUAadzMpS\ni7ETFR1dx6vbpTB6WbXQUNUMD3d6DAAAAK/hHEYAAADYojACAADAFoURAAAAtiiMAAAAsEVhBAAA\ngC0KIwAAAGxRGAEAAGCLwggAAABbFEYAAADYojACAADAFoURAAAAtiiMAAAAsEVhBAAAgC0K428c\nO3bM6REAAAB8CoVR0lNPPaUNGzZo7dq1+utf/2q5Zvny5UpMTCzhyQAAAJwX4PQAJWHLli0aP368\n8vPz1blzZ+3bt08nT55UaGio3nnnHf3yyy/KzMzUwoULdejQIS1atEjZ2dlavHixTNPUuHHjZBiG\nJGnBggVatmyZFixYoIiICIdfGQAAQPErE4Wxbt26GjZsmPbu3atNmzZp5MiROnv2rKZOnaoDBw4U\nrBs0aJBWrFihgQMH6j//+Y8iIyP14YcfatOmTWrevLm+//577dy5U6tXr6YsAgCAMqNMvCX9xhtv\naNeuXWrUqJFOnDihV155RbGxsQoJCZHL5SpYZxiGTNOUJI0dO1YXLlzQddddV7AmKChI3bt318KF\nCx15HQAAAE4oE4WxXr162rBhgzZs2CB/f3+dO3dOzz77rEJDQ3X69Gnl5eXJz89P1157rX744QfN\nnz9f1113nWbNmqVDhw4pNTVVknTNNdfomWee0SeffKL9+/c7/KoAAABKhmFePKRWRk2dOlWrVq3S\nvHnzdN11113x9lYMGaKa4eFemAwAAODS/JKRobqP/VnR0XU8Wh8ZGebRujJxhNHO6NGj9cUXX3il\nLAIAAJRGZb4wAgAAwB6FEQAAALYojAAAALBFYQQAAIAtCiMAAABsURgBAABgi8IIAAAAWxRGAAAA\n2KIwAgAAwBaFEQAAALYojAAAALBFYQQAAICtAKcHKG1OZmU5PQIAACijTmZlqW4xbNcwTdMshu2W\nWQcPHlZGRo7TY/ic8PAK5GKBXKyRizsysUYu1sjFWlnJpWbNKAUEeHZMMDIyzKN1FEYvu3AhX2lp\n2U6P4XMiIoLJxQK5WCMXd2RijVyskYs1cnHnaWHkHEYAAADYojACAADAFoURAAAAtiiMAAAAsEVh\nBAAAgC0KIwAAAGxx4W4vO3r0aJm4xtOlSk8vG9e+ulTkYo1c3JGJtas9l0u5Xh7gJPZSL3tv6keq\nEl7V6TEAAD7udMYpdXk4XtHRdZweBfhdFEYvqxJeVdUq1XB6DAAAAK/hHEYAAADYojACAADAFoUR\nAAAAtiiMAAAAsEVhBAAAgC0KIwAAAGxRGAEAAGCLwggAAABbFEYAAADYojACAADAFoURAAAAtiiM\nAAAAsEVh/I1jx445PQIAAIBPKVOFcfny5ercubMSEhIs71+7dq3++te/lvBUAAAAvi3A6QFKkmEY\nysvL07Fjx5Sfn6+hQ4fq6NGjCgwM1IwZM/Tmm2/q8OHDWrRokSpWrKjXXntN5cqV08iRI1WhQgWN\nHTtW9erV04EDBzRr1iw1atTI6ZcEAABQ7MrUEcZfO3/+vPbv368777xT9957rypXrqzExEQ1btxY\nAwcO1JQpU3ThwgXl5ORo/vz5MgxDmZmZmjVrlpo1a6Yvv/zS6ZcAAABQIsrUEUbTNAv+nZ+fr1Gj\nRik4OFhz585VamqqrrnmmoI1+fn5mjRpkkJCQnTq1CmZpqmwsDAFBQUpODhYeXl5Tr4UAACAElOm\nCqNhGAX/DgoK0ubNm7V3716VK1dOt956qypUqKAffvhBCxYs0NNPP61x48ZJkiZMmCDDMAo9HgAA\noKwwzIuH1OAVb45+V9Uq1XB6DACAjzt5JknN+zdWdHQdr287IiJYaWnZXt/u1Y5c3EVGhnm0rsye\nwwgAAADPUBgBAABgi8IIAAAAWxRGAAAA2KIwAgAAwBaFEQAAALYojAAAALBFYQQAAIAtCiMAAABs\nURgBAABgi8IIAAAAWxRGAAAA2KIwAgAAwBaFEQAAALYCnB6gtDmdccrpEQAAVwF+X+BqQmH0sr6j\nuykjI8fpMXxOeHgFcrFALtbIxR2ZWLvac6lZM8rpEQCPUBi9rG7dukpLy3Z6DJ8TERFMLhbIxRq5\nuCMTa+QClAzOYQQAAIAtCiMAAABsGaZpmk4PAQAAAN/FEUYAAADYojACAADAFoURAAAAtiiMAAAA\nsEVhBAAAgC0KIwAAAGzxTS9esHPnTiUnJ6tGjRpq1qyZ0+P4hD179uiGG27QypUr5e/vrx49eigo\nKMjpsXwC+4s79hdr7Cvu2FeKxv7ijv3F2uXsK1yH8QqNGDFC3333nSIjI5WSkqIGDRro5Zdfdnos\nxyUkJOi6665Tenq6ypUrp5CQEM2YMcPpsRzH/mKN/cUd+4o19hVr7C/W2F/cXe6+whHGK7Rnzx5t\n3LhRhmHI5XLpjjvucHokn3D+/HkdOXJEH374oQzDUHx8vNMj+QT2F2vsL+7YV6yxr1hjf7HG/uLu\ncvcVCuMVatiwobp06aJq1aopJSVFDRs2dHokn1ClShX997//1datW/Xpp58qNjbW6ZF8AvuLNfYX\nd+wr1thXrLG/WGN/cXe5+wpvSXvBjh07lJycrOrVq6t58+ZOj+Mzzp49Kz8/PyUnJysqKkqBgYFO\nj+Q40zS1c+dOJSUlqXr16mrRooXTI/mMrKws+fv7KykpSbVq1WJ/0f/9v6VatWrsK79y8f8t7CuF\n7dixQ0lJSapRowa/i36F30XuLqe3+E+cOHFi8Y5Vum3btk2zZs3Spk2btH//ftWsWVO1atVyeizH\nbdu2TZMmTdKcOXP0zTffqHbt2uQiyTAMRUVF6cYbbySP3wgMDFS5cuVUqVIl+fv7Oz2O45YsWaJX\nX31VMTEx6ty5s4YMGaKePXs6PZbjlixZomeffVaBgYFq27atHnroIXKRtGvXLm3cuFFNmjTR888/\nr5CQEMXExDg9luN27dqlpUuXKiwsTH/5y18UHh5e5nNZuXKlMjIylJ+fr4kTJ6py5coeHXnlLekr\nNG7cOD366KOqXr26kpOTNW7cOK1bt87psRxHLtbi4+NlGIYuHtg3DEMbN250eCrnkYu7WbNm6amn\nntIbb7yhwMBAJSUlOT2ST5g1a5ZGjx6tN998k1x+ZcyYMWrevLmGDBmiZ555RtOmTVP37t2dHstx\nv83l5ZdfLvO5vPjii/Lz81OdOnWUmZmpDz74wKO/dFEYr5BpmjIMQ35+fvLz47KWF5GLtcTERH3+\n+ef605/+JM4G+T/k4s7f31/XXHONXn/9dQ0YMEDp6elOj+QT/P39de2115LLb7hcLj344INq2LCh\n2rVrp1mzZjk9kk8gF3cffPCBRo0apbvuukunTp3SggULPHocb0lfoRtvvFELFizQ6tWr9dNPP2n0\n6NGqXbu202M5jlysxcXFae/everbt6/q1KlDJv8fubirWrWqfvjhB91222265ZZbtHXrVg0YMMDp\nsRxHLtaqVq2qU6dOqXfv3vr66691/fXXq3Hjxk6P5ThycRcSEqK77rpLH3zwgf773//qoYce8uhx\nfOjFy3bv3l3md0Yr5GJtz549atSokdNj+BxyccefIWvkYo1crJFLYVlZWfrxxx89+v8t7xV6gcvl\n0unTp5Wfn6+XXnrJ6XF8BrlY+3UuL774otPj+AxyccefIWvkYo1crJGLu4uZVKhQweP/31IYr9Dc\nuXMVFxenNm3aqGnTpurQoYPTI/kEcrFGLtbIxR2ZWCMXa+RijVzcXXYmJq5Ix44dzRMnTpj5+fnm\nzz//bMbHxzs9kk8gF2vkYo1c3JGJNXKxRi7WyMXd5WbCp6SvUEREhGbOnKkaNWooOTlZlSpVcnok\nn0Au1sjFGrm4IxNr5GKNXKyRi7vLzYQPvVyhkydP6r333iu4Ynrfvn1VrVo1p8dyHLlYIxdr5OKO\nTKyRizVysUYu7i43EwojAAAAbPGhFwAAANiiMAIAAMAWhREAAAC2KIwAtHHjRj388MO65ZZb1KhR\nI7Vt21aPPvqoNm3a5LZ2+fLlio2N1fz58732/MePH1dsbKyGDRtWcFtiYqJiY2OVlZXlteeRip5/\n9erVOnbsmFefqyz64osvtGfPHqfHKNL58+d111136Z133im47dChQ+rfv7+aNGmi7t27W+73ktSv\nXz89/vjjlvdt2bJFLVq0UEpKSnGMDTiOwgiUcc8995yGDRumQ4cO6Y477tADDzygNm3aaOfOnXr0\n0Uc1fvz4QusbNGig4cOHKy4uzmszVKxYUcOHD1e3bt0K3W4Yhtee4yKr+adOnao///nPOnv2rNef\nryxZvHixHnroIZ08edLpUYr0+uuvKzc3V4MGDZIkmaapkSNH6vDhw+rfv78qVKigxx57TN9//32h\nx3366afavXu3RowYYbndW2+9VU2bNtWkSZOK/TUATuA6jEAZtm3bNi1atEhdunTRtGnT5Of3f3+H\nzMrK0n333adly5apffv2uv322yVJsbGxio2N9eocYWFhGj58uFe3WRSr+U+fPl0iz13a+XqOR44c\n0ZtvvqnnnntOAQH/+/W3Z88eHThwQNOmTVNCQoLOnTun9u3b67333tMzzzwj6X+l8pVXXlG3bt10\n3XXXFbn9kSNHqlevXtq8eTPfKIJShyOMQBm2efNmSdKgQYMKlUVJCg0N1ahRoyRJGzZsKOnRcBXz\n1au1vf322woLC1P37t0Lbjt+/LgkFfwlonz58qpXr17B7ZK0Zs0aHTx4sMi3oy+qX7++mjVrptdf\nf70YpgecRWEEyrALFy5Iktvbbxc1b95cL7/8su6///6C2y6eAzhv3ryC2+Lj4zVkyBB9//33evDB\nB9W0aVO1bt1a48eP17lz55ScnKwnnnhCN998s2699VaNHj1aZ86cKXi81TmMRc07b948/eEPf1Dz\n5s3VsGFDxcfHa8KECUpNTXXb3vTp0/X8888rLi5OrVu31tq1a93OYYyPj9fKlSslST179lR8fLx2\n7Nih2NhYjR492nKOTp06qWPHjrazxsfHa8CAAdq/f78SExMVFxen9u3b6/nnn1d6errb+pSUFE2c\nOFHt2rVTo0aNdPvtt+uf//yn29vkiYmJio+P12effab4+HjFxcXpiSeeKLh/+/btevjhh9WqVSs1\nb95c/fr108aNG92eb9++fXr00UfVqlUrNWnSRD179tSSJUssX0diYqIOHTqkRx55RDfffLOaNWum\noUOHav/+/YXmevXVVyVJw4cPL3QU9+zZs3r11Vd19913q1mzZmrcuLG6dOmiqVOnKicnx+05lyxZ\nou7duysuLk6dOnXS7NmztXLlSsXGxmr79u2X9TrOnDmjVatWqWvXrgVHF6X/nQ5xccaLMjMzFRYW\nJknKz8/XjBkz1KtXL0VHR7tt97e6d++uXbt2adeuXb+7FriaUBiBMqxt27aSpL///e96/vnntWvX\nLrlcroL7g4KC1LVrV8u3oH97fuHx48c1YMAASdKAAQMUGRmpZcuW6amnnlL//v2VlJSkfv36qU6d\nOvrXv/6lcePG/e42f2vUqFGaMmWKAgMDde+996pfv34KDAzU0qVLNXToULf1y5Yt09q1azVgwADF\nxcWpadOmbmvuv//+gtfXr18/DR48WM2bN1ft2rW1adMmnTt3rtD6b775RsePH1ePHj1sZ5X+940K\n999/v86ePatBgwYpOjpaCxcu1KBBg5SdnV2w7sSJE+rTp4+WLl2qRo0aaciQIbrmmms0e/ZsJSYm\nupWqtLQ0jRw5Us2bN1fv3r3VokULSdKqVat0//33a+fOnerQoYP69OmjpKQkDRs2TMuXLy94/Gef\nfaZ+/frp66+/LiiELpdLEydOdDtnVZKSkpLUv39/nTlzRv369VPLli31+eef67777iso6r+eo1u3\nbgWnGOTl5WnIkCGaOXOmqlevroEDB+qee+7RuXPnNGfOHI0ZM6bQc02ePFkTJ07U+fPn9Yc//EFx\ncXF6+eWXC8ror13K69iwYYNyc3ML9vmL6tevr6CgIM2dO1dZWVlav369Dh8+rGbNmhVkevz48d/9\ny8xFF7e/evVqj9YDV43i+nJrAFeHiRMnmjExMQX/NGvWzBw6dKj5zjvvmElJSW7rP/jgAzMmJsac\nN29ewW0dO3Y0Y2JizMmTJxfclpGRYcbFxZkxMTHmE088UXB7fn6+eccdd5ixsbHmuXPnTNM0zWPH\njpkxMTHmsGHDCtYNGjTIjI2NNTMzM03TNM3//Oc/ZkxMjDl69OhC8+Tl5Zndu3c3Y2JizB9//LHQ\n9urXr29+//33vzv/mDFjzJiYGPO7774ruG3GjBlmTEyM+dFHH1nmdfDgQdtcL2by6KOPmi6Xq+D2\n5557zoyJiTFnzJhRcNsf//hHs379+ubmzZsLbWP+/PlmTEyM+Y9//KNQLjExMebf/va3QmvT0tLM\nm2++2WzTpo155MiRgttTU1PN2267zWzdurWZl5dnZmdnm61btzbbtGlj/vzzzwXrXC6X+fjjj5sx\nMTGF5rj4Op577rlCzzdu3DgzJibGfPfddwtumz59uhkTE2Nu2LCh4LbVq1ebMTEx5ssvv1zo8f+v\nvbsPiqpsHzj+XZaVgFVAMbTiTVJgaFCnUlKQUWhSJwVRo2LGXtCGySFLHV/+SMzRwmnQMVI0MDG0\nwWQ0oJKXhmJ9QSWmVlBHG6VoYqzARUVA2Ty/P5g9DwvLspDPz3y8Pv/AHs65X845DBfXue/7tLa2\nKlOnTlXCwsLU++DMmTNKcHCwkpiYqLS1tan7fv/990pwcLASEhKinD59WlEUZcD9WLlypRIcHGzz\nns7JyVFCQkLU34GXX35ZMZvNyu3bt5Xp06db9b37tezLpEmTlDlz5vS7nxD3E8kwCvGAS0tLY9eu\nXURFRaHT6Whra6OyspIPPviAmJgYtmzZ4tCYNI1Gw6uvvqp+Hjp0KGPGjAHgtddeU7c7OTkRFhaG\noij8/vvvDrdz9OjRpKen9xpHptVq1WxQ98fSAH5+fowbN87hOrqLi4sDrDNFnZ2dHDlyhLCwMLuT\nH7q3bc2aNVaZ07fffhs3NzeKi4uBriykwWBg2rRpREdHWx2flJTEqFGjOHz4cK+yn3vuOavPlZWV\n6kQlf39/dbuXlxdr165l8eLF3Lx5k4qKCkwmE8nJyTzyyCPqfhqNhuXLlwNYZSMtP1uyZInVtmnT\npgFd2VF7wsLC2LRpk9WwBgB3d3dCQ0Mxm820tLQAXdk86Jo84urqqu4bHR3N1KlTre7Dgfbj3Llz\n6PV6fHx8erUxOTmZAwcOsGbNGjIzM8nLy0Or1fLFF19w9epVUlJSaG9vZ8WKFYSHh6tDNfoSFBTE\nzyO+IKQAAAn1SURBVD//jNlstntuhLifyCxpIQTR0dFER0fT1tZGdXU1J0+epKKigl9//ZVPPvmE\nO3fusHLlSrtlODs7M3r0aKttbm5uaDQaHnvsMavtLi4uQNeaeI7y8fEhPj4es9nM2bNnqa+vp6Gh\ngfPnz1NVVQVg9Tgd6FXvQPj6+vLkk09y9OhRrl+/zrBhwzh27BgtLS28+eabDre557g3vV5PQEAA\n58+fp6Ojg3PnzgFdj5kzMzN7laHT6bhy5Qp//vknDz/8MIDNc2oZT2hruaNZs2ap39fV1alfbdXn\n5ORkNTYRuq5Xz0BLr9cD/V/DgIAAAgICuHXrFkajUb1uZ8+epbq6Go1Go1632tpaNBoN4eHhvcqZ\nOHEix48fH3Q/mpub8fLy6rOd4eHhVvV2dHSQlZVFUlIS3t7efPjhhxgMBtLT07l27Rrvv/8+vr6+\nzJ8/v1dZXl5eKIqCyWRi5MiRds+PEPcLCRiFECo3Nzc1eFy9ejUHDx5k3bp17Nu3j9TUVDXQs6V7\nRqinIUOG3JX25efns337dnVxZA8PD8aPH09QUBBGo7FXJvShhx76R/XFx8dTU1NDaWkpCxcupKio\nCGdnZ55//nmHjreVzQLw9vYGupYuun79OoDdiRIajYZr166pASP07pulHEsg15cbN24A8PXXX9ut\nqztb18+SNe0v+6woCjt37mTPnj1qG729vZk4cSKPPvooly5dUsswmUy4urravJe6930w/WhtbR1Q\n8LZ//346OjrUzGpBQQELFixQ1wqtqqri888/txkwWtp//fp1CRjF/wwJGIV4QLW2tpKQkEBYWBhb\nt261uc/ChQspKSnh+PHjXLlyxepR5/+3I0eOsH79ekJCQnjvvfcICwtTA7K0tDSMRuNdr3PWrFls\n3LiRkpIS4uLi+O6774iMjGT48OEOHX/r1i2b2y2Bk6enJ25ubgAsXbqU1NTUQbfVUo6txcdv376N\nk5MTzs7O6n579+5l8uTJg67PUbt372bbtm1MnjyZJUuWEBoayogRIwBYvHgxly5dUvfV6/U0Njby\n999/o9Vqrcrp+cafgfbDw8PD4bcGtba2kp2dzaJFi/D09MRkMnHt2jUCAgLUffz9/Tl16pTN4y3B\nrL1/sIS438gYRiEeUHq9ntbWViorK9U/cH3RarVqVuxesYwlzMjIYMaMGVbZu8uXLwODX/+vr9nZ\ner2emJgYTp8+TXl5OR0dHQ7Nju7eru6zoQHa29u5cOECoaGhODs7ExwcDNDn6/R27NhBTk6OugRS\nXyzl2Aqcd+/ezYQJE9Tlgvqq78aNG6Snp1NUVNR/52ywdR6/+uornJ2d2bFjB5GRkWqwqCgKly9f\nRqPRqNftiSeewGw2q4+bu+vZr4H2Y+TIkepYyf7k5uaiKAqvv/460LW0DmA1JvHWrVt93jcmkwmt\nVttnhlmI+5EEjEI8wJKSkmhra2PZsmW9JoxA11IkVVVVxMbG4u7ufg9a+B+WbE3Pd/V++eWX6li4\nwU4ysKzLZ2s8Xnx8PJ2dnWRkZKDX64mNjXW43I6ODrZs2aJ+VhSFjIwM2tvb1UeZvr6+PP300xgM\nBkpLS62OLyoq4qOPPsJgMKDT6ezWFRsbi6urK5999pnVRJSWlhby8/Nxd3dn/PjxPPvss+j1erKz\ns/nll1+syti8eTO5ubk0NDQ43MfubJ1HFxcXzGZzr/tr+/btajst1y0hIQGArVu3Wi1ndPLkSb79\n9lurAG2g/Rg7dizt7e39vi+8paWF3NxckpOT1cf7I0aMwMPDw2rIgNFoJDAwsNfxd+7c4dKlSwQG\nBvZ7zYS4n8gjaSEeYCkpKVy8eJHS0lJiY2OJjIzEz8+Pzs5OjEYjP/30E0FBQaxfv37QdQw269fz\n2Li4OL755hv1ndPu7u7U1tby448/MmXKFE6cOGG1GPhAWDJBmzdv5plnnrF6TWFkZCTe3t40NjYy\nf/78AY3H1Ol0FBQUUFdXx/jx49VzGhERoa5ZCbBhwwaSkpJYtmwZ06ZN4/HHH6e+vp7Kyko8PT1J\nS0vr87xYeHh4kJaWxtq1a5k3bx4xMTG4ublRUlJCc3MzmZmZ6HQ6dDodGzduZOXKlcybN4/Y2FhG\njhxJdXU1tbW1hIeHk5ycPNBTCMCoUaMAyMrK4uzZs7z11lvMnTsXo9HISy+9xMyZM9HpdJw6dYr6\n+nqeeuopfvjhB0wmE/7+/kyYMIEXX3yR/Px84uLiiIqKorm5mfLycoYNG4bJZFLfSDR06NAB9WP6\n9OkUFxdTU1NjdwHunJwcXFxcWLRokbpNo9GQkJBAbm4uWq2WlpYWzpw5Q0ZGRq/jL168yM2bN5ky\nZcqgzqEQ/1aSYRTiAabVatm2bRsff/wxkZGRnDlzhry8PA4dOoTZbGbFihUcPnzYanapRqPpd4Ht\n7mzt62gZ3feJjo5my5Yt+Pn5UVRUxKFDh/Dy8uLgwYPqG1kMBoNDZfasOykpialTp1JXV6dOdrBw\ncnJSs4qWpXYcpdfr2bNnD4qikJ+fz9WrV0lNTSU7O9uqDYGBgRw6dIgXXniBCxcukJeXx8WLF4mL\ni6OgoKDXEj59nbv4+Hg+/fRTQkNDKS0tpaCgAD8/P3bt2mWVGZ05cyb79u0jIiICg8HA/v37aWtr\nY+nSpezZs8fuBCZ7Zs+ezaxZs/jtt984cOAAjY2NJCUl8e677+Lp6cnBgwcpLi5m3LhxFBYWqssw\ndb9u69atY9WqVWg0Gg4cOEBdXR2rVq1SM7Ld2zaQfkRFRTFkyBCrmdY9NTU1sX//fpYsWdJrUtHy\n5ctJTEykrKyM2tpa3nnnHXUCTHfHjh0DcHhilBD3C43yT/79F0KIB0BiYiJ//fUXFRUVDh8zY8YM\n2tvb1SV/RP+amppwdnbG09Oz189Wr15NYWEhJ06ccHjSUU9paWkUFhZy7NixfmeTD9bs2bMZPnw4\n+/bt+6+UL8S9IhlGIYSww2AwYDQaWbBgwb1uyv+8wsJCIiIi1Hd7WzQ0NFBeXs7YsWMHHSwCvPHG\nG5jN5kFP6ulPTU0Nly9fJiUl5b9SvhD3kmQYhRDChk2bNlFTU8OFCxfw8PCgpKSEYcOGOXy8ZBgH\n7o8//mDOnDm0t7cTExODr68vTU1NlJWVYTabyc7OZtKkSf+ojoyMDIqLiykrK7tr64NavPLKK7i6\nurJz5867Wq4Q/waSYRRCCBt8fHyor69nzJgxZGVlDShYFIPj4+NDQUEBc+fOpba2lr1793L06FGi\noqLIz8//x8EiQGpqKu7u7uTl5d2FFv/H0aNHOX/+PBs2bLir5QrxbyEZRiGEEEIIYZdkGIUQQggh\nhF0SMAohhBBCCLskYBRCCCGEEHZJwCiEEEIIIeySgFEIIYQQQtglAaMQQgghhLDr/wB7K+jNvRFa\nuQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fd79d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_related_terms(u'timesnow')#punjabelection2017#timesnow#immigrant#syria#refugee#hillary#donaldtrump\n",
"data_dict = listToDict(word2vec.similar_by_word('timesnow'))\n",
"sns.set_style(\"darkgrid\")\n",
"plt.rc(\"font\", size=44)\n",
"plt.figure(figsize=(10,8),dpi=500) # does not affect the following plot\n",
"plt.xlabel(\"Similarity percentage(%)\",size=20)\n",
"plt.ylabel(\"Similar words\",size=20)\n",
"bar_plot = sns.barplot(x=data_dict.values(),y=data_dict.keys(),\n",
" palette=\"muted\",\n",
" x_order=data_dict.keys().sort(reverse=True))\n",
"plt.xticks(rotation=90)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 498,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"modi_ji 0.316\n",
"narendramodi_amitshah 0.316\n",
"bjpwins 0.294\n",
"congratulation_narendramodi_ji 0.294\n",
"narendramodi_ji 0.289\n",
"pm_narendramodi 0.288\n",
"bow 0.285\n",
"develop 0.281\n",
"nair_chennai_modi 0.278\n",
"har_har_modi 0.276\n"
]
},
{
"data": {
"image/png": 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JkycrISFBq1at0n333afw8HDXOvqLj3Gx4uJi+fn5XXUMAABAdWOYbk5BJicn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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113a055d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_related_terms(u'narendramodi')\n",
"data_dict = listToDict(word2vec.similar_by_word('narendramodi'))\n",
"sns.set_style(\"darkgrid\")\n",
"\n",
"plt.figure(figsize=(10,8),dpi=500) # does not affect the following plot\n",
"plt.xlabel(\"Similarity percentage(%)\",size=20)\n",
"plt.ylabel(\"Similar words\",size=20)\n",
"bar_plot = sns.barplot(x=data_dict.values(),y=data_dict.keys(),\n",
" palette=\"muted\",\n",
" x_order=data_dict.keys().sort(reverse=True))\n",
"plt.xticks(rotation=90)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 511,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"punjabpolls2017 0.378\n",
"goaelections2017 0.338\n",
"punjab 0.336\n",
"lambi 0.335\n",
"parkash_singh_badal 0.335\n",
"overconfidence 0.327\n",
"aamaadmipa_arvindkejriwal 0.307\n",
"delhites 0.283\n",
"cross_halfway_mark 0.279\n",
"wittyfeedlive 0.274\n"
]
},
{
"data": {
"image/png": 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Jibz99tsVFliSJEmqCGX+6LMGDRrQrVs3atWqRVRUFLm5uezYsYOcnJzyzCdJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11422e110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_related_terms(u'punjabelection2017')\n",
"data_dict = listToDict(word2vec.similar_by_word('punjabelection2017'))\n",
"sns.set_style(\"darkgrid\")\n",
"\n",
"plt.figure(figsize=(10,8),dpi=500) # does not affect the following plot\n",
"plt.xlabel(\"Similarity percentage(%)\",size=20)\n",
"plt.ylabel(\"Similar words\",size=20)\n",
"bar_plot = sns.barplot(x=data_dict.values(),y=data_dict.keys(),\n",
" palette=\"muted\",\n",
" x_order=data_dict.keys().sort(reverse=True))\n",
"plt.xticks(rotation=90)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 510,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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9vffee+d945NhGLxbHwAAAFV2Qe/W//DDD1W3bl21bNlSgYGBnswFAAAAG3K7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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114fd6350>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"muslim 0.404\n",
"hindus 0.395\n",
"dalit 0.351\n",
"dalits 0.35\n",
"muslim_vote 0.339\n",
"muslims 0.319\n",
"divide 0.303\n",
"liberal 0.302\n",
"muslim_woman 0.299\n",
"sehwag_karun_nair 0.299\n"
]
}
],
"source": [
"data_dict = listToDict(word2vec.similar_by_word('hindu'))\n",
"sns.set_style(\"darkgrid\")\n",
"\n",
"plt.figure(figsize=(10,8),dpi=500) # does not affect the following plot\n",
"plt.xlabel(\"Similarity percentage(%)\",size=20)\n",
"plt.ylabel(\"Similar words\",size=20)\n",
"bar_plot = sns.barplot(x=data_dict.values(),y=data_dict.keys(),\n",
" palette=\"muted\",\n",
" x_order=data_dict.keys().sort(reverse=True))\n",
"plt.xticks(rotation=90)\n",
"plt.show()\n",
"get_related_terms(u'hindu')"
]
},
{
"cell_type": "code",
"execution_count": 509,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"punjabpolls2017 0.378\n",
"goaelections2017 0.338\n",
"punjab 0.336\n",
"lambi 0.335\n",
"parkash_singh_badal 0.335\n",
"overconfidence 0.327\n",
"aamaadmipa_arvindkejriwal 0.307\n",
"delhites 0.283\n",
"cross_halfway_mark 0.279\n",
"wittyfeedlive 0.274\n"
]
},
{
"data": {
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Jibz99tsVFliSJEmqCGX+6LMGDRrQrVs3atWqRVRUFLm5uezYsYOcnJzyzCdJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1153e1510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_related_terms(u'punjabelection2017')\n",
"data_dict = listToDict(word2vec.similar_by_word('punjabelection2017'))\n",
"sns.set_style(\"darkgrid\")\n",
"\n",
"plt.figure(figsize=(10,8),dpi=500) # does not affect the following plot\n",
"plt.xlabel(\"Similarity percentage(%)\",size=20)\n",
"plt.ylabel(\"Similar words\",size=20)\n",
"bar_plot = sns.barplot(x=data_dict.values(),y=data_dict.keys(),\n",
" palette=\"muted\",\n",
" x_order=data_dict.keys().sort(reverse=True))\n",
"plt.xticks(rotation=90)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 512,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>...</th>\n",
" <th>190</th>\n",
" <th>191</th>\n",
" <th>192</th>\n",
" <th>193</th>\n",
" <th>194</th>\n",
" <th>195</th>\n",
" <th>196</th>\n",
" <th>197</th>\n",
" <th>198</th>\n",
" <th>199</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>electionresults</th>\n",
" <td>0.038593</td>\n",
" <td>0.065232</td>\n",
" <td>0.130983</td>\n",
" <td>0.009477</td>\n",
" <td>-0.018893</td>\n",
" <td>0.056132</td>\n",
" <td>-0.068405</td>\n",
" <td>-0.022548</td>\n",
" <td>0.030052</td>\n",
" <td>0.090498</td>\n",
" <td>...</td>\n",
" <td>0.121614</td>\n",
" <td>0.001951</td>\n",
" <td>-0.014015</td>\n",
" <td>0.001160</td>\n",
" <td>0.037758</td>\n",
" <td>-0.031155</td>\n",
" <td>-0.050242</td>\n",
" <td>0.150196</td>\n",
" <td>0.018113</td>\n",
" <td>0.100890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bjp</th>\n",
" <td>-0.023424</td>\n",
" <td>-0.023021</td>\n",
" <td>0.045584</td>\n",
" <td>0.026587</td>\n",
" <td>-0.013147</td>\n",
" <td>0.020020</td>\n",
" <td>0.009653</td>\n",
" <td>0.007908</td>\n",
" <td>-0.022853</td>\n",
" <td>-0.029902</td>\n",
" <td>...</td>\n",
" <td>0.009916</td>\n",
" <td>-0.029126</td>\n",
" <td>-0.028172</td>\n",
" <td>-0.123596</td>\n",
" <td>-0.107109</td>\n",
" <td>0.052286</td>\n",
" <td>-0.052555</td>\n",
" <td>0.083021</td>\n",
" <td>0.053085</td>\n",
" <td>-0.008552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>win</th>\n",
" <td>-0.029219</td>\n",
" <td>-0.085240</td>\n",
" <td>0.027453</td>\n",
" <td>0.104936</td>\n",
" <td>-0.063723</td>\n",
" <td>-0.030763</td>\n",
" <td>0.064524</td>\n",
" <td>0.036528</td>\n",
" <td>-0.090418</td>\n",
" <td>-0.146715</td>\n",
" <td>...</td>\n",
" <td>-0.056088</td>\n",
" <td>-0.013934</td>\n",
" <td>0.076431</td>\n",
" <td>-0.078700</td>\n",
" <td>0.039870</td>\n",
" <td>-0.044035</td>\n",
" <td>-0.071660</td>\n",
" <td>-0.020569</td>\n",
" <td>-0.039647</td>\n",
" <td>-0.033876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>congress</th>\n",
" <td>-0.126363</td>\n",
" <td>-0.194618</td>\n",
" <td>-0.027479</td>\n",
" <td>0.032964</td>\n",
" <td>0.062613</td>\n",
" <td>-0.019236</td>\n",
" <td>-0.083040</td>\n",
" <td>-0.039329</td>\n",
" <td>0.014001</td>\n",
" <td>-0.084960</td>\n",
" <td>...</td>\n",
" <td>0.055513</td>\n",
" <td>-0.140310</td>\n",
" <td>-0.007756</td>\n",
" <td>-0.077056</td>\n",
" <td>-0.116416</td>\n",
" <td>0.127231</td>\n",
" <td>-0.139047</td>\n",
" <td>0.052605</td>\n",
" <td>0.053434</td>\n",
" <td>0.040970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>punjab</th>\n",
" <td>0.068467</td>\n",
" <td>-0.142689</td>\n",
" <td>-0.019247</td>\n",
" <td>0.010518</td>\n",
" <td>0.061997</td>\n",
" <td>-0.067995</td>\n",
" <td>-0.042101</td>\n",
" <td>0.015361</td>\n",
" <td>-0.013578</td>\n",
" <td>-0.099104</td>\n",
" <td>...</td>\n",
" <td>0.036901</td>\n",
" <td>-0.032342</td>\n",
" <td>-0.135181</td>\n",
" <td>0.080569</td>\n",
" <td>-0.034288</td>\n",
" <td>0.066213</td>\n",
" <td>-0.100044</td>\n",
" <td>-0.097200</td>\n",
" <td>-0.098354</td>\n",
" <td>-0.074190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>upelection2017</th>\n",
" <td>-0.059415</td>\n",
" <td>0.119698</td>\n",
" <td>-0.072613</td>\n",
" <td>-0.007520</td>\n",
" <td>0.009200</td>\n",
" <td>-0.117717</td>\n",
" <td>-0.137367</td>\n",
" <td>-0.038348</td>\n",
" <td>-0.033626</td>\n",
" <td>0.014944</td>\n",
" <td>...</td>\n",
" <td>-0.046908</td>\n",
" <td>-0.078908</td>\n",
" <td>-0.026526</td>\n",
" <td>-0.028114</td>\n",
" <td>0.023593</td>\n",
" <td>-0.008405</td>\n",
" <td>-0.056531</td>\n",
" <td>-0.022312</td>\n",
" <td>0.137108</td>\n",
" <td>-0.029557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>elections2017</th>\n",
" <td>0.000885</td>\n",
" <td>0.026864</td>\n",
" <td>0.056395</td>\n",
" <td>0.138327</td>\n",
" <td>0.023747</td>\n",
" <td>0.029732</td>\n",
" <td>-0.053312</td>\n",
" <td>0.071559</td>\n",
" <td>0.082543</td>\n",
" <td>0.093932</td>\n",
" <td>...</td>\n",
" <td>0.001061</td>\n",
" <td>-0.116691</td>\n",
" <td>-0.008292</td>\n",
" <td>0.013324</td>\n",
" <td>-0.012990</td>\n",
" <td>-0.034696</td>\n",
" <td>0.019054</td>\n",
" <td>0.034339</td>\n",
" <td>0.017413</td>\n",
" <td>-0.008826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>modi</th>\n",
" <td>0.057329</td>\n",
" <td>-0.058722</td>\n",
" <td>0.004947</td>\n",
" <td>0.058037</td>\n",
" <td>-0.066921</td>\n",
" <td>0.039373</td>\n",
" <td>0.026097</td>\n",
" <td>0.093025</td>\n",
" <td>-0.026491</td>\n",
" <td>-0.111197</td>\n",
" <td>...</td>\n",
" <td>-0.092832</td>\n",
" <td>-0.038741</td>\n",
" <td>0.074647</td>\n",
" <td>-0.011324</td>\n",
" <td>0.005478</td>\n",
" <td>0.031353</td>\n",
" <td>-0.047724</td>\n",
" <td>0.129504</td>\n",
" <td>-0.035455</td>\n",
" <td>-0.027849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>narendramodi</th>\n",
" <td>0.106970</td>\n",
" <td>-0.034159</td>\n",
" <td>-0.134925</td>\n",
" <td>0.120943</td>\n",
" <td>0.086266</td>\n",
" <td>-0.051825</td>\n",
" <td>0.051399</td>\n",
" <td>-0.063423</td>\n",
" <td>0.027204</td>\n",
" <td>-0.016370</td>\n",
" <td>...</td>\n",
" <td>0.053611</td>\n",
" <td>-0.144650</td>\n",
" <td>0.122521</td>\n",
" <td>0.005819</td>\n",
" <td>0.079221</td>\n",
" <td>-0.095277</td>\n",
" <td>-0.014294</td>\n",
" <td>0.065366</td>\n",
" <td>-0.032062</td>\n",
" <td>-0.102929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>goa</th>\n",
" <td>0.009466</td>\n",
" <td>-0.100763</td>\n",
" <td>0.051813</td>\n",
" <td>0.074620</td>\n",
" <td>0.080515</td>\n",
" <td>0.001770</td>\n",
" <td>-0.064579</td>\n",
" <td>-0.023309</td>\n",
" <td>0.071276</td>\n",
" <td>-0.127215</td>\n",
" <td>...</td>\n",
" <td>0.124331</td>\n",
" <td>0.059266</td>\n",
" <td>0.043658</td>\n",
" <td>0.045934</td>\n",
" <td>-0.015065</td>\n",
" <td>0.115155</td>\n",
" <td>-0.079758</td>\n",
" <td>-0.007518</td>\n",
" <td>-0.048590</td>\n",
" <td>-0.028523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>people</th>\n",
" <td>-0.039698</td>\n",
" <td>0.033923</td>\n",
" <td>0.060276</td>\n",
" <td>0.024705</td>\n",
" <td>0.227086</td>\n",
" <td>-0.082467</td>\n",
" <td>0.060742</td>\n",
" <td>0.103884</td>\n",
" <td>0.048004</td>\n",
" <td>-0.050993</td>\n",
" <td>...</td>\n",
" <td>0.088357</td>\n",
" <td>-0.205868</td>\n",
" <td>-0.045787</td>\n",
" <td>0.040409</td>\n",
" <td>0.005570</td>\n",
" <td>-0.057884</td>\n",
" <td>0.020583</td>\n",
" <td>0.085420</td>\n",
" <td>0.004435</td>\n",
" <td>-0.060302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aap</th>\n",
" <td>-0.034824</td>\n",
" <td>-0.055105</td>\n",
" <td>-0.047506</td>\n",
" <td>-0.052748</td>\n",
" <td>-0.075673</td>\n",
" <td>0.051220</td>\n",
" <td>-0.109569</td>\n",
" <td>0.027053</td>\n",
" <td>-0.071330</td>\n",
" <td>-0.088910</td>\n",
" <td>...</td>\n",
" <td>0.015587</td>\n",
" <td>-0.078347</td>\n",
" <td>0.016722</td>\n",
" <td>-0.066571</td>\n",
" <td>0.074345</td>\n",
" <td>0.067832</td>\n",
" <td>-0.132459</td>\n",
" <td>-0.073835</td>\n",
" <td>0.010995</td>\n",
" <td>-0.058810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>seat</th>\n",
" <td>-0.095142</td>\n",
" <td>-0.048568</td>\n",
" <td>0.023420</td>\n",
" <td>0.027419</td>\n",
" <td>-0.042211</td>\n",
" <td>-0.006452</td>\n",
" <td>-0.189088</td>\n",
" <td>-0.023351</td>\n",
" <td>-0.084174</td>\n",
" <td>-0.091096</td>\n",
" <td>...</td>\n",
" <td>-0.062534</td>\n",
" <td>0.016595</td>\n",
" <td>-0.003928</td>\n",
" <td>-0.065256</td>\n",
" <td>-0.037860</td>\n",
" <td>0.039611</td>\n",
" <td>0.100247</td>\n",
" <td>0.079270</td>\n",
" <td>-0.122635</td>\n",
" <td>0.044122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bjp4india</th>\n",
" <td>0.063929</td>\n",
" <td>0.012793</td>\n",
" <td>0.003649</td>\n",
" <td>0.045386</td>\n",
" <td>-0.021476</td>\n",
" <td>-0.045998</td>\n",
" <td>-0.071840</td>\n",
" <td>0.001270</td>\n",
" <td>-0.026532</td>\n",
" <td>-0.012768</td>\n",
" <td>...</td>\n",
" <td>-0.010398</td>\n",
" <td>-0.088884</td>\n",
" <td>0.141554</td>\n",
" <td>0.016091</td>\n",
" <td>-0.060637</td>\n",
" <td>0.025861</td>\n",
" <td>-0.081247</td>\n",
" <td>-0.018354</td>\n",
" <td>0.020156</td>\n",
" <td>0.073299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>election</th>\n",
" <td>-0.126395</td>\n",
" <td>-0.036746</td>\n",
" <td>-0.027660</td>\n",
" <td>0.033241</td>\n",
" <td>-0.021149</td>\n",
" <td>0.051008</td>\n",
" <td>0.089375</td>\n",
" <td>-0.051359</td>\n",
" <td>0.039666</td>\n",
" <td>0.130272</td>\n",
" <td>...</td>\n",
" <td>0.155409</td>\n",
" <td>-0.030844</td>\n",
" <td>0.077666</td>\n",
" <td>0.041271</td>\n",
" <td>0.083457</td>\n",
" <td>-0.102640</td>\n",
" <td>-0.015524</td>\n",
" <td>-0.006577</td>\n",
" <td>-0.103896</td>\n",
" <td>-0.024485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vote</th>\n",
" <td>0.075080</td>\n",
" <td>-0.045672</td>\n",
" <td>-0.152598</td>\n",
" <td>0.034740</td>\n",
" <td>-0.083779</td>\n",
" <td>-0.012839</td>\n",
" <td>-0.108461</td>\n",
" <td>-0.018243</td>\n",
" <td>-0.035966</td>\n",
" <td>-0.130858</td>\n",
" <td>...</td>\n",
" <td>-0.025897</td>\n",
" <td>-0.079209</td>\n",
" <td>0.017629</td>\n",
" <td>-0.095567</td>\n",
" <td>-0.035544</td>\n",
" <td>0.061042</td>\n",
" <td>-0.082762</td>\n",
" <td>0.057972</td>\n",
" <td>0.025470</td>\n",
" <td>-0.090255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lead</th>\n",
" <td>-0.024408</td>\n",
" <td>-0.008424</td>\n",
" <td>-0.024544</td>\n",
" <td>0.032646</td>\n",
" <td>0.089608</td>\n",
" <td>-0.087065</td>\n",
" <td>-0.057443</td>\n",
" <td>0.050723</td>\n",
" <td>-0.010519</td>\n",
" <td>0.009008</td>\n",
" <td>...</td>\n",
" <td>-0.074429</td>\n",
" <td>0.012403</td>\n",
" <td>-0.012009</td>\n",
" <td>0.110392</td>\n",
" <td>0.006180</td>\n",
" <td>0.139540</td>\n",
" <td>0.070745</td>\n",
" <td>0.003855</td>\n",
" <td>0.031885</td>\n",
" <td>0.079737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>india</th>\n",
" <td>0.056050</td>\n",
" <td>-0.037646</td>\n",
" <td>-0.022806</td>\n",
" <td>0.002640</td>\n",
" <td>-0.039420</td>\n",
" <td>-0.032335</td>\n",
" <td>0.081846</td>\n",
" <td>-0.032084</td>\n",
" <td>0.061125</td>\n",
" <td>0.096432</td>\n",
" <td>...</td>\n",
" <td>0.041833</td>\n",
" <td>0.017394</td>\n",
" <td>0.065386</td>\n",
" <td>-0.028952</td>\n",
" <td>0.087125</td>\n",
" <td>0.075055</td>\n",
" <td>0.074320</td>\n",
" <td>0.108562</td>\n",
" <td>-0.174714</td>\n",
" <td>-0.030632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>punjabelection2017</th>\n",
" <td>0.062055</td>\n",
" <td>-0.013855</td>\n",
" <td>0.024407</td>\n",
" <td>0.080090</td>\n",
" <td>0.054993</td>\n",
" <td>0.025438</td>\n",
" <td>0.008611</td>\n",
" <td>-0.027822</td>\n",
" <td>-0.066533</td>\n",
" <td>0.073121</td>\n",
" <td>...</td>\n",
" <td>-0.040618</td>\n",
" <td>-0.082355</td>\n",
" <td>-0.053480</td>\n",
" <td>0.120564</td>\n",
" <td>-0.026865</td>\n",
" <td>-0.067068</td>\n",
" <td>-0.116497</td>\n",
" <td>0.066209</td>\n",
" <td>-0.019966</td>\n",
" <td>-0.025676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>not</th>\n",
" <td>0.099812</td>\n",
" <td>-0.013112</td>\n",
" <td>-0.012298</td>\n",
" <td>-0.009953</td>\n",
" <td>-0.066764</td>\n",
" <td>0.139616</td>\n",
" <td>-0.096990</td>\n",
" <td>0.075979</td>\n",
" <td>-0.012788</td>\n",
" <td>-0.060358</td>\n",
" <td>...</td>\n",
" <td>0.044464</td>\n",
" <td>-0.019485</td>\n",
" <td>0.100505</td>\n",
" <td>0.113914</td>\n",
" <td>-0.014448</td>\n",
" <td>-0.008090</td>\n",
" <td>-0.060805</td>\n",
" <td>-0.045079</td>\n",
" <td>-0.044406</td>\n",
" <td>-0.066812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>result</th>\n",
" <td>0.076679</td>\n",
" <td>0.002745</td>\n",
" <td>-0.034826</td>\n",
" <td>-0.092080</td>\n",
" <td>0.038041</td>\n",
" <td>-0.079494</td>\n",
" <td>-0.012775</td>\n",
" <td>-0.016264</td>\n",
" <td>0.168397</td>\n",
" <td>-0.070314</td>\n",
" <td>...</td>\n",
" <td>0.003131</td>\n",
" <td>-0.094888</td>\n",
" <td>-0.032183</td>\n",
" <td>0.007620</td>\n",
" <td>0.128673</td>\n",
" <td>-0.030522</td>\n",
" <td>-0.024016</td>\n",
" <td>0.050490</td>\n",
" <td>0.006241</td>\n",
" <td>0.159599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mayawati</th>\n",
" <td>0.136586</td>\n",
" <td>0.027942</td>\n",
" <td>-0.071640</td>\n",
" <td>0.061259</td>\n",
" <td>0.020730</td>\n",
" <td>-0.064309</td>\n",
" <td>-0.137225</td>\n",
" <td>0.052240</td>\n",
" <td>0.013239</td>\n",
" <td>-0.020661</td>\n",
" <td>...</td>\n",
" <td>0.029006</td>\n",
" <td>-0.164574</td>\n",
" <td>0.097879</td>\n",
" <td>-0.067564</td>\n",
" <td>-0.017508</td>\n",
" <td>-0.077931</td>\n",
" <td>-0.109366</td>\n",
" <td>-0.081635</td>\n",
" <td>-0.016363</td>\n",
" <td>0.110366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>today</th>\n",
" <td>-0.009684</td>\n",
" <td>0.053628</td>\n",
" <td>-0.114227</td>\n",
" <td>0.091992</td>\n",
" <td>0.116572</td>\n",
" <td>0.011550</td>\n",
" <td>0.022499</td>\n",
" <td>-0.042905</td>\n",
" <td>-0.036348</td>\n",
" <td>0.035779</td>\n",
" <td>...</td>\n",
" <td>0.036069</td>\n",
" <td>-0.140189</td>\n",
" <td>-0.006895</td>\n",
" <td>-0.050676</td>\n",
" <td>-0.021702</td>\n",
" <td>-0.035698</td>\n",
" <td>-0.116479</td>\n",
" <td>-0.003879</td>\n",
" <td>0.035651</td>\n",
" <td>0.042404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>manipur</th>\n",
" <td>0.023840</td>\n",
" <td>-0.017338</td>\n",
" <td>0.067993</td>\n",
" <td>0.001078</td>\n",
" <td>0.032815</td>\n",
" <td>0.016632</td>\n",
" <td>-0.102225</td>\n",
" <td>0.078326</td>\n",
" <td>0.078213</td>\n",
" <td>-0.024353</td>\n",
" <td>...</td>\n",
" <td>0.126637</td>\n",
" <td>-0.034347</td>\n",
" <td>0.095073</td>\n",
" <td>-0.051953</td>\n",
" <td>0.011405</td>\n",
" <td>0.033644</td>\n",
" <td>0.051250</td>\n",
" <td>-0.022853</td>\n",
" <td>-0.040544</td>\n",
" <td>-0.051701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <td>0.150224</td>\n",
" <td>0.007678</td>\n",
" <td>0.033943</td>\n",
" <td>-0.007928</td>\n",
" <td>0.163444</td>\n",
" <td>-0.045412</td>\n",
" <td>-0.135379</td>\n",
" <td>0.004417</td>\n",
" <td>0.028392</td>\n",
" <td>0.023189</td>\n",
" <td>...</td>\n",
" <td>-0.043129</td>\n",
" <td>0.057697</td>\n",
" <td>-0.050728</td>\n",
" <td>0.120806</td>\n",
" <td>-0.033045</td>\n",
" <td>-0.021622</td>\n",
" <td>0.103954</td>\n",
" <td>0.084632</td>\n",
" <td>-0.077904</td>\n",
" <td>0.066601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>arvindkejriwal</th>\n",
" <td>-0.054333</td>\n",
" <td>-0.004763</td>\n",
" <td>0.124946</td>\n",
" <td>-0.072454</td>\n",
" <td>0.002282</td>\n",
" <td>0.003564</td>\n",
" <td>0.051566</td>\n",
" <td>0.025033</td>\n",
" <td>-0.040877</td>\n",
" <td>0.005556</td>\n",
" <td>...</td>\n",
" <td>-0.023419</td>\n",
" <td>-0.003268</td>\n",
" <td>-0.030896</td>\n",
" <td>-0.032492</td>\n",
" <td>-0.003914</td>\n",
" <td>0.006729</td>\n",
" <td>-0.066372</td>\n",
" <td>0.001733</td>\n",
" <td>0.055826</td>\n",
" <td>0.070511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>state</th>\n",
" <td>-0.059632</td>\n",
" <td>0.005639</td>\n",
" <td>-0.103978</td>\n",
" <td>-0.023545</td>\n",
" <td>0.048953</td>\n",
" <td>0.029854</td>\n",
" <td>0.023474</td>\n",
" <td>0.024836</td>\n",
" <td>0.023378</td>\n",
" <td>-0.115147</td>\n",
" <td>...</td>\n",
" <td>0.038821</td>\n",
" <td>-0.017290</td>\n",
" <td>-0.040165</td>\n",
" <td>-0.105001</td>\n",
" <td>0.025496</td>\n",
" <td>0.088523</td>\n",
" <td>-0.081328</td>\n",
" <td>-0.016549</td>\n",
" <td>-0.173032</td>\n",
" <td>-0.081411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>say</th>\n",
" <td>0.063084</td>\n",
" <td>-0.086668</td>\n",
" <td>0.015514</td>\n",
" <td>0.043112</td>\n",
" <td>0.087299</td>\n",
" <td>0.002264</td>\n",
" <td>-0.047222</td>\n",
" <td>0.047205</td>\n",
" <td>0.127651</td>\n",
" <td>-0.022330</td>\n",
" <td>...</td>\n",
" <td>-0.068483</td>\n",
" <td>-0.009658</td>\n",
" <td>0.015862</td>\n",
" <td>-0.004461</td>\n",
" <td>0.033136</td>\n",
" <td>-0.109790</td>\n",
" <td>-0.044600</td>\n",
" <td>0.094797</td>\n",
" <td>-0.002640</td>\n",
" <td>-0.035027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>uttarpradesh</th>\n",
" <td>0.043367</td>\n",
" <td>0.054092</td>\n",
" <td>-0.053679</td>\n",
" <td>0.115545</td>\n",
" <td>0.025056</td>\n",
" <td>-0.068364</td>\n",
" <td>-0.123933</td>\n",
" <td>-0.061981</td>\n",
" <td>0.108704</td>\n",
" <td>-0.014090</td>\n",
" <td>...</td>\n",
" <td>0.186138</td>\n",
" <td>-0.007268</td>\n",
" <td>0.017787</td>\n",
" <td>0.034278</td>\n",
" <td>0.023755</td>\n",
" <td>-0.060320</td>\n",
" <td>0.089871</td>\n",
" <td>-0.001383</td>\n",
" <td>-0.010562</td>\n",
" <td>-0.110586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>victory</th>\n",
" <td>0.179940</td>\n",
" <td>0.007479</td>\n",
" <td>0.029669</td>\n",
" <td>0.004491</td>\n",
" <td>0.055226</td>\n",
" <td>-0.059822</td>\n",
" <td>-0.132920</td>\n",
" <td>-0.075399</td>\n",
" <td>0.091894</td>\n",
" <td>-0.033675</td>\n",
" <td>...</td>\n",
" <td>-0.016780</td>\n",
" <td>-0.065449</td>\n",
" <td>-0.040673</td>\n",
" <td>0.051611</td>\n",
" <td>-0.139144</td>\n",
" <td>-0.125057</td>\n",
" <td>-0.054146</td>\n",
" <td>-0.006119</td>\n",
" <td>-0.056002</td>\n",
" <td>-0.026770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rajdeep</th>\n",
" <td>0.000572</td>\n",
" <td>-0.139684</td>\n",
" <td>0.067499</td>\n",
" <td>0.025494</td>\n",
" <td>0.085057</td>\n",
" <td>-0.012667</td>\n",
" <td>-0.131865</td>\n",
" <td>0.087055</td>\n",
" <td>0.013715</td>\n",
" <td>0.022383</td>\n",
" <td>...</td>\n",
" <td>-0.073607</td>\n",
" <td>0.017322</td>\n",
" <td>-0.078839</td>\n",
" <td>0.008494</td>\n",
" <td>0.011737</td>\n",
" <td>-0.159414</td>\n",
" <td>0.009522</td>\n",
" <td>0.037300</td>\n",
" <td>0.007489</td>\n",
" <td>-0.001320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>increase</th>\n",
" <td>0.002741</td>\n",
" <td>-0.042367</td>\n",
" <td>0.048650</td>\n",
" <td>0.094575</td>\n",
" <td>-0.059034</td>\n",
" <td>-0.103580</td>\n",
" <td>0.038335</td>\n",
" <td>-0.145551</td>\n",
" <td>0.011547</td>\n",
" <td>-0.051403</td>\n",
" <td>...</td>\n",
" <td>-0.051460</td>\n",
" <td>-0.036290</td>\n",
" <td>0.012811</td>\n",
" <td>-0.006737</td>\n",
" <td>-0.007264</td>\n",
" <td>-0.109139</td>\n",
" <td>0.025313</td>\n",
" <td>0.151042</td>\n",
" <td>0.004530</td>\n",
" <td>0.134056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hindutva</th>\n",
" <td>0.043134</td>\n",
" <td>0.088895</td>\n",
" <td>-0.081953</td>\n",
" <td>-0.001649</td>\n",
" <td>-0.116976</td>\n",
" <td>0.035731</td>\n",
" <td>0.017499</td>\n",
" <td>-0.129806</td>\n",
" <td>0.044985</td>\n",
" <td>-0.070479</td>\n",
" <td>...</td>\n",
" <td>-0.069209</td>\n",
" <td>-0.179267</td>\n",
" <td>0.052250</td>\n",
" <td>-0.065992</td>\n",
" <td>-0.040703</td>\n",
" <td>0.034155</td>\n",
" <td>-0.075206</td>\n",
" <td>0.018361</td>\n",
" <td>-0.098286</td>\n",
" <td>0.109001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>exactly</th>\n",
" <td>0.019895</td>\n",
" <td>-0.035512</td>\n",
" <td>-0.026814</td>\n",
" <td>0.101532</td>\n",
" <td>-0.069336</td>\n",
" <td>-0.000572</td>\n",
" <td>-0.109778</td>\n",
" <td>-0.036733</td>\n",
" <td>-0.033457</td>\n",
" <td>-0.011201</td>\n",
" <td>...</td>\n",
" <td>0.050013</td>\n",
" <td>-0.019603</td>\n",
" <td>0.122286</td>\n",
" <td>-0.107954</td>\n",
" <td>0.008353</td>\n",
" <td>-0.015783</td>\n",
" <td>0.018979</td>\n",
" <td>0.106707</td>\n",
" <td>0.028863</td>\n",
" <td>0.004645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>expe</th>\n",
" <td>0.015386</td>\n",
" <td>-0.031245</td>\n",
" <td>0.009943</td>\n",
" <td>-0.059035</td>\n",
" <td>0.027866</td>\n",
" <td>0.008620</td>\n",
" <td>-0.002010</td>\n",
" <td>-0.058105</td>\n",
" <td>0.094351</td>\n",
" <td>-0.096780</td>\n",
" <td>...</td>\n",
" <td>-0.019896</td>\n",
" <td>-0.161666</td>\n",
" <td>0.106552</td>\n",
" <td>-0.072747</td>\n",
" <td>-0.009588</td>\n",
" <td>-0.058226</td>\n",
" <td>0.036806</td>\n",
" <td>0.043856</td>\n",
" <td>0.046624</td>\n",
" <td>-0.082276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>self</th>\n",
" <td>-0.057144</td>\n",
" <td>-0.078644</td>\n",
" <td>-0.049909</td>\n",
" <td>0.024160</td>\n",
" <td>-0.089639</td>\n",
" <td>-0.075370</td>\n",
" <td>-0.162126</td>\n",
" <td>-0.028731</td>\n",
" <td>0.121647</td>\n",
" <td>-0.021950</td>\n",
" <td>...</td>\n",
" <td>-0.037909</td>\n",
" <td>-0.203708</td>\n",
" <td>0.018260</td>\n",
" <td>0.070127</td>\n",
" <td>0.048522</td>\n",
" <td>0.013464</td>\n",
" <td>-0.021706</td>\n",
" <td>0.034909</td>\n",
" <td>0.014207</td>\n",
" <td>0.105151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>line</th>\n",
" <td>0.026982</td>\n",
" <td>0.008358</td>\n",
" <td>0.013785</td>\n",
" <td>0.024504</td>\n",
" <td>-0.037684</td>\n",
" <td>-0.058826</td>\n",
" <td>0.019199</td>\n",
" <td>-0.077528</td>\n",
" <td>0.039583</td>\n",
" <td>-0.073356</td>\n",
" <td>...</td>\n",
" <td>0.047194</td>\n",
" <td>-0.123512</td>\n",
" <td>0.004382</td>\n",
" <td>0.040966</td>\n",
" <td>-0.110072</td>\n",
" <td>0.064913</td>\n",
" <td>0.030666</td>\n",
" <td>-0.032984</td>\n",
" <td>-0.091194</td>\n",
" <td>-0.021220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cross_halfway_mark</th>\n",
" <td>-0.021558</td>\n",
" <td>-0.061889</td>\n",
" <td>0.033273</td>\n",
" <td>0.096349</td>\n",
" <td>0.062784</td>\n",
" <td>-0.010625</td>\n",
" <td>-0.013493</td>\n",
" <td>-0.020203</td>\n",
" <td>-0.022609</td>\n",
" <td>-0.030026</td>\n",
" <td>...</td>\n",
" <td>-0.007541</td>\n",
" <td>0.026947</td>\n",
" <td>-0.096860</td>\n",
" <td>0.067924</td>\n",
" <td>0.078989</td>\n",
" <td>0.056889</td>\n",
" <td>-0.085800</td>\n",
" <td>0.032944</td>\n",
" <td>-0.028633</td>\n",
" <td>0.013812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>congratulation_sir</th>\n",
" <td>0.013797</td>\n",
" <td>0.018192</td>\n",
" <td>0.027101</td>\n",
" <td>-0.015782</td>\n",
" <td>-0.023544</td>\n",
" <td>-0.026581</td>\n",
" <td>-0.022060</td>\n",
" <td>-0.047410</td>\n",
" <td>0.028588</td>\n",
" <td>-0.094107</td>\n",
" <td>...</td>\n",
" <td>0.016904</td>\n",
" <td>-0.033561</td>\n",
" <td>-0.033346</td>\n",
" <td>-0.082044</td>\n",
" <td>0.026074</td>\n",
" <td>-0.009851</td>\n",
" <td>-0.008612</td>\n",
" <td>0.000366</td>\n",
" <td>0.029629</td>\n",
" <td>-0.084629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>local</th>\n",
" <td>0.057909</td>\n",
" <td>-0.050637</td>\n",
" <td>0.021727</td>\n",
" <td>0.001402</td>\n",
" <td>-0.073308</td>\n",
" <td>-0.029511</td>\n",
" <td>0.019499</td>\n",
" <td>0.020546</td>\n",
" <td>0.043304</td>\n",
" <td>0.016321</td>\n",
" <td>...</td>\n",
" <td>0.000168</td>\n",
" <td>-0.075014</td>\n",
" <td>0.142310</td>\n",
" <td>-0.134372</td>\n",
" <td>-0.013498</td>\n",
" <td>-0.073779</td>\n",
" <td>0.000324</td>\n",
" <td>0.027341</td>\n",
" <td>-0.099459</td>\n",
" <td>-0.045796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>expectation</th>\n",
" <td>0.042574</td>\n",
" <td>-0.141244</td>\n",
" <td>-0.075892</td>\n",
" <td>-0.046158</td>\n",
" <td>-0.005300</td>\n",
" <td>0.029256</td>\n",
" <td>0.008429</td>\n",
" <td>-0.013683</td>\n",
" <td>-0.041414</td>\n",
" <td>0.020637</td>\n",
" <td>...</td>\n",
" <td>0.095484</td>\n",
" <td>-0.133989</td>\n",
" <td>0.011314</td>\n",
" <td>0.082593</td>\n",
" <td>-0.075108</td>\n",
" <td>0.012156</td>\n",
" <td>-0.006141</td>\n",
" <td>0.087376</td>\n",
" <td>-0.027974</td>\n",
" <td>0.045746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>common</th>\n",
" <td>0.138926</td>\n",
" <td>0.067191</td>\n",
" <td>-0.147634</td>\n",
" <td>0.047569</td>\n",
" <td>0.026127</td>\n",
" <td>-0.048608</td>\n",
" <td>0.100050</td>\n",
" <td>0.124762</td>\n",
" <td>0.005328</td>\n",
" <td>-0.070907</td>\n",
" <td>...</td>\n",
" <td>-0.032742</td>\n",
" <td>-0.042576</td>\n",
" <td>0.047057</td>\n",
" <td>0.079273</td>\n",
" <td>-0.051876</td>\n",
" <td>0.094442</td>\n",
" <td>0.017851</td>\n",
" <td>0.039200</td>\n",
" <td>-0.020738</td>\n",
" <td>-0.049305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ban</th>\n",
" <td>-0.060220</td>\n",
" <td>-0.115585</td>\n",
" <td>0.112577</td>\n",
" <td>0.069342</td>\n",
" <td>0.031616</td>\n",
" <td>0.008764</td>\n",
" <td>-0.058356</td>\n",
" <td>-0.030033</td>\n",
" <td>-0.130406</td>\n",
" <td>0.011818</td>\n",
" <td>...</td>\n",
" <td>-0.003338</td>\n",
" <td>-0.073826</td>\n",
" <td>-0.143346</td>\n",
" <td>-0.046458</td>\n",
" <td>0.145053</td>\n",
" <td>0.019136</td>\n",
" <td>0.103405</td>\n",
" <td>-0.070201</td>\n",
" <td>-0.032085</td>\n",
" <td>-0.178364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>electoral</th>\n",
" <td>0.007523</td>\n",
" <td>-0.117843</td>\n",
" <td>0.048318</td>\n",
" <td>0.093463</td>\n",
" <td>0.041712</td>\n",
" <td>-0.089972</td>\n",
" <td>0.002510</td>\n",
" <td>0.016579</td>\n",
" <td>-0.006702</td>\n",
" <td>0.024066</td>\n",
" <td>...</td>\n",
" <td>0.100980</td>\n",
" <td>0.001423</td>\n",
" <td>0.025170</td>\n",
" <td>-0.044179</td>\n",
" <td>-0.101104</td>\n",
" <td>-0.038782</td>\n",
" <td>-0.021458</td>\n",
" <td>0.031113</td>\n",
" <td>-0.056363</td>\n",
" <td>-0.074877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coz</th>\n",
" <td>0.009905</td>\n",
" <td>0.002019</td>\n",
" <td>-0.046259</td>\n",
" <td>0.022158</td>\n",
" <td>0.047524</td>\n",
" <td>-0.053648</td>\n",
" <td>0.036422</td>\n",
" <td>0.055437</td>\n",
" <td>0.015699</td>\n",
" <td>-0.079241</td>\n",
" <td>...</td>\n",
" <td>-0.092309</td>\n",
" <td>-0.150016</td>\n",
" <td>0.044821</td>\n",
" <td>-0.017710</td>\n",
" <td>0.036217</td>\n",
" <td>-0.022037</td>\n",
" <td>0.022665</td>\n",
" <td>-0.063585</td>\n",
" <td>0.001722</td>\n",
" <td>0.014351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>laugh</th>\n",
" <td>-0.098381</td>\n",
" <td>-0.096486</td>\n",
" <td>0.019137</td>\n",
" <td>-0.014487</td>\n",
" <td>0.097855</td>\n",
" <td>-0.050768</td>\n",
" <td>0.045400</td>\n",
" <td>0.014776</td>\n",
" <td>-0.040167</td>\n",
" <td>-0.104711</td>\n",
" <td>...</td>\n",
" <td>-0.152082</td>\n",
" <td>-0.116488</td>\n",
" <td>0.010465</td>\n",
" <td>0.003475</td>\n",
" <td>0.025192</td>\n",
" <td>0.099849</td>\n",
" <td>-0.046368</td>\n",
" <td>0.057842</td>\n",
" <td>-0.058274</td>\n",
" <td>0.123511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>near</th>\n",
" <td>-0.074969</td>\n",
" <td>0.105880</td>\n",
" <td>-0.048510</td>\n",
" <td>-0.084780</td>\n",
" <td>0.131893</td>\n",
" <td>-0.060494</td>\n",
" <td>0.077748</td>\n",
" <td>-0.040068</td>\n",
" <td>0.038564</td>\n",
" <td>-0.075247</td>\n",
" <td>...</td>\n",
" <td>0.028618</td>\n",
" <td>-0.065068</td>\n",
" <td>-0.015515</td>\n",
" <td>0.052545</td>\n",
" <td>-0.042279</td>\n",
" <td>0.035203</td>\n",
" <td>-0.107615</td>\n",
" <td>0.021339</td>\n",
" <td>-0.013617</td>\n",
" <td>0.113182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>develop</th>\n",
" <td>-0.055676</td>\n",
" <td>0.022227</td>\n",
" <td>-0.016562</td>\n",
" <td>-0.052855</td>\n",
" <td>-0.009292</td>\n",
" <td>0.006036</td>\n",
" <td>0.123389</td>\n",
" <td>0.055140</td>\n",
" <td>0.066402</td>\n",
" <td>-0.050526</td>\n",
" <td>...</td>\n",
" <td>-0.015021</td>\n",
" <td>-0.088237</td>\n",
" <td>-0.000258</td>\n",
" <td>-0.005249</td>\n",
" <td>0.016249</td>\n",
" <td>-0.088044</td>\n",
" <td>0.070216</td>\n",
" <td>0.031028</td>\n",
" <td>0.042927</td>\n",
" <td>-0.113180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>perform</th>\n",
" <td>0.068399</td>\n",
" <td>-0.109243</td>\n",
" <td>0.087984</td>\n",
" <td>-0.089746</td>\n",
" <td>0.076768</td>\n",
" <td>-0.065495</td>\n",
" <td>-0.050097</td>\n",
" <td>-0.026354</td>\n",
" <td>0.019051</td>\n",
" <td>0.089457</td>\n",
" <td>...</td>\n",
" <td>0.134925</td>\n",
" <td>-0.121449</td>\n",
" <td>0.021458</td>\n",
" <td>-0.209780</td>\n",
" <td>0.114002</td>\n",
" <td>-0.062772</td>\n",
" <td>0.048758</td>\n",
" <td>-0.093813</td>\n",
" <td>0.025514</td>\n",
" <td>0.063813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kyaukhaadlega</th>\n",
" <td>-0.053153</td>\n",
" <td>-0.041528</td>\n",
" <td>-0.060244</td>\n",
" <td>-0.066774</td>\n",
" <td>0.064793</td>\n",
" <td>-0.006858</td>\n",
" <td>-0.046686</td>\n",
" <td>0.045093</td>\n",
" <td>-0.011661</td>\n",
" <td>0.116930</td>\n",
" <td>...</td>\n",
" <td>0.013920</td>\n",
" <td>0.000936</td>\n",
" <td>-0.019011</td>\n",
" <td>-0.019647</td>\n",
" <td>0.110923</td>\n",
" <td>-0.064272</td>\n",
" <td>-0.044606</td>\n",
" <td>0.050543</td>\n",
" <td>0.076061</td>\n",
" <td>-0.017054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build</th>\n",
" <td>0.073477</td>\n",
" <td>-0.027803</td>\n",
" <td>0.116254</td>\n",
" <td>-0.215213</td>\n",
" <td>0.008742</td>\n",
" <td>-0.063480</td>\n",
" <td>-0.046682</td>\n",
" <td>0.078327</td>\n",
" <td>0.045782</td>\n",
" <td>-0.041363</td>\n",
" <td>...</td>\n",
" <td>-0.030358</td>\n",
" <td>0.002865</td>\n",
" <td>0.046920</td>\n",
" <td>-0.159242</td>\n",
" <td>-0.055738</td>\n",
" <td>-0.115379</td>\n",
" <td>-0.079165</td>\n",
" <td>0.044054</td>\n",
" <td>0.007809</td>\n",
" <td>0.007130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dat</th>\n",
" <td>-0.041859</td>\n",
" <td>0.051822</td>\n",
" <td>0.077876</td>\n",
" <td>-0.056574</td>\n",
" <td>0.038935</td>\n",
" <td>-0.051600</td>\n",
" <td>-0.016748</td>\n",
" <td>0.043907</td>\n",
" <td>0.000600</td>\n",
" <td>0.010083</td>\n",
" <td>...</td>\n",
" <td>-0.002709</td>\n",
" <td>-0.085736</td>\n",
" <td>0.098313</td>\n",
" <td>0.013040</td>\n",
" <td>-0.059380</td>\n",
" <td>-0.056215</td>\n",
" <td>-0.025118</td>\n",
" <td>0.088603</td>\n",
" <td>-0.123315</td>\n",
" <td>-0.032688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>thought</th>\n",
" <td>-0.020270</td>\n",
" <td>-0.003915</td>\n",
" <td>0.027130</td>\n",
" <td>-0.022510</td>\n",
" <td>-0.001879</td>\n",
" <td>0.037204</td>\n",
" <td>0.045211</td>\n",
" <td>0.150274</td>\n",
" <td>0.036449</td>\n",
" <td>0.017040</td>\n",
" <td>...</td>\n",
" <td>0.058931</td>\n",
" <td>-0.127265</td>\n",
" <td>0.015879</td>\n",
" <td>0.033426</td>\n",
" <td>0.006267</td>\n",
" <td>-0.046305</td>\n",
" <td>-0.020966</td>\n",
" <td>0.027727</td>\n",
" <td>0.100224</td>\n",
" <td>-0.067964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rahul_akhilesh</th>\n",
" <td>-0.114333</td>\n",
" <td>0.011766</td>\n",
" <td>-0.054500</td>\n",
" <td>0.037149</td>\n",
" <td>-0.012070</td>\n",
" <td>0.037191</td>\n",
" <td>-0.046341</td>\n",
" <td>-0.076965</td>\n",
" <td>-0.035298</td>\n",
" <td>-0.118777</td>\n",
" <td>...</td>\n",
" <td>-0.026322</td>\n",
" <td>-0.137221</td>\n",
" <td>0.111736</td>\n",
" <td>0.074768</td>\n",
" <td>0.085226</td>\n",
" <td>-0.041079</td>\n",
" <td>-0.095149</td>\n",
" <td>0.068541</td>\n",
" <td>0.155929</td>\n",
" <td>0.020803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>shekhargupta</th>\n",
" <td>0.096427</td>\n",
" <td>0.035468</td>\n",
" <td>0.030736</td>\n",
" <td>0.017017</td>\n",
" <td>0.049494</td>\n",
" <td>-0.008830</td>\n",
" <td>-0.063456</td>\n",
" <td>0.075355</td>\n",
" <td>-0.043478</td>\n",
" <td>-0.008048</td>\n",
" <td>...</td>\n",
" <td>0.018096</td>\n",
" <td>-0.034190</td>\n",
" <td>0.105129</td>\n",
" <td>0.009823</td>\n",
" <td>0.049082</td>\n",
" <td>-0.128055</td>\n",
" <td>-0.097915</td>\n",
" <td>-0.006912</td>\n",
" <td>0.039343</td>\n",
" <td>-0.051636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dalits</th>\n",
" <td>-0.022647</td>\n",
" <td>0.015133</td>\n",
" <td>-0.022348</td>\n",
" <td>0.008382</td>\n",
" <td>-0.038843</td>\n",
" <td>-0.024790</td>\n",
" <td>-0.039074</td>\n",
" <td>0.018119</td>\n",
" <td>-0.089860</td>\n",
" <td>0.007483</td>\n",
" <td>...</td>\n",
" <td>-0.064357</td>\n",
" <td>0.021281</td>\n",
" <td>0.003567</td>\n",
" <td>-0.046159</td>\n",
" <td>0.094281</td>\n",
" <td>0.001393</td>\n",
" <td>0.056063</td>\n",
" <td>0.018718</td>\n",
" <td>-0.058261</td>\n",
" <td>0.053186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tally</th>\n",
" <td>-0.050746</td>\n",
" <td>0.053442</td>\n",
" <td>0.013369</td>\n",
" <td>-0.162591</td>\n",
" <td>0.016935</td>\n",
" <td>-0.025198</td>\n",
" <td>0.012515</td>\n",
" <td>0.051626</td>\n",
" <td>0.018235</td>\n",
" <td>0.040107</td>\n",
" <td>...</td>\n",
" <td>-0.071195</td>\n",
" <td>0.006373</td>\n",
" <td>0.016522</td>\n",
" <td>-0.104566</td>\n",
" <td>-0.033155</td>\n",
" <td>0.031846</td>\n",
" <td>0.006604</td>\n",
" <td>0.009546</td>\n",
" <td>-0.051816</td>\n",
" <td>0.030673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ind</th>\n",
" <td>-0.189764</td>\n",
" <td>-0.030724</td>\n",
" <td>-0.060189</td>\n",
" <td>-0.027609</td>\n",
" <td>-0.113397</td>\n",
" <td>0.035910</td>\n",
" <td>-0.085639</td>\n",
" <td>-0.028148</td>\n",
" <td>-0.065998</td>\n",
" <td>0.040376</td>\n",
" <td>...</td>\n",
" <td>0.058363</td>\n",
" <td>0.067316</td>\n",
" <td>-0.065128</td>\n",
" <td>-0.092801</td>\n",
" <td>-0.077179</td>\n",
" <td>0.095876</td>\n",
" <td>-0.007619</td>\n",
" <td>0.040394</td>\n",
" <td>0.089587</td>\n",
" <td>0.057230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>repeat</th>\n",
" <td>-0.038974</td>\n",
" <td>-0.001742</td>\n",
" <td>0.160014</td>\n",
" <td>-0.114607</td>\n",
" <td>-0.075890</td>\n",
" <td>-0.013283</td>\n",
" <td>-0.051917</td>\n",
" <td>0.064910</td>\n",
" <td>-0.001597</td>\n",
" <td>0.004153</td>\n",
" <td>...</td>\n",
" <td>-0.052163</td>\n",
" <td>-0.045921</td>\n",
" <td>0.041476</td>\n",
" <td>-0.002198</td>\n",
" <td>-0.067260</td>\n",
" <td>-0.050876</td>\n",
" <td>0.046228</td>\n",
" <td>0.014761</td>\n",
" <td>0.045161</td>\n",
" <td>-0.050687</td>\n",
" </tr>\n",
" <tr>\n",
" <th>narrative</th>\n",
" <td>0.012858</td>\n",
" <td>0.100006</td>\n",
" <td>0.079361</td>\n",
" <td>-0.106948</td>\n",
" <td>0.019481</td>\n",
" <td>0.022624</td>\n",
" <td>-0.058684</td>\n",
" <td>-0.002876</td>\n",
" <td>0.096466</td>\n",
" <td>-0.002182</td>\n",
" <td>...</td>\n",
" <td>-0.030939</td>\n",
" <td>-0.019613</td>\n",
" <td>0.021866</td>\n",
" <td>0.001329</td>\n",
" <td>0.012298</td>\n",
" <td>-0.108745</td>\n",
" <td>0.039810</td>\n",
" <td>0.005657</td>\n",
" <td>0.085686</td>\n",
" <td>-0.092077</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1007 rows × 200 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 \\\n",
"electionresults 0.038593 0.065232 0.130983 0.009477 -0.018893 \n",
"bjp -0.023424 -0.023021 0.045584 0.026587 -0.013147 \n",
"win -0.029219 -0.085240 0.027453 0.104936 -0.063723 \n",
"congress -0.126363 -0.194618 -0.027479 0.032964 0.062613 \n",
"punjab 0.068467 -0.142689 -0.019247 0.010518 0.061997 \n",
"upelection2017 -0.059415 0.119698 -0.072613 -0.007520 0.009200 \n",
"elections2017 0.000885 0.026864 0.056395 0.138327 0.023747 \n",
"modi 0.057329 -0.058722 0.004947 0.058037 -0.066921 \n",
"narendramodi 0.106970 -0.034159 -0.134925 0.120943 0.086266 \n",
"goa 0.009466 -0.100763 0.051813 0.074620 0.080515 \n",
"people -0.039698 0.033923 0.060276 0.024705 0.227086 \n",
"aap -0.034824 -0.055105 -0.047506 -0.052748 -0.075673 \n",
"seat -0.095142 -0.048568 0.023420 0.027419 -0.042211 \n",
"bjp4india 0.063929 0.012793 0.003649 0.045386 -0.021476 \n",
"election -0.126395 -0.036746 -0.027660 0.033241 -0.021149 \n",
"vote 0.075080 -0.045672 -0.152598 0.034740 -0.083779 \n",
"lead -0.024408 -0.008424 -0.024544 0.032646 0.089608 \n",
"india 0.056050 -0.037646 -0.022806 0.002640 -0.039420 \n",
"punjabelection2017 0.062055 -0.013855 0.024407 0.080090 0.054993 \n",
"not 0.099812 -0.013112 -0.012298 -0.009953 -0.066764 \n",
"result 0.076679 0.002745 -0.034826 -0.092080 0.038041 \n",
"mayawati 0.136586 0.027942 -0.071640 0.061259 0.020730 \n",
"today -0.009684 0.053628 -0.114227 0.091992 0.116572 \n",
"manipur 0.023840 -0.017338 0.067993 0.001078 0.032815 \n",
"time 0.150224 0.007678 0.033943 -0.007928 0.163444 \n",
"arvindkejriwal -0.054333 -0.004763 0.124946 -0.072454 0.002282 \n",
"state -0.059632 0.005639 -0.103978 -0.023545 0.048953 \n",
"say 0.063084 -0.086668 0.015514 0.043112 0.087299 \n",
"uttarpradesh 0.043367 0.054092 -0.053679 0.115545 0.025056 \n",
"victory 0.179940 0.007479 0.029669 0.004491 0.055226 \n",
"... ... ... ... ... ... \n",
"rajdeep 0.000572 -0.139684 0.067499 0.025494 0.085057 \n",
"increase 0.002741 -0.042367 0.048650 0.094575 -0.059034 \n",
"hindutva 0.043134 0.088895 -0.081953 -0.001649 -0.116976 \n",
"exactly 0.019895 -0.035512 -0.026814 0.101532 -0.069336 \n",
"expe 0.015386 -0.031245 0.009943 -0.059035 0.027866 \n",
"self -0.057144 -0.078644 -0.049909 0.024160 -0.089639 \n",
"line 0.026982 0.008358 0.013785 0.024504 -0.037684 \n",
"cross_halfway_mark -0.021558 -0.061889 0.033273 0.096349 0.062784 \n",
"congratulation_sir 0.013797 0.018192 0.027101 -0.015782 -0.023544 \n",
"local 0.057909 -0.050637 0.021727 0.001402 -0.073308 \n",
"expectation 0.042574 -0.141244 -0.075892 -0.046158 -0.005300 \n",
"common 0.138926 0.067191 -0.147634 0.047569 0.026127 \n",
"ban -0.060220 -0.115585 0.112577 0.069342 0.031616 \n",
"electoral 0.007523 -0.117843 0.048318 0.093463 0.041712 \n",
"coz 0.009905 0.002019 -0.046259 0.022158 0.047524 \n",
"laugh -0.098381 -0.096486 0.019137 -0.014487 0.097855 \n",
"near -0.074969 0.105880 -0.048510 -0.084780 0.131893 \n",
"develop -0.055676 0.022227 -0.016562 -0.052855 -0.009292 \n",
"perform 0.068399 -0.109243 0.087984 -0.089746 0.076768 \n",
"kyaukhaadlega -0.053153 -0.041528 -0.060244 -0.066774 0.064793 \n",
"build 0.073477 -0.027803 0.116254 -0.215213 0.008742 \n",
"dat -0.041859 0.051822 0.077876 -0.056574 0.038935 \n",
"thought -0.020270 -0.003915 0.027130 -0.022510 -0.001879 \n",
"rahul_akhilesh -0.114333 0.011766 -0.054500 0.037149 -0.012070 \n",
"shekhargupta 0.096427 0.035468 0.030736 0.017017 0.049494 \n",
"dalits -0.022647 0.015133 -0.022348 0.008382 -0.038843 \n",
"tally -0.050746 0.053442 0.013369 -0.162591 0.016935 \n",
"ind -0.189764 -0.030724 -0.060189 -0.027609 -0.113397 \n",
"repeat -0.038974 -0.001742 0.160014 -0.114607 -0.075890 \n",
"narrative 0.012858 0.100006 0.079361 -0.106948 0.019481 \n",
"\n",
" 5 6 7 8 9 \\\n",
"electionresults 0.056132 -0.068405 -0.022548 0.030052 0.090498 \n",
"bjp 0.020020 0.009653 0.007908 -0.022853 -0.029902 \n",
"win -0.030763 0.064524 0.036528 -0.090418 -0.146715 \n",
"congress -0.019236 -0.083040 -0.039329 0.014001 -0.084960 \n",
"punjab -0.067995 -0.042101 0.015361 -0.013578 -0.099104 \n",
"upelection2017 -0.117717 -0.137367 -0.038348 -0.033626 0.014944 \n",
"elections2017 0.029732 -0.053312 0.071559 0.082543 0.093932 \n",
"modi 0.039373 0.026097 0.093025 -0.026491 -0.111197 \n",
"narendramodi -0.051825 0.051399 -0.063423 0.027204 -0.016370 \n",
"goa 0.001770 -0.064579 -0.023309 0.071276 -0.127215 \n",
"people -0.082467 0.060742 0.103884 0.048004 -0.050993 \n",
"aap 0.051220 -0.109569 0.027053 -0.071330 -0.088910 \n",
"seat -0.006452 -0.189088 -0.023351 -0.084174 -0.091096 \n",
"bjp4india -0.045998 -0.071840 0.001270 -0.026532 -0.012768 \n",
"election 0.051008 0.089375 -0.051359 0.039666 0.130272 \n",
"vote -0.012839 -0.108461 -0.018243 -0.035966 -0.130858 \n",
"lead -0.087065 -0.057443 0.050723 -0.010519 0.009008 \n",
"india -0.032335 0.081846 -0.032084 0.061125 0.096432 \n",
"punjabelection2017 0.025438 0.008611 -0.027822 -0.066533 0.073121 \n",
"not 0.139616 -0.096990 0.075979 -0.012788 -0.060358 \n",
"result -0.079494 -0.012775 -0.016264 0.168397 -0.070314 \n",
"mayawati -0.064309 -0.137225 0.052240 0.013239 -0.020661 \n",
"today 0.011550 0.022499 -0.042905 -0.036348 0.035779 \n",
"manipur 0.016632 -0.102225 0.078326 0.078213 -0.024353 \n",
"time -0.045412 -0.135379 0.004417 0.028392 0.023189 \n",
"arvindkejriwal 0.003564 0.051566 0.025033 -0.040877 0.005556 \n",
"state 0.029854 0.023474 0.024836 0.023378 -0.115147 \n",
"say 0.002264 -0.047222 0.047205 0.127651 -0.022330 \n",
"uttarpradesh -0.068364 -0.123933 -0.061981 0.108704 -0.014090 \n",
"victory -0.059822 -0.132920 -0.075399 0.091894 -0.033675 \n",
"... ... ... ... ... ... \n",
"rajdeep -0.012667 -0.131865 0.087055 0.013715 0.022383 \n",
"increase -0.103580 0.038335 -0.145551 0.011547 -0.051403 \n",
"hindutva 0.035731 0.017499 -0.129806 0.044985 -0.070479 \n",
"exactly -0.000572 -0.109778 -0.036733 -0.033457 -0.011201 \n",
"expe 0.008620 -0.002010 -0.058105 0.094351 -0.096780 \n",
"self -0.075370 -0.162126 -0.028731 0.121647 -0.021950 \n",
"line -0.058826 0.019199 -0.077528 0.039583 -0.073356 \n",
"cross_halfway_mark -0.010625 -0.013493 -0.020203 -0.022609 -0.030026 \n",
"congratulation_sir -0.026581 -0.022060 -0.047410 0.028588 -0.094107 \n",
"local -0.029511 0.019499 0.020546 0.043304 0.016321 \n",
"expectation 0.029256 0.008429 -0.013683 -0.041414 0.020637 \n",
"common -0.048608 0.100050 0.124762 0.005328 -0.070907 \n",
"ban 0.008764 -0.058356 -0.030033 -0.130406 0.011818 \n",
"electoral -0.089972 0.002510 0.016579 -0.006702 0.024066 \n",
"coz -0.053648 0.036422 0.055437 0.015699 -0.079241 \n",
"laugh -0.050768 0.045400 0.014776 -0.040167 -0.104711 \n",
"near -0.060494 0.077748 -0.040068 0.038564 -0.075247 \n",
"develop 0.006036 0.123389 0.055140 0.066402 -0.050526 \n",
"perform -0.065495 -0.050097 -0.026354 0.019051 0.089457 \n",
"kyaukhaadlega -0.006858 -0.046686 0.045093 -0.011661 0.116930 \n",
"build -0.063480 -0.046682 0.078327 0.045782 -0.041363 \n",
"dat -0.051600 -0.016748 0.043907 0.000600 0.010083 \n",
"thought 0.037204 0.045211 0.150274 0.036449 0.017040 \n",
"rahul_akhilesh 0.037191 -0.046341 -0.076965 -0.035298 -0.118777 \n",
"shekhargupta -0.008830 -0.063456 0.075355 -0.043478 -0.008048 \n",
"dalits -0.024790 -0.039074 0.018119 -0.089860 0.007483 \n",
"tally -0.025198 0.012515 0.051626 0.018235 0.040107 \n",
"ind 0.035910 -0.085639 -0.028148 -0.065998 0.040376 \n",
"repeat -0.013283 -0.051917 0.064910 -0.001597 0.004153 \n",
"narrative 0.022624 -0.058684 -0.002876 0.096466 -0.002182 \n",
"\n",
" ... 190 191 192 193 \\\n",
"electionresults ... 0.121614 0.001951 -0.014015 0.001160 \n",
"bjp ... 0.009916 -0.029126 -0.028172 -0.123596 \n",
"win ... -0.056088 -0.013934 0.076431 -0.078700 \n",
"congress ... 0.055513 -0.140310 -0.007756 -0.077056 \n",
"punjab ... 0.036901 -0.032342 -0.135181 0.080569 \n",
"upelection2017 ... -0.046908 -0.078908 -0.026526 -0.028114 \n",
"elections2017 ... 0.001061 -0.116691 -0.008292 0.013324 \n",
"modi ... -0.092832 -0.038741 0.074647 -0.011324 \n",
"narendramodi ... 0.053611 -0.144650 0.122521 0.005819 \n",
"goa ... 0.124331 0.059266 0.043658 0.045934 \n",
"people ... 0.088357 -0.205868 -0.045787 0.040409 \n",
"aap ... 0.015587 -0.078347 0.016722 -0.066571 \n",
"seat ... -0.062534 0.016595 -0.003928 -0.065256 \n",
"bjp4india ... -0.010398 -0.088884 0.141554 0.016091 \n",
"election ... 0.155409 -0.030844 0.077666 0.041271 \n",
"vote ... -0.025897 -0.079209 0.017629 -0.095567 \n",
"lead ... -0.074429 0.012403 -0.012009 0.110392 \n",
"india ... 0.041833 0.017394 0.065386 -0.028952 \n",
"punjabelection2017 ... -0.040618 -0.082355 -0.053480 0.120564 \n",
"not ... 0.044464 -0.019485 0.100505 0.113914 \n",
"result ... 0.003131 -0.094888 -0.032183 0.007620 \n",
"mayawati ... 0.029006 -0.164574 0.097879 -0.067564 \n",
"today ... 0.036069 -0.140189 -0.006895 -0.050676 \n",
"manipur ... 0.126637 -0.034347 0.095073 -0.051953 \n",
"time ... -0.043129 0.057697 -0.050728 0.120806 \n",
"arvindkejriwal ... -0.023419 -0.003268 -0.030896 -0.032492 \n",
"state ... 0.038821 -0.017290 -0.040165 -0.105001 \n",
"say ... -0.068483 -0.009658 0.015862 -0.004461 \n",
"uttarpradesh ... 0.186138 -0.007268 0.017787 0.034278 \n",
"victory ... -0.016780 -0.065449 -0.040673 0.051611 \n",
"... ... ... ... ... ... \n",
"rajdeep ... -0.073607 0.017322 -0.078839 0.008494 \n",
"increase ... -0.051460 -0.036290 0.012811 -0.006737 \n",
"hindutva ... -0.069209 -0.179267 0.052250 -0.065992 \n",
"exactly ... 0.050013 -0.019603 0.122286 -0.107954 \n",
"expe ... -0.019896 -0.161666 0.106552 -0.072747 \n",
"self ... -0.037909 -0.203708 0.018260 0.070127 \n",
"line ... 0.047194 -0.123512 0.004382 0.040966 \n",
"cross_halfway_mark ... -0.007541 0.026947 -0.096860 0.067924 \n",
"congratulation_sir ... 0.016904 -0.033561 -0.033346 -0.082044 \n",
"local ... 0.000168 -0.075014 0.142310 -0.134372 \n",
"expectation ... 0.095484 -0.133989 0.011314 0.082593 \n",
"common ... -0.032742 -0.042576 0.047057 0.079273 \n",
"ban ... -0.003338 -0.073826 -0.143346 -0.046458 \n",
"electoral ... 0.100980 0.001423 0.025170 -0.044179 \n",
"coz ... -0.092309 -0.150016 0.044821 -0.017710 \n",
"laugh ... -0.152082 -0.116488 0.010465 0.003475 \n",
"near ... 0.028618 -0.065068 -0.015515 0.052545 \n",
"develop ... -0.015021 -0.088237 -0.000258 -0.005249 \n",
"perform ... 0.134925 -0.121449 0.021458 -0.209780 \n",
"kyaukhaadlega ... 0.013920 0.000936 -0.019011 -0.019647 \n",
"build ... -0.030358 0.002865 0.046920 -0.159242 \n",
"dat ... -0.002709 -0.085736 0.098313 0.013040 \n",
"thought ... 0.058931 -0.127265 0.015879 0.033426 \n",
"rahul_akhilesh ... -0.026322 -0.137221 0.111736 0.074768 \n",
"shekhargupta ... 0.018096 -0.034190 0.105129 0.009823 \n",
"dalits ... -0.064357 0.021281 0.003567 -0.046159 \n",
"tally ... -0.071195 0.006373 0.016522 -0.104566 \n",
"ind ... 0.058363 0.067316 -0.065128 -0.092801 \n",
"repeat ... -0.052163 -0.045921 0.041476 -0.002198 \n",
"narrative ... -0.030939 -0.019613 0.021866 0.001329 \n",
"\n",
" 194 195 196 197 198 199 \n",
"electionresults 0.037758 -0.031155 -0.050242 0.150196 0.018113 0.100890 \n",
"bjp -0.107109 0.052286 -0.052555 0.083021 0.053085 -0.008552 \n",
"win 0.039870 -0.044035 -0.071660 -0.020569 -0.039647 -0.033876 \n",
"congress -0.116416 0.127231 -0.139047 0.052605 0.053434 0.040970 \n",
"punjab -0.034288 0.066213 -0.100044 -0.097200 -0.098354 -0.074190 \n",
"upelection2017 0.023593 -0.008405 -0.056531 -0.022312 0.137108 -0.029557 \n",
"elections2017 -0.012990 -0.034696 0.019054 0.034339 0.017413 -0.008826 \n",
"modi 0.005478 0.031353 -0.047724 0.129504 -0.035455 -0.027849 \n",
"narendramodi 0.079221 -0.095277 -0.014294 0.065366 -0.032062 -0.102929 \n",
"goa -0.015065 0.115155 -0.079758 -0.007518 -0.048590 -0.028523 \n",
"people 0.005570 -0.057884 0.020583 0.085420 0.004435 -0.060302 \n",
"aap 0.074345 0.067832 -0.132459 -0.073835 0.010995 -0.058810 \n",
"seat -0.037860 0.039611 0.100247 0.079270 -0.122635 0.044122 \n",
"bjp4india -0.060637 0.025861 -0.081247 -0.018354 0.020156 0.073299 \n",
"election 0.083457 -0.102640 -0.015524 -0.006577 -0.103896 -0.024485 \n",
"vote -0.035544 0.061042 -0.082762 0.057972 0.025470 -0.090255 \n",
"lead 0.006180 0.139540 0.070745 0.003855 0.031885 0.079737 \n",
"india 0.087125 0.075055 0.074320 0.108562 -0.174714 -0.030632 \n",
"punjabelection2017 -0.026865 -0.067068 -0.116497 0.066209 -0.019966 -0.025676 \n",
"not -0.014448 -0.008090 -0.060805 -0.045079 -0.044406 -0.066812 \n",
"result 0.128673 -0.030522 -0.024016 0.050490 0.006241 0.159599 \n",
"mayawati -0.017508 -0.077931 -0.109366 -0.081635 -0.016363 0.110366 \n",
"today -0.021702 -0.035698 -0.116479 -0.003879 0.035651 0.042404 \n",
"manipur 0.011405 0.033644 0.051250 -0.022853 -0.040544 -0.051701 \n",
"time -0.033045 -0.021622 0.103954 0.084632 -0.077904 0.066601 \n",
"arvindkejriwal -0.003914 0.006729 -0.066372 0.001733 0.055826 0.070511 \n",
"state 0.025496 0.088523 -0.081328 -0.016549 -0.173032 -0.081411 \n",
"say 0.033136 -0.109790 -0.044600 0.094797 -0.002640 -0.035027 \n",
"uttarpradesh 0.023755 -0.060320 0.089871 -0.001383 -0.010562 -0.110586 \n",
"victory -0.139144 -0.125057 -0.054146 -0.006119 -0.056002 -0.026770 \n",
"... ... ... ... ... ... ... \n",
"rajdeep 0.011737 -0.159414 0.009522 0.037300 0.007489 -0.001320 \n",
"increase -0.007264 -0.109139 0.025313 0.151042 0.004530 0.134056 \n",
"hindutva -0.040703 0.034155 -0.075206 0.018361 -0.098286 0.109001 \n",
"exactly 0.008353 -0.015783 0.018979 0.106707 0.028863 0.004645 \n",
"expe -0.009588 -0.058226 0.036806 0.043856 0.046624 -0.082276 \n",
"self 0.048522 0.013464 -0.021706 0.034909 0.014207 0.105151 \n",
"line -0.110072 0.064913 0.030666 -0.032984 -0.091194 -0.021220 \n",
"cross_halfway_mark 0.078989 0.056889 -0.085800 0.032944 -0.028633 0.013812 \n",
"congratulation_sir 0.026074 -0.009851 -0.008612 0.000366 0.029629 -0.084629 \n",
"local -0.013498 -0.073779 0.000324 0.027341 -0.099459 -0.045796 \n",
"expectation -0.075108 0.012156 -0.006141 0.087376 -0.027974 0.045746 \n",
"common -0.051876 0.094442 0.017851 0.039200 -0.020738 -0.049305 \n",
"ban 0.145053 0.019136 0.103405 -0.070201 -0.032085 -0.178364 \n",
"electoral -0.101104 -0.038782 -0.021458 0.031113 -0.056363 -0.074877 \n",
"coz 0.036217 -0.022037 0.022665 -0.063585 0.001722 0.014351 \n",
"laugh 0.025192 0.099849 -0.046368 0.057842 -0.058274 0.123511 \n",
"near -0.042279 0.035203 -0.107615 0.021339 -0.013617 0.113182 \n",
"develop 0.016249 -0.088044 0.070216 0.031028 0.042927 -0.113180 \n",
"perform 0.114002 -0.062772 0.048758 -0.093813 0.025514 0.063813 \n",
"kyaukhaadlega 0.110923 -0.064272 -0.044606 0.050543 0.076061 -0.017054 \n",
"build -0.055738 -0.115379 -0.079165 0.044054 0.007809 0.007130 \n",
"dat -0.059380 -0.056215 -0.025118 0.088603 -0.123315 -0.032688 \n",
"thought 0.006267 -0.046305 -0.020966 0.027727 0.100224 -0.067964 \n",
"rahul_akhilesh 0.085226 -0.041079 -0.095149 0.068541 0.155929 0.020803 \n",
"shekhargupta 0.049082 -0.128055 -0.097915 -0.006912 0.039343 -0.051636 \n",
"dalits 0.094281 0.001393 0.056063 0.018718 -0.058261 0.053186 \n",
"tally -0.033155 0.031846 0.006604 0.009546 -0.051816 0.030673 \n",
"ind -0.077179 0.095876 -0.007619 0.040394 0.089587 0.057230 \n",
"repeat -0.067260 -0.050876 0.046228 0.014761 0.045161 -0.050687 \n",
"narrative 0.012298 -0.108745 0.039810 0.005657 0.085686 -0.092077 \n",
"\n",
"[1007 rows x 200 columns]"
]
},
"execution_count": 512,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# build a list of the terms, integer indices,\n",
"# and term counts from the food2vec model vocabulary\n",
"ordered_vocab = [(term, voc.index, voc.count)\n",
" for term, voc in word2vec.wv.vocab.iteritems()]\n",
"\n",
"# sort by the term counts, so the most common terms appear first\n",
"ordered_vocab = sorted(ordered_vocab, key=lambda (term, index, count): -count)\n",
"\n",
"# unzip the terms, integer indices, and counts into separate lists\n",
"ordered_terms, term_indices, term_counts = zip(*ordered_vocab)\n",
"\n",
"# create a DataFrame with the word2vec vectors as data,\n",
"# and the terms as row labels\n",
"word_vectors = pd.DataFrame(word2vec.wv.syn0norm[term_indices, :],\n",
" index=ordered_terms)\n",
"\n",
"word_vectors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating the visualization"
]
},
{
"cell_type": "code",
"execution_count": 513,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.manifold import TSNE\n",
"import spacy"
]
},
{
"cell_type": "code",
"execution_count": 514,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"op = list(ordered_terms)"
]
},
{
"cell_type": "code",
"execution_count": 515,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'electionresults', <gensim.models.keyedvectors.Vocab at 0x10914afd0>),\n",
" (u'bjp', <gensim.models.keyedvectors.Vocab at 0x109954cd0>),\n",
" (u'win', <gensim.models.keyedvectors.Vocab at 0x10d748a10>),\n",
" (u'congress', <gensim.models.keyedvectors.Vocab at 0x109059510>),\n",
" (u'punjab', <gensim.models.keyedvectors.Vocab at 0x10d948190>),\n",
" (u'upelection2017', <gensim.models.keyedvectors.Vocab at 0x10982b610>),\n",
" (u'elections2017', <gensim.models.keyedvectors.Vocab at 0x10871a910>),\n",
" (u'modi', <gensim.models.keyedvectors.Vocab at 0x1091ecc10>),\n",
" (u'narendramodi', <gensim.models.keyedvectors.Vocab at 0x10d748c50>),\n",
" (u'goa', <gensim.models.keyedvectors.Vocab at 0x109424550>)]"
]
},
"execution_count": 515,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import Counter\n",
"word_freq = Counter(word2vec.wv.vocab)\n",
"word_freq.most_common(10)"
]
},
{
"cell_type": "code",
"execution_count": 516,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tsne_input = word_vectors.drop(spacy.en.STOPWORDS, errors=u'ignore')\n",
"tsne_input = tsne_input.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 517,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>...</th>\n",
" <th>190</th>\n",
" <th>191</th>\n",
" <th>192</th>\n",
" <th>193</th>\n",
" <th>194</th>\n",
" <th>195</th>\n",
" <th>196</th>\n",
" <th>197</th>\n",
" <th>198</th>\n",
" <th>199</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>electionresults</th>\n",
" <td>0.038593</td>\n",
" <td>0.065232</td>\n",
" <td>0.130983</td>\n",
" <td>0.009477</td>\n",
" <td>-0.018893</td>\n",
" <td>0.056132</td>\n",
" <td>-0.068405</td>\n",
" <td>-0.022548</td>\n",
" <td>0.030052</td>\n",
" <td>0.090498</td>\n",
" <td>...</td>\n",
" <td>0.121614</td>\n",
" <td>0.001951</td>\n",
" <td>-0.014015</td>\n",
" <td>0.001160</td>\n",
" <td>0.037758</td>\n",
" <td>-0.031155</td>\n",
" <td>-0.050242</td>\n",
" <td>0.150196</td>\n",
" <td>0.018113</td>\n",
" <td>0.100890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bjp</th>\n",
" <td>-0.023424</td>\n",
" <td>-0.023021</td>\n",
" <td>0.045584</td>\n",
" <td>0.026587</td>\n",
" <td>-0.013147</td>\n",
" <td>0.020020</td>\n",
" <td>0.009653</td>\n",
" <td>0.007908</td>\n",
" <td>-0.022853</td>\n",
" <td>-0.029902</td>\n",
" <td>...</td>\n",
" <td>0.009916</td>\n",
" <td>-0.029126</td>\n",
" <td>-0.028172</td>\n",
" <td>-0.123596</td>\n",
" <td>-0.107109</td>\n",
" <td>0.052286</td>\n",
" <td>-0.052555</td>\n",
" <td>0.083021</td>\n",
" <td>0.053085</td>\n",
" <td>-0.008552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>win</th>\n",
" <td>-0.029219</td>\n",
" <td>-0.085240</td>\n",
" <td>0.027453</td>\n",
" <td>0.104936</td>\n",
" <td>-0.063723</td>\n",
" <td>-0.030763</td>\n",
" <td>0.064524</td>\n",
" <td>0.036528</td>\n",
" <td>-0.090418</td>\n",
" <td>-0.146715</td>\n",
" <td>...</td>\n",
" <td>-0.056088</td>\n",
" <td>-0.013934</td>\n",
" <td>0.076431</td>\n",
" <td>-0.078700</td>\n",
" <td>0.039870</td>\n",
" <td>-0.044035</td>\n",
" <td>-0.071660</td>\n",
" <td>-0.020569</td>\n",
" <td>-0.039647</td>\n",
" <td>-0.033876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>congress</th>\n",
" <td>-0.126363</td>\n",
" <td>-0.194618</td>\n",
" <td>-0.027479</td>\n",
" <td>0.032964</td>\n",
" <td>0.062613</td>\n",
" <td>-0.019236</td>\n",
" <td>-0.083040</td>\n",
" <td>-0.039329</td>\n",
" <td>0.014001</td>\n",
" <td>-0.084960</td>\n",
" <td>...</td>\n",
" <td>0.055513</td>\n",
" <td>-0.140310</td>\n",
" <td>-0.007756</td>\n",
" <td>-0.077056</td>\n",
" <td>-0.116416</td>\n",
" <td>0.127231</td>\n",
" <td>-0.139047</td>\n",
" <td>0.052605</td>\n",
" <td>0.053434</td>\n",
" <td>0.040970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>punjab</th>\n",
" <td>0.068467</td>\n",
" <td>-0.142689</td>\n",
" <td>-0.019247</td>\n",
" <td>0.010518</td>\n",
" <td>0.061997</td>\n",
" <td>-0.067995</td>\n",
" <td>-0.042101</td>\n",
" <td>0.015361</td>\n",
" <td>-0.013578</td>\n",
" <td>-0.099104</td>\n",
" <td>...</td>\n",
" <td>0.036901</td>\n",
" <td>-0.032342</td>\n",
" <td>-0.135181</td>\n",
" <td>0.080569</td>\n",
" <td>-0.034288</td>\n",
" <td>0.066213</td>\n",
" <td>-0.100044</td>\n",
" <td>-0.097200</td>\n",
" <td>-0.098354</td>\n",
" <td>-0.074190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>upelection2017</th>\n",
" <td>-0.059415</td>\n",
" <td>0.119698</td>\n",
" <td>-0.072613</td>\n",
" <td>-0.007520</td>\n",
" <td>0.009200</td>\n",
" <td>-0.117717</td>\n",
" <td>-0.137367</td>\n",
" <td>-0.038348</td>\n",
" <td>-0.033626</td>\n",
" <td>0.014944</td>\n",
" <td>...</td>\n",
" <td>-0.046908</td>\n",
" <td>-0.078908</td>\n",
" <td>-0.026526</td>\n",
" <td>-0.028114</td>\n",
" <td>0.023593</td>\n",
" <td>-0.008405</td>\n",
" <td>-0.056531</td>\n",
" <td>-0.022312</td>\n",
" <td>0.137108</td>\n",
" <td>-0.029557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>elections2017</th>\n",
" <td>0.000885</td>\n",
" <td>0.026864</td>\n",
" <td>0.056395</td>\n",
" <td>0.138327</td>\n",
" <td>0.023747</td>\n",
" <td>0.029732</td>\n",
" <td>-0.053312</td>\n",
" <td>0.071559</td>\n",
" <td>0.082543</td>\n",
" <td>0.093932</td>\n",
" <td>...</td>\n",
" <td>0.001061</td>\n",
" <td>-0.116691</td>\n",
" <td>-0.008292</td>\n",
" <td>0.013324</td>\n",
" <td>-0.012990</td>\n",
" <td>-0.034696</td>\n",
" <td>0.019054</td>\n",
" <td>0.034339</td>\n",
" <td>0.017413</td>\n",
" <td>-0.008826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>modi</th>\n",
" <td>0.057329</td>\n",
" <td>-0.058722</td>\n",
" <td>0.004947</td>\n",
" <td>0.058037</td>\n",
" <td>-0.066921</td>\n",
" <td>0.039373</td>\n",
" <td>0.026097</td>\n",
" <td>0.093025</td>\n",
" <td>-0.026491</td>\n",
" <td>-0.111197</td>\n",
" <td>...</td>\n",
" <td>-0.092832</td>\n",
" <td>-0.038741</td>\n",
" <td>0.074647</td>\n",
" <td>-0.011324</td>\n",
" <td>0.005478</td>\n",
" <td>0.031353</td>\n",
" <td>-0.047724</td>\n",
" <td>0.129504</td>\n",
" <td>-0.035455</td>\n",
" <td>-0.027849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>narendramodi</th>\n",
" <td>0.106970</td>\n",
" <td>-0.034159</td>\n",
" <td>-0.134925</td>\n",
" <td>0.120943</td>\n",
" <td>0.086266</td>\n",
" <td>-0.051825</td>\n",
" <td>0.051399</td>\n",
" <td>-0.063423</td>\n",
" <td>0.027204</td>\n",
" <td>-0.016370</td>\n",
" <td>...</td>\n",
" <td>0.053611</td>\n",
" <td>-0.144650</td>\n",
" <td>0.122521</td>\n",
" <td>0.005819</td>\n",
" <td>0.079221</td>\n",
" <td>-0.095277</td>\n",
" <td>-0.014294</td>\n",
" <td>0.065366</td>\n",
" <td>-0.032062</td>\n",
" <td>-0.102929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>goa</th>\n",
" <td>0.009466</td>\n",
" <td>-0.100763</td>\n",
" <td>0.051813</td>\n",
" <td>0.074620</td>\n",
" <td>0.080515</td>\n",
" <td>0.001770</td>\n",
" <td>-0.064579</td>\n",
" <td>-0.023309</td>\n",
" <td>0.071276</td>\n",
" <td>-0.127215</td>\n",
" <td>...</td>\n",
" <td>0.124331</td>\n",
" <td>0.059266</td>\n",
" <td>0.043658</td>\n",
" <td>0.045934</td>\n",
" <td>-0.015065</td>\n",
" <td>0.115155</td>\n",
" <td>-0.079758</td>\n",
" <td>-0.007518</td>\n",
" <td>-0.048590</td>\n",
" <td>-0.028523</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 200 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 \\\n",
"electionresults 0.038593 0.065232 0.130983 0.009477 -0.018893 0.056132 \n",
"bjp -0.023424 -0.023021 0.045584 0.026587 -0.013147 0.020020 \n",
"win -0.029219 -0.085240 0.027453 0.104936 -0.063723 -0.030763 \n",
"congress -0.126363 -0.194618 -0.027479 0.032964 0.062613 -0.019236 \n",
"punjab 0.068467 -0.142689 -0.019247 0.010518 0.061997 -0.067995 \n",
"upelection2017 -0.059415 0.119698 -0.072613 -0.007520 0.009200 -0.117717 \n",
"elections2017 0.000885 0.026864 0.056395 0.138327 0.023747 0.029732 \n",
"modi 0.057329 -0.058722 0.004947 0.058037 -0.066921 0.039373 \n",
"narendramodi 0.106970 -0.034159 -0.134925 0.120943 0.086266 -0.051825 \n",
"goa 0.009466 -0.100763 0.051813 0.074620 0.080515 0.001770 \n",
"\n",
" 6 7 8 9 ... 190 \\\n",
"electionresults -0.068405 -0.022548 0.030052 0.090498 ... 0.121614 \n",
"bjp 0.009653 0.007908 -0.022853 -0.029902 ... 0.009916 \n",
"win 0.064524 0.036528 -0.090418 -0.146715 ... -0.056088 \n",
"congress -0.083040 -0.039329 0.014001 -0.084960 ... 0.055513 \n",
"punjab -0.042101 0.015361 -0.013578 -0.099104 ... 0.036901 \n",
"upelection2017 -0.137367 -0.038348 -0.033626 0.014944 ... -0.046908 \n",
"elections2017 -0.053312 0.071559 0.082543 0.093932 ... 0.001061 \n",
"modi 0.026097 0.093025 -0.026491 -0.111197 ... -0.092832 \n",
"narendramodi 0.051399 -0.063423 0.027204 -0.016370 ... 0.053611 \n",
"goa -0.064579 -0.023309 0.071276 -0.127215 ... 0.124331 \n",
"\n",
" 191 192 193 194 195 196 \\\n",
"electionresults 0.001951 -0.014015 0.001160 0.037758 -0.031155 -0.050242 \n",
"bjp -0.029126 -0.028172 -0.123596 -0.107109 0.052286 -0.052555 \n",
"win -0.013934 0.076431 -0.078700 0.039870 -0.044035 -0.071660 \n",
"congress -0.140310 -0.007756 -0.077056 -0.116416 0.127231 -0.139047 \n",
"punjab -0.032342 -0.135181 0.080569 -0.034288 0.066213 -0.100044 \n",
"upelection2017 -0.078908 -0.026526 -0.028114 0.023593 -0.008405 -0.056531 \n",
"elections2017 -0.116691 -0.008292 0.013324 -0.012990 -0.034696 0.019054 \n",
"modi -0.038741 0.074647 -0.011324 0.005478 0.031353 -0.047724 \n",
"narendramodi -0.144650 0.122521 0.005819 0.079221 -0.095277 -0.014294 \n",
"goa 0.059266 0.043658 0.045934 -0.015065 0.115155 -0.079758 \n",
"\n",
" 197 198 199 \n",
"electionresults 0.150196 0.018113 0.100890 \n",
"bjp 0.083021 0.053085 -0.008552 \n",
"win -0.020569 -0.039647 -0.033876 \n",
"congress 0.052605 0.053434 0.040970 \n",
"punjab -0.097200 -0.098354 -0.074190 \n",
"upelection2017 -0.022312 0.137108 -0.029557 \n",
"elections2017 0.034339 0.017413 -0.008826 \n",
"modi 0.129504 -0.035455 -0.027849 \n",
"narendramodi 0.065366 -0.032062 -0.102929 \n",
"goa -0.007518 -0.048590 -0.028523 \n",
"\n",
"[10 rows x 200 columns]"
]
},
"execution_count": 517,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tsne_input.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 518,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tsne_filepath = os.path.join(u'tsne_model')\n",
"\n",
"tsne_vectors_filepath = os.path.join(u'tsne_vectors.npy')"
]
},
{
"cell_type": "code",
"execution_count": 519,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import warnings\n",
"import cPickle as pickle"
]
},
{
"cell_type": "code",
"execution_count": 520,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.93 s, sys: 599 ms, total: 6.53 s\n",
"Wall time: 6.96 s\n"
]
}
],
"source": [
"%%time\n",
"tsne = TSNE()\n",
"tsne_vectors = tsne.fit_transform(word_vectors.values)\n",
" \n",
"with open(tsne_filepath, 'w') as f:\n",
" pickle.dump(tsne, f)\n",
" pd.np.save(tsne_vectors_filepath, tsne_vectors)\n",
" \n",
"\n",
"#loading the model\n",
"\n",
"with open(tsne_filepath) as f:\n",
" tsne = pickle.load(f)\n",
" \n",
"tsne_vectors = pd.np.load(tsne_vectors_filepath)\n",
"\n",
"tsne_vectors = pd.DataFrame(tsne_vectors,\n",
" index=pd.Index(word_vectors.index),\n",
" columns=[u'x_coord', u'y_coord'])"
]
},
{
"cell_type": "code",
"execution_count": 521,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tsne_vectors[u'word'] = tsne_vectors.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# creating the plot"
]
},
{
"cell_type": "code",
"execution_count": 522,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"}(this));"
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},
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}
],
"source": [
"from bokeh.plotting import figure, show, output_notebook\n",
"from bokeh.models import HoverTool, ColumnDataSource\n",
"\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 523,
"metadata": {
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{
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" var render_items = [{\"docid\":\"2886f9d2-d825-4ef9-9873-e11890ce1f4f\",\"elementid\":\"27c53e05-278c-4745-879a-29729e40c214\",\"modelid\":\"92636a3e-e0da-4077-9897-5ebcc4557c15\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"27c53e05-278c-4745-879a-29729e40c214\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# add our DataFrame as a ColumnDataSource for Bokeh\n",
"plot_data = ColumnDataSource(tsne_vectors)\n",
"\n",
"# create the plot and configure the\n",
"# title, dimensions, and tools\n",
"tsne_plot = figure(title=u't-SNE Word Embeddings',\n",
" plot_width = 600,\n",
" plot_height = 600,\n",
" tools= (u'pan, wheel_zoom, box_zoom,'\n",
" u'box_select, resize, reset'),\n",
" #active_scroll=u'wheel_zoom'\n",
" )\n",
"\n",
"# add a hover tool to display words on roll-over\n",
"tsne_plot.add_tools( HoverTool(tooltips = u'@word') )\n",
"\n",
"# draw the words as circles on the plot\n",
"tsne_plot.circle(u'x_coord', u'y_coord', source=plot_data,\n",
" color=u'blue', line_alpha=0.2, fill_alpha=0.1,\n",
" size=10, hover_line_color=u'black')\n",
"\n",
"# configure visual elements of the plot\n",
"#tsne_plot.title.text_font_size = '16pt'\n",
"tsne_plot.xaxis.visible = False\n",
"tsne_plot.yaxis.visible = False\n",
"tsne_plot.grid.grid_line_color = None\n",
"tsne_plot.outline_line_color = None\n",
"\n",
"# engage!\n",
"show(tsne_plot);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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