#Setting up Twitter
import tweepy
consumer_key = "GlYCSvDgUet79gori1M5rxmMW"
consumer_secret = "JRNb6FIjsSMOu6CU4QRMdJ1kMsVd7IF6g9PnKgD2qrdeva2iFY"
access_token = "259205396-ZWx5lQCRzy5GMnzmNTIQzMckDqRnzjfVnoFu0VgG"
access_token_secret = "eBx4oQQYHhXRgQE6cOioMSUhzpLWNLc8c2hgL4GGmG2Kd"
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
query = ':)'
max_tweets = 10
searched_tweets = [status.text for status in tweepy.Cursor(api.search, q=query, lang="en").items(max_tweets)]
# First let's see what we have to work with
for tweet in searched_tweets:
print(tweet+"\n")
RT @shqlby: I'm in Australia for the two weeks, please come see me :-) @NiallOfficial https://t.co/zVCyPE8nwn @ilves Yep. I have all his books. :) RT @_sushitae: [HELP RT] please help me to get 3k retweets to learn korean :)) @cudooville https://t.co/CVzfD0BG3K RT @pressedolan: I decided to re-make an old drawing of mine :) • @EthanDolan https://t.co/W8srpTCEvc @highmeh @truneski Go do the OSCE :) Good friends make the world go round :) RT @YourHobi: where's Kim Army? She's upstairs dreaming about Bangtan :) https://t.co/oIIuPntMYZ @JustinStone_ Hey, why did you remove singles 2016 vol.2 from spotify? Is it available somewhere? : ) @Complic8d_Andre @bluemarine69 Lols :) Hapi weekend dre :) @mikhan11 This is awesome!!!!! :D #ALDUB23rdMonthsary
Here are some things that I want to do:
import re
# Defining new Function
def process_tweet( tweet , punctuation=False):
#
# 1. Remove @username
tweet = re.sub('@[^\s]+','',tweet)
#
# 2. Remove www.* or https?://*
tweet = re.sub('((www\.[\s]+)|(https?:/?/?[^\s]+))','',tweet)
#
# 3. Remove 'RT'
tweet = tweet.replace('RT','')
#
# 4. Hashtag remove
tweet = tweet.replace('#','')
#
# 5. Remove commas, ?s, !s, and periods.
if punctuation:
tweet = tweet.replace('.','')
tweet = tweet.replace(',','')
tweet = tweet.replace('?','')
tweet = tweet.replace('!','')
#
# 6. Convert to lower case, split into individual words
words = tweet.lower().split()
#
# 7. Join the words back into one string separated by space,
# and return the result.
return( words)
# Load the punkt tokenizer
import nltk
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
for tweet in searched_tweets:
print(" ".join( process_tweet(tweet) ))
i'm in australia for the two weeks, please come see me :-) yep. i have all his books. :) [help ] please help me to get 3k retweets to learn korean :)) i decided to re-make an old drawing of mine :) • go do the osce :) good friends make the world go round :) where's kim army? she's upstairs dreaming about bangtan :) hey, why did you remove singles 2016 vol.2 from spotify? is it available somewhere? : ) lols :) hapi weekend dre :) this is awesome!!!!! :d aldub23rdmonthsary
for tweet in searched_tweets:
print(tokenizer.tokenize(" ".join( process_tweet(tweet) )))
["i'm in australia for the two weeks, please come see me :-)"] ['yep.', 'i have all his books.', ':)'] ['[help ] please help me to get 3k retweets to learn korean :))'] ['i decided to re-make an old drawing of mine :) •'] ['go do the osce :)'] ['good friends make the world go round :)'] ["where's kim army?", "she's upstairs dreaming about bangtan :)"] ['hey, why did you remove singles 2016 vol.2 from spotify?', 'is it available somewhere?', ': )'] ['lols :) hapi weekend dre :)'] ['this is awesome!!!!!', ':d aldub23rdmonthsary']
# Define a function to split a review into parsed sentences
def process_whole_tweet( tweet ):
# Function to split a review into parsed sentences. Returns a
# list of sentences, where each sentence is a list of words
#
# 1. Use the NLTK tokenizer to split the paragraph into sentences
raw_sentences = tokenizer.tokenize(" ".join( process_tweet(tweet) ))
#
# 2. Loop over each sentence
sentences = []
for raw_sentence in raw_sentences:
# If a sentence is empty, skip it
if len(raw_sentence) > 0:
# Otherwise, call review_to_wordlist to get a list of words
sentences.append( process_tweet( raw_sentence, True))
#
# Return the list of sentences (each sentence is a list of words,
# so this returns a list of lists
return sentences
for tweet in searched_tweets:
print(process_whole_tweet(tweet))
[["i'm", 'in', 'australia', 'for', 'the', 'two', 'weeks', 'please', 'come', 'see', 'me', ':-)']] [['yep'], ['i', 'have', 'all', 'his', 'books'], [':)']] [['[help', ']', 'please', 'help', 'me', 'to', 'get', '3k', 'retweets', 'to', 'learn', 'korean', ':))']] [['i', 'decided', 'to', 're-make', 'an', 'old', 'drawing', 'of', 'mine', ':)', '•']] [['go', 'do', 'the', 'osce', ':)']] [['good', 'friends', 'make', 'the', 'world', 'go', 'round', ':)']] [["where's", 'kim', 'army'], ["she's", 'upstairs', 'dreaming', 'about', 'bangtan', ':)']] [['hey', 'why', 'did', 'you', 'remove', 'singles', '2016', 'vol2', 'from', 'spotify'], ['is', 'it', 'available', 'somewhere'], [':', ')']] [['lols', ':)', 'hapi', 'weekend', 'dre', ':)']] [['this', 'is', 'awesome'], [':d', 'aldub23rdmonthsary']]
import _pickle as pickle
def save_obj(obj, name ):
with open('output/'+ name + '.pkl', 'wb') as f:
pickle.dump(obj, f)
def load_obj(name ):
with open('output/'+ name + '.pkl', 'rb') as f:
return pickle.load(f)
import pandas as pd
header = ["sentiment","id","time","query","handler","tweet"]
# Read data from files
manual = pd.read_csv("./data/twitter_manual.csv", header=None,encoding = "ISO-8859-1")
full = pd.read_csv("./data/twitter_full.csv", header=None,encoding = "ISO-8859-1")
manual.columns = header
full.columns = header
# Verify the number of reviews that were read (100,000 in total)
print ("Read %d labeled train reviews - %d from twitter_manual and %d from twitter_full\n"
% (len(manual)+len(full), len(manual), len(full)))
print (manual[:10])
Read 1600498 labeled train reviews - 498 from twitter_manual and 1600000 from twitter_full
sentiment id time query handler \
0 4 3 Mon May 11 03:17:40 UTC 2009 kindle2 tpryan
1 4 4 Mon May 11 03:18:03 UTC 2009 kindle2 vcu451
2 4 5 Mon May 11 03:18:54 UTC 2009 kindle2 chadfu
3 4 6 Mon May 11 03:19:04 UTC 2009 kindle2 SIX15
4 4 7 Mon May 11 03:21:41 UTC 2009 kindle2 yamarama
5 4 8 Mon May 11 03:22:00 UTC 2009 kindle2 GeorgeVHulme
6 0 9 Mon May 11 03:22:30 UTC 2009 aig Seth937
7 4 10 Mon May 11 03:26:10 UTC 2009 jquery dcostalis
8 4 11 Mon May 11 03:27:15 UTC 2009 twitter PJ_King
9 4 12 Mon May 11 03:29:20 UTC 2009 obama mandanicole
tweet
0 @stellargirl I loooooooovvvvvveee my Kindle2. ...
1 Reading my kindle2... Love it... Lee childs i...
2 Ok, first assesment of the #kindle2 ...it fuck...
3 @kenburbary You'll love your Kindle2. I've had...
4 @mikefish Fair enough. But i have the Kindle2...
5 @richardebaker no. it is too big. I'm quite ha...
6 Fuck this economy. I hate aig and their non lo...
7 Jquery is my new best friend.
8 Loves twitter
9 how can you not love Obama? he makes jokes abo...
# Split list into training and testing
import numpy as np
msk = np.random.rand(len(full)) < 0.5
train = full[msk]
rest = full[~msk]
msk2 = np.random.rand(len(rest)) < 0.01
test = rest[msk2]
rest = rest[~msk2]
print ("Train - %d; Test - %d; Rest - %d" % (len(train), len(test), len(rest)))
Train - 799650; Test - 8117; Rest - 792233
sentences = [] # Initialize an empty list of sentences
print ("Parsing sentences from test set")
for tweet in test["tweet"]:
sentences += process_whole_tweet(tweet)
print ("Parsing sentences from training set")
for tweet in train["tweet"]:
sentences += process_whole_tweet(tweet)
print ("Parsing sentences from remaining set")
for tweet in rest["tweet"]:
sentences += process_whole_tweet(tweet)
# Check how many sentences we have in total - should be around 850,000+
print (len(sentences))
print (sentences[0])
2683104 ['is', 'upset', 'that', 'he', "can't", 'update', 'his', 'facebook', 'by', 'texting', 'it', 'and', 'might', 'cry', 'as', 'a', 'result', 'school', 'today', 'also']
# Import the built-in logging module and configure it so that Word2Vec
# creates nice output messages
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\
level=logging.INFO)
logging.disable(logging.NOTSET)
# Set values for various parameters
num_features = 400 # Word vector dimensionality
min_word_count = 30 # Minimum word count
num_workers = 4 # Number of threads to run in parallel
context = 10 # Context window size
downsampling = 1e-3 # Downsample setting for frequent words
# Initialize and train the model (this will take some time)
from gensim.models import word2vec
print ("Training model...")
model = word2vec.Word2Vec(sentences, workers=num_workers, \
size=num_features, min_count = min_word_count, \
window = context, sample = downsampling)
# If you don't plan to train the model any further, calling
# init_sims will make the model much more memory-efficient.
model.init_sims(replace=True)
# It can be helpful to create a meaningful model name and
# save the model for later use. You can load it later using Word2Vec.load()
model_name = "output/400features_30minwords_10context_twitter"
model.save(model_name)
logging.disable(logging.INFO)
2017-06-13 10:57:09,313 : INFO : collecting all words and their counts 2017-06-13 10:57:09,314 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types 2017-06-13 10:57:09,350 : INFO : PROGRESS: at sentence #10000, processed 80420 words, keeping 11023 word types 2017-06-13 10:57:09,392 : INFO : PROGRESS: at sentence #20000, processed 160059 words, keeping 17471 word types 2017-06-13 10:57:09,420 : INFO : PROGRESS: at sentence #30000, processed 239679 words, keeping 22721 word types 2017-06-13 10:57:09,458 : INFO : PROGRESS: at sentence #40000, processed 319901 words, keeping 27594 word types
Training model...
2017-06-13 10:57:09,512 : INFO : PROGRESS: at sentence #50000, processed 399113 words, keeping 32031 word types 2017-06-13 10:57:09,558 : INFO : PROGRESS: at sentence #60000, processed 478802 words, keeping 36228 word types 2017-06-13 10:57:09,590 : INFO : PROGRESS: at sentence #70000, processed 558566 words, keeping 40148 word types 2017-06-13 10:57:09,625 : INFO : PROGRESS: at sentence #80000, processed 636147 words, keeping 43890 word types 2017-06-13 10:57:09,661 : INFO : PROGRESS: at sentence #90000, processed 714871 words, keeping 47420 word types 2017-06-13 10:57:09,711 : INFO : PROGRESS: at sentence #100000, processed 795129 words, keeping 50858 word types 2017-06-13 10:57:09,739 : INFO : PROGRESS: at sentence #110000, processed 874610 words, keeping 54191 word types 2017-06-13 10:57:09,778 : INFO : PROGRESS: at sentence #120000, processed 953346 words, keeping 57466 word types 2017-06-13 10:57:09,808 : INFO : PROGRESS: at sentence #130000, processed 1032400 words, keeping 60669 word types 2017-06-13 10:57:09,845 : INFO : PROGRESS: at sentence #140000, processed 1111635 words, keeping 63707 word types 2017-06-13 10:57:09,882 : INFO : PROGRESS: at sentence #150000, processed 1190295 words, keeping 66811 word types 2017-06-13 10:57:09,926 : INFO : PROGRESS: at sentence #160000, processed 1269988 words, keeping 69869 word types 2017-06-13 10:57:09,957 : INFO : PROGRESS: at sentence #170000, processed 1349438 words, keeping 72886 word types 2017-06-13 10:57:09,998 : INFO : PROGRESS: at sentence #180000, processed 1429565 words, keeping 75729 word types 2017-06-13 10:57:10,032 : INFO : PROGRESS: at sentence #190000, processed 1508942 words, keeping 78609 word types 2017-06-13 10:57:10,070 : INFO : PROGRESS: at sentence #200000, processed 1589739 words, keeping 81493 word types 2017-06-13 10:57:10,118 : INFO : PROGRESS: at sentence #210000, processed 1669736 words, keeping 84222 word types 2017-06-13 10:57:10,148 : INFO : PROGRESS: at sentence #220000, processed 1750264 words, keeping 87127 word types 2017-06-13 10:57:10,185 : INFO : PROGRESS: at sentence #230000, processed 1830237 words, keeping 89871 word types 2017-06-13 10:57:10,228 : INFO : PROGRESS: at sentence #240000, processed 1908612 words, keeping 92397 word types 2017-06-13 10:57:10,267 : INFO : PROGRESS: at sentence #250000, processed 1988491 words, keeping 95152 word types 2017-06-13 10:57:10,298 : INFO : PROGRESS: at sentence #260000, processed 2067787 words, keeping 97765 word types 2017-06-13 10:57:10,335 : INFO : PROGRESS: at sentence #270000, processed 2143349 words, keeping 100564 word types 2017-06-13 10:57:10,373 : INFO : PROGRESS: at sentence #280000, processed 2215784 words, keeping 103574 word types 2017-06-13 10:57:10,414 : INFO : PROGRESS: at sentence #290000, processed 2287523 words, keeping 106405 word types 2017-06-13 10:57:10,453 : INFO : PROGRESS: at sentence #300000, processed 2360178 words, keeping 109272 word types 2017-06-13 10:57:10,492 : INFO : PROGRESS: at sentence #310000, processed 2430322 words, keeping 111904 word types 2017-06-13 10:57:10,527 : INFO : PROGRESS: at sentence #320000, processed 2501115 words, keeping 114690 word types 2017-06-13 10:57:10,561 : INFO : PROGRESS: at sentence #330000, processed 2572705 words, keeping 117532 word types 2017-06-13 10:57:10,590 : INFO : PROGRESS: at sentence #340000, processed 2642795 words, keeping 120273 word types 2017-06-13 10:57:10,628 : INFO : PROGRESS: at sentence #350000, processed 2711654 words, keeping 122800 word types 2017-06-13 10:57:10,662 : INFO : PROGRESS: at sentence #360000, processed 2781223 words, keeping 125233 word types 2017-06-13 10:57:10,704 : INFO : PROGRESS: at sentence #370000, processed 2850297 words, keeping 127875 word types 2017-06-13 10:57:10,737 : INFO : PROGRESS: at sentence #380000, processed 2920057 words, keeping 130327 word types 2017-06-13 10:57:10,775 : INFO : PROGRESS: at sentence #390000, processed 2989031 words, keeping 132891 word types 2017-06-13 10:57:10,817 : INFO : PROGRESS: at sentence #400000, processed 3059768 words, keeping 135391 word types 2017-06-13 10:57:10,858 : INFO : PROGRESS: at sentence #410000, processed 3131069 words, keeping 137976 word types 2017-06-13 10:57:10,888 : INFO : PROGRESS: at sentence #420000, processed 3202384 words, keeping 140464 word types 2017-06-13 10:57:10,929 : INFO : PROGRESS: at sentence #430000, processed 3272626 words, keeping 143004 word types 2017-06-13 10:57:10,959 : INFO : PROGRESS: at sentence #440000, processed 3343377 words, keeping 145459 word types 2017-06-13 10:57:10,995 : INFO : PROGRESS: at sentence #450000, processed 3414876 words, keeping 148288 word types 2017-06-13 10:57:11,038 : INFO : PROGRESS: at sentence #460000, processed 3485287 words, keeping 150685 word types 2017-06-13 10:57:11,068 : INFO : PROGRESS: at sentence #470000, processed 3556983 words, keeping 153210 word types 2017-06-13 10:57:11,112 : INFO : PROGRESS: at sentence #480000, processed 3627027 words, keeping 155507 word types 2017-06-13 10:57:11,151 : INFO : PROGRESS: at sentence #490000, processed 3698482 words, keeping 157982 word types 2017-06-13 10:57:11,200 : INFO : PROGRESS: at sentence #500000, processed 3770601 words, keeping 160191 word types 2017-06-13 10:57:11,255 : INFO : PROGRESS: at sentence #510000, processed 3840697 words, keeping 162663 word types 2017-06-13 10:57:11,296 : INFO : PROGRESS: at sentence #520000, processed 3913162 words, keeping 165020 word types 2017-06-13 10:57:11,344 : INFO : PROGRESS: at sentence #530000, processed 3984518 words, keeping 167408 word types 2017-06-13 10:57:11,399 : INFO : PROGRESS: at sentence #540000, processed 4059071 words, keeping 169688 word types 2017-06-13 10:57:11,455 : INFO : PROGRESS: at sentence #550000, processed 4140080 words, keeping 171735 word types 2017-06-13 10:57:11,515 : INFO : PROGRESS: at sentence #560000, processed 4218589 words, keeping 173687 word types 2017-06-13 10:57:11,586 : INFO : 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types 2017-06-13 10:57:12,260 : INFO : PROGRESS: at sentence #660000, processed 5013222 words, keeping 193094 word types 2017-06-13 10:57:12,304 : INFO : PROGRESS: at sentence #670000, processed 5092207 words, keeping 195002 word types 2017-06-13 10:57:12,362 : INFO : PROGRESS: at sentence #680000, processed 5169807 words, keeping 196891 word types 2017-06-13 10:57:12,436 : INFO : PROGRESS: at sentence #690000, processed 5249505 words, keeping 198867 word types 2017-06-13 10:57:12,487 : INFO : PROGRESS: at sentence #700000, processed 5328640 words, keeping 200780 word types 2017-06-13 10:57:12,550 : INFO : PROGRESS: at sentence #710000, processed 5409239 words, keeping 202764 word types 2017-06-13 10:57:12,605 : INFO : PROGRESS: at sentence #720000, processed 5488070 words, keeping 204675 word types 2017-06-13 10:57:12,682 : INFO : PROGRESS: at sentence #730000, processed 5568699 words, keeping 206562 word types 2017-06-13 10:57:12,764 : INFO : PROGRESS: at sentence #740000, processed 5646562 words, keeping 208396 word types 2017-06-13 10:57:12,845 : INFO : PROGRESS: at sentence #750000, processed 5724742 words, keeping 210254 word types 2017-06-13 10:57:12,919 : INFO : PROGRESS: at sentence #760000, processed 5805056 words, keeping 212070 word types 2017-06-13 10:57:12,986 : INFO : PROGRESS: at sentence #770000, processed 5885569 words, keeping 213941 word types 2017-06-13 10:57:13,054 : INFO : PROGRESS: at sentence #780000, processed 5963819 words, keeping 215616 word types 2017-06-13 10:57:13,099 : INFO : PROGRESS: at sentence #790000, processed 6040469 words, keeping 217334 word types 2017-06-13 10:57:13,154 : INFO : PROGRESS: at sentence #800000, processed 6121104 words, keeping 219232 word types 2017-06-13 10:57:13,203 : INFO : PROGRESS: at sentence #810000, processed 6201279 words, keeping 221057 word types 2017-06-13 10:57:13,253 : INFO : PROGRESS: at sentence #820000, processed 6280836 words, keeping 222813 word types 2017-06-13 10:57:13,300 : INFO : 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types 2017-06-13 10:57:13,914 : INFO : PROGRESS: at sentence #920000, processed 7073725 words, keeping 240013 word types 2017-06-13 10:57:13,994 : INFO : PROGRESS: at sentence #930000, processed 7153523 words, keeping 241665 word types 2017-06-13 10:57:14,072 : INFO : PROGRESS: at sentence #940000, processed 7232996 words, keeping 243325 word types 2017-06-13 10:57:14,142 : INFO : PROGRESS: at sentence #950000, processed 7312447 words, keeping 245109 word types 2017-06-13 10:57:14,195 : INFO : PROGRESS: at sentence #960000, processed 7393953 words, keeping 246933 word types 2017-06-13 10:57:14,255 : INFO : PROGRESS: at sentence #970000, processed 7473347 words, keeping 248687 word types 2017-06-13 10:57:14,309 : INFO : PROGRESS: at sentence #980000, processed 7550987 words, keeping 250360 word types 2017-06-13 10:57:14,379 : INFO : PROGRESS: at sentence #990000, processed 7632214 words, keeping 252246 word types 2017-06-13 10:57:14,459 : INFO : PROGRESS: at sentence #1000000, processed 7713000 words, keeping 253999 word types 2017-06-13 10:57:14,541 : INFO : PROGRESS: at sentence #1010000, processed 7791926 words, keeping 255670 word types 2017-06-13 10:57:14,617 : INFO : PROGRESS: at sentence #1020000, processed 7871201 words, keeping 257344 word types 2017-06-13 10:57:14,674 : INFO : PROGRESS: at sentence #1030000, processed 7948428 words, keeping 258896 word types 2017-06-13 10:57:14,733 : INFO : PROGRESS: at sentence #1040000, processed 8026148 words, keeping 260590 word types 2017-06-13 10:57:14,785 : INFO : PROGRESS: at sentence #1050000, processed 8106395 words, keeping 262377 word types 2017-06-13 10:57:14,841 : INFO : PROGRESS: at sentence #1060000, processed 8184892 words, keeping 264094 word types 2017-06-13 10:57:14,889 : INFO : PROGRESS: at sentence #1070000, processed 8264486 words, keeping 265668 word types 2017-06-13 10:57:14,947 : INFO : PROGRESS: at sentence #1080000, processed 8342734 words, keeping 267237 word types 2017-06-13 10:57:14,998 : INFO : PROGRESS: at sentence #1090000, processed 8419653 words, keeping 268881 word types 2017-06-13 10:57:15,054 : INFO : PROGRESS: at sentence #1100000, processed 8498098 words, keeping 270541 word types 2017-06-13 10:57:15,099 : INFO : PROGRESS: at sentence #1110000, processed 8576992 words, keeping 272296 word types 2017-06-13 10:57:15,149 : INFO : PROGRESS: at sentence #1120000, processed 8655050 words, keeping 273847 word types 2017-06-13 10:57:15,234 : INFO : PROGRESS: at sentence #1130000, processed 8734088 words, keeping 275440 word types 2017-06-13 10:57:15,351 : INFO : PROGRESS: at sentence #1140000, processed 8812366 words, keeping 277034 word types 2017-06-13 10:57:15,463 : INFO : PROGRESS: at sentence #1150000, processed 8889785 words, keeping 278622 word types 2017-06-13 10:57:15,573 : INFO : PROGRESS: at sentence #1160000, processed 8969027 words, keeping 280364 word types 2017-06-13 10:57:15,691 : INFO : PROGRESS: at sentence #1170000, processed 9048874 words, keeping 282046 word types 2017-06-13 10:57:15,771 : INFO : PROGRESS: at sentence #1180000, processed 9128858 words, keeping 283702 word types 2017-06-13 10:57:15,850 : INFO : PROGRESS: at sentence #1190000, processed 9207671 words, keeping 285245 word types 2017-06-13 10:57:15,966 : INFO : PROGRESS: at sentence #1200000, processed 9285618 words, keeping 286846 word types 2017-06-13 10:57:16,077 : INFO : PROGRESS: at sentence #1210000, processed 9364027 words, keeping 288447 word types 2017-06-13 10:57:16,199 : INFO : PROGRESS: at sentence #1220000, processed 9445529 words, keeping 290238 word types 2017-06-13 10:57:16,295 : INFO : PROGRESS: at sentence #1230000, processed 9525420 words, keeping 291946 word types 2017-06-13 10:57:16,372 : INFO : PROGRESS: at sentence #1240000, processed 9605919 words, keeping 293560 word types 2017-06-13 10:57:16,471 : INFO : PROGRESS: at sentence #1250000, processed 9685296 words, keeping 295070 word types 2017-06-13 10:57:16,555 : INFO : PROGRESS: at sentence #1260000, processed 9764593 words, keeping 296709 word types 2017-06-13 10:57:16,641 : INFO : PROGRESS: at sentence #1270000, processed 9842747 words, keeping 298307 word types 2017-06-13 10:57:16,731 : INFO : PROGRESS: at sentence #1280000, processed 9925260 words, keeping 299949 word types 2017-06-13 10:57:16,804 : INFO : PROGRESS: at sentence #1290000, processed 10005948 words, keeping 301562 word types 2017-06-13 10:57:16,910 : INFO : PROGRESS: at sentence #1300000, processed 10086352 words, keeping 303091 word types 2017-06-13 10:57:17,001 : INFO : PROGRESS: at sentence #1310000, processed 10166106 words, keeping 304514 word types 2017-06-13 10:57:17,111 : INFO : PROGRESS: at sentence #1320000, processed 10244520 words, keeping 306067 word types 2017-06-13 10:57:17,184 : INFO : PROGRESS: at sentence #1330000, processed 10324510 words, keeping 307696 word types 2017-06-13 10:57:17,282 : INFO : PROGRESS: at sentence #1340000, processed 10405155 words, keeping 309367 word types 2017-06-13 10:57:17,364 : INFO : PROGRESS: at sentence #1350000, processed 10486453 words, keeping 310963 word types 2017-06-13 10:57:17,439 : INFO : PROGRESS: at sentence #1360000, processed 10567069 words, keeping 312481 word types 2017-06-13 10:57:17,522 : INFO : PROGRESS: at sentence #1370000, processed 10646058 words, keeping 314026 word types 2017-06-13 10:57:17,604 : INFO : PROGRESS: at sentence #1380000, processed 10724496 words, keeping 315584 word types 2017-06-13 10:57:17,695 : INFO : PROGRESS: at sentence #1390000, processed 10805953 words, keeping 317344 word types 2017-06-13 10:57:17,769 : INFO : PROGRESS: at sentence #1400000, processed 10887684 words, keeping 318972 word types 2017-06-13 10:57:17,871 : INFO : PROGRESS: at sentence #1410000, processed 10967460 words, keeping 320550 word types 2017-06-13 10:57:17,988 : INFO : PROGRESS: at sentence #1420000, processed 11047802 words, keeping 322121 word types 2017-06-13 10:57:18,103 : INFO : PROGRESS: at sentence #1430000, processed 11126260 words, keeping 323782 word types 2017-06-13 10:57:18,222 : INFO : PROGRESS: at sentence #1440000, processed 11203584 words, keeping 325332 word types 2017-06-13 10:57:18,342 : INFO : PROGRESS: at sentence #1450000, processed 11283872 words, keeping 326962 word types 2017-06-13 10:57:18,453 : INFO : PROGRESS: at sentence #1460000, processed 11362918 words, keeping 328540 word types 2017-06-13 10:57:18,572 : INFO : PROGRESS: at sentence #1470000, processed 11441986 words, keeping 330047 word types 2017-06-13 10:57:18,696 : INFO : PROGRESS: at sentence #1480000, processed 11521110 words, keeping 331512 word types 2017-06-13 10:57:18,803 : INFO : PROGRESS: at sentence #1490000, processed 11599571 words, keeping 333020 word types 2017-06-13 10:57:18,909 : INFO : PROGRESS: at sentence #1500000, processed 11677291 words, keeping 334490 word types 2017-06-13 10:57:18,990 : INFO : PROGRESS: at sentence #1510000, processed 11755854 words, keeping 336018 word types 2017-06-13 10:57:19,062 : INFO : PROGRESS: at sentence #1520000, processed 11836720 words, keeping 337632 word types 2017-06-13 10:57:19,137 : INFO : PROGRESS: at sentence #1530000, processed 11916536 words, keeping 339122 word types 2017-06-13 10:57:19,217 : INFO : PROGRESS: at sentence #1540000, processed 11994819 words, keeping 340547 word types 2017-06-13 10:57:19,301 : INFO : PROGRESS: at sentence #1550000, processed 12074553 words, keeping 342132 word types 2017-06-13 10:57:19,387 : INFO : PROGRESS: at sentence #1560000, processed 12152895 words, keeping 343591 word types 2017-06-13 10:57:19,483 : INFO : PROGRESS: at sentence #1570000, processed 12231207 words, keeping 345141 word types 2017-06-13 10:57:19,603 : INFO : PROGRESS: at sentence #1580000, processed 12311453 words, keeping 346797 word types 2017-06-13 10:57:19,741 : INFO : PROGRESS: at sentence #1590000, processed 12391354 words, keeping 348340 word types 2017-06-13 10:57:20,237 : INFO : PROGRESS: at sentence #1600000, processed 12465628 words, keeping 350048 word types 2017-06-13 10:57:20,364 : INFO : PROGRESS: at sentence #1610000, processed 12539399 words, keeping 351944 word types 2017-06-13 10:57:20,487 : INFO : PROGRESS: at sentence #1620000, processed 12609274 words, keeping 353533 word types 2017-06-13 10:57:20,610 : INFO : PROGRESS: at sentence #1630000, processed 12678820 words, keeping 355216 word types 2017-06-13 10:57:20,727 : INFO : PROGRESS: at sentence #1640000, processed 12750943 words, keeping 357008 word types 2017-06-13 10:57:20,840 : INFO : PROGRESS: at sentence #1650000, processed 12821981 words, keeping 358645 word types 2017-06-13 10:57:20,948 : INFO : PROGRESS: at sentence #1660000, processed 12896085 words, keeping 360475 word types 2017-06-13 10:57:21,020 : INFO : PROGRESS: at sentence #1670000, processed 12970597 words, keeping 362244 word types 2017-06-13 10:57:21,102 : INFO : PROGRESS: at sentence #1680000, processed 13041271 words, keeping 364022 word types 2017-06-13 10:57:21,190 : INFO : PROGRESS: at sentence #1690000, processed 13113320 words, keeping 365863 word types 2017-06-13 10:57:21,267 : INFO : PROGRESS: at sentence #1700000, processed 13182576 words, keeping 367441 word types 2017-06-13 10:57:21,367 : INFO : PROGRESS: at sentence #1710000, processed 13254857 words, keeping 369212 word types 2017-06-13 10:57:21,474 : INFO : PROGRESS: at sentence #1720000, processed 13325870 words, keeping 370843 word types 2017-06-13 10:57:21,557 : INFO : PROGRESS: at sentence #1730000, processed 13396243 words, keeping 372453 word types 2017-06-13 10:57:21,661 : INFO : PROGRESS: at sentence #1740000, processed 13468987 words, keeping 374137 word types 2017-06-13 10:57:21,777 : INFO : PROGRESS: at sentence #1750000, processed 13538273 words, keeping 375633 word types 2017-06-13 10:57:21,894 : INFO : PROGRESS: at sentence #1760000, processed 13610699 words, keeping 377298 word types 2017-06-13 10:57:21,997 : INFO : PROGRESS: at sentence #1770000, processed 13681604 words, keeping 378834 word types 2017-06-13 10:57:22,067 : INFO : PROGRESS: at sentence #1780000, processed 13752169 words, keeping 380528 word types 2017-06-13 10:57:22,178 : INFO : PROGRESS: at sentence #1790000, processed 13824314 words, keeping 382256 word types 2017-06-13 10:57:22,269 : INFO : PROGRESS: at sentence #1800000, processed 13892894 words, keeping 383800 word types 2017-06-13 10:57:22,388 : INFO : PROGRESS: at sentence #1810000, processed 13965333 words, keeping 385549 word types 2017-06-13 10:57:22,509 : INFO : PROGRESS: at sentence #1820000, processed 14036231 words, keeping 387205 word types 2017-06-13 10:57:22,639 : INFO : PROGRESS: at sentence #1830000, processed 14106761 words, keeping 388791 word types 2017-06-13 10:57:22,760 : INFO : PROGRESS: at sentence #1840000, processed 14178079 words, keeping 390511 word types 2017-06-13 10:57:22,883 : INFO : PROGRESS: at sentence #1850000, processed 14250404 words, keeping 392358 word types 2017-06-13 10:57:22,998 : INFO : PROGRESS: at sentence #1860000, processed 14322694 words, keeping 394045 word types 2017-06-13 10:57:23,110 : INFO : PROGRESS: at sentence #1870000, processed 14393451 words, keeping 395622 word types 2017-06-13 10:57:23,225 : INFO : PROGRESS: at sentence #1880000, processed 14464178 words, keeping 397338 word types 2017-06-13 10:57:23,324 : INFO : PROGRESS: at sentence #1890000, processed 14534281 words, keeping 398992 word types 2017-06-13 10:57:23,466 : INFO : PROGRESS: at sentence #1900000, processed 14604763 words, keeping 400602 word types 2017-06-13 10:57:23,580 : INFO : PROGRESS: at sentence #1910000, processed 14673345 words, keeping 402279 word types 2017-06-13 10:57:23,670 : INFO : PROGRESS: at sentence #1920000, processed 14741699 words, keeping 403815 word types 2017-06-13 10:57:23,787 : INFO : PROGRESS: at sentence #1930000, processed 14811763 words, keeping 405544 word types 2017-06-13 10:57:23,935 : INFO : PROGRESS: at sentence #1940000, processed 14882639 words, keeping 407226 word types 2017-06-13 10:57:24,028 : INFO : PROGRESS: at sentence #1950000, processed 14952044 words, keeping 408744 word types 2017-06-13 10:57:24,142 : INFO : PROGRESS: at sentence #1960000, processed 15022400 words, keeping 410298 word types 2017-06-13 10:57:24,251 : INFO : PROGRESS: at sentence #1970000, processed 15090766 words, keeping 411960 word types 2017-06-13 10:57:24,365 : INFO : PROGRESS: at sentence #1980000, processed 15157397 words, keeping 413545 word types 2017-06-13 10:57:24,509 : INFO : PROGRESS: at sentence #1990000, processed 15226060 words, keeping 415163 word types 2017-06-13 10:57:24,661 : INFO : PROGRESS: at sentence #2000000, processed 15297475 words, keeping 416826 word types 2017-06-13 10:57:24,775 : INFO : PROGRESS: at sentence #2010000, processed 15368685 words, keeping 418483 word types 2017-06-13 10:57:24,921 : INFO : PROGRESS: at sentence #2020000, processed 15438646 words, keeping 420033 word types 2017-06-13 10:57:25,044 : INFO : PROGRESS: at sentence #2030000, processed 15508025 words, keeping 421640 word types 2017-06-13 10:57:25,179 : INFO : PROGRESS: at sentence #2040000, processed 15577975 words, keeping 423178 word types 2017-06-13 10:57:25,276 : INFO : PROGRESS: at sentence #2050000, processed 15648535 words, keeping 424736 word types 2017-06-13 10:57:25,400 : INFO : PROGRESS: at sentence #2060000, processed 15717864 words, keeping 426242 word types 2017-06-13 10:57:25,464 : INFO : PROGRESS: at sentence #2070000, processed 15785929 words, keeping 427860 word types 2017-06-13 10:57:25,558 : INFO : PROGRESS: at sentence #2080000, processed 15854839 words, keeping 429462 word types 2017-06-13 10:57:25,630 : INFO : PROGRESS: at sentence #2090000, processed 15925993 words, keeping 431063 word types 2017-06-13 10:57:25,728 : INFO : PROGRESS: at sentence #2100000, processed 15995727 words, keeping 432617 word types 2017-06-13 10:57:25,842 : INFO : PROGRESS: at sentence #2110000, processed 16065927 words, keeping 434111 word types 2017-06-13 10:57:25,945 : INFO : PROGRESS: at sentence #2120000, processed 16137110 words, keeping 435663 word types 2017-06-13 10:57:26,069 : INFO : PROGRESS: at sentence #2130000, processed 16209248 words, keeping 437312 word types 2017-06-13 10:57:26,175 : INFO : PROGRESS: at sentence #2140000, processed 16279868 words, keeping 438768 word types 2017-06-13 10:57:26,318 : INFO : PROGRESS: at sentence #2150000, processed 16347835 words, keeping 440277 word types 2017-06-13 10:57:26,431 : INFO : PROGRESS: at sentence #2160000, processed 16419241 words, keeping 442011 word types 2017-06-13 10:57:26,578 : INFO : PROGRESS: at sentence #2170000, processed 16491061 words, keeping 443828 word types 2017-06-13 10:57:26,698 : INFO : PROGRESS: at sentence #2180000, processed 16563091 words, keeping 445509 word types 2017-06-13 10:57:26,842 : INFO : PROGRESS: at sentence #2190000, processed 16635860 words, keeping 447240 word types 2017-06-13 10:57:26,997 : INFO : PROGRESS: at sentence #2200000, processed 16707763 words, keeping 448772 word types 2017-06-13 10:57:27,124 : INFO : PROGRESS: at sentence #2210000, processed 16780096 words, keeping 450354 word types 2017-06-13 10:57:27,265 : INFO : PROGRESS: at sentence #2220000, processed 16849425 words, keeping 451849 word types 2017-06-13 10:57:27,432 : INFO : PROGRESS: at sentence #2230000, processed 16919034 words, keeping 453387 word types 2017-06-13 10:57:27,565 : INFO : PROGRESS: at sentence #2240000, processed 16988214 words, keeping 454911 word types 2017-06-13 10:57:27,679 : INFO : PROGRESS: at sentence #2250000, processed 17060305 words, keeping 456738 word types 2017-06-13 10:57:27,823 : INFO : PROGRESS: at sentence #2260000, processed 17133961 words, keeping 458580 word types 2017-06-13 10:57:27,993 : INFO : PROGRESS: at sentence #2270000, processed 17205932 words, keeping 460229 word types 2017-06-13 10:57:28,164 : INFO : PROGRESS: at sentence #2280000, processed 17278203 words, keeping 461891 word types 2017-06-13 10:57:28,309 : INFO : PROGRESS: at sentence #2290000, processed 17346956 words, keeping 463401 word types 2017-06-13 10:57:28,379 : INFO : PROGRESS: at sentence #2300000, processed 17417153 words, keeping 465084 word types 2017-06-13 10:57:28,531 : INFO : PROGRESS: at sentence #2310000, processed 17490073 words, keeping 466870 word types 2017-06-13 10:57:28,628 : INFO : PROGRESS: at sentence #2320000, processed 17561733 words, keeping 468564 word types 2017-06-13 10:57:28,707 : INFO : PROGRESS: at sentence #2330000, processed 17632578 words, keeping 470150 word types 2017-06-13 10:57:28,795 : INFO : PROGRESS: at sentence #2340000, processed 17703440 words, keeping 471744 word types 2017-06-13 10:57:28,946 : INFO : PROGRESS: at sentence #2350000, processed 17774024 words, keeping 473384 word types 2017-06-13 10:57:29,045 : INFO : PROGRESS: at sentence #2360000, processed 17844827 words, keeping 474888 word types 2017-06-13 10:57:29,183 : INFO : PROGRESS: at sentence #2370000, processed 17914010 words, keeping 476346 word types 2017-06-13 10:57:29,353 : INFO : PROGRESS: at sentence #2380000, processed 17982911 words, keeping 477779 word types 2017-06-13 10:57:29,486 : INFO : PROGRESS: at sentence #2390000, processed 18053750 words, keeping 479309 word types 2017-06-13 10:57:29,636 : INFO : PROGRESS: at sentence #2400000, processed 18125559 words, keeping 481038 word types 2017-06-13 10:57:29,803 : INFO : PROGRESS: at sentence #2410000, processed 18196608 words, keeping 482713 word types 2017-06-13 10:57:29,961 : INFO : PROGRESS: at sentence #2420000, processed 18266531 words, keeping 484248 word types 2017-06-13 10:57:30,130 : INFO : PROGRESS: at sentence #2430000, processed 18337028 words, keeping 485761 word types 2017-06-13 10:57:30,292 : INFO : PROGRESS: at sentence #2440000, processed 18410486 words, keeping 487210 word types 2017-06-13 10:57:30,402 : INFO : PROGRESS: at sentence #2450000, processed 18480725 words, keeping 488724 word types 2017-06-13 10:57:30,551 : INFO : PROGRESS: at sentence #2460000, processed 18549472 words, keeping 490116 word types 2017-06-13 10:57:30,675 : INFO : PROGRESS: at sentence #2470000, processed 18617927 words, keeping 491585 word types 2017-06-13 10:57:30,774 : INFO : PROGRESS: at sentence #2480000, processed 18688921 words, keeping 493151 word types 2017-06-13 10:57:30,880 : INFO : PROGRESS: at sentence #2490000, processed 18761560 words, keeping 494830 word types 2017-06-13 10:57:31,082 : INFO : PROGRESS: at sentence #2500000, processed 18833620 words, keeping 496536 word types 2017-06-13 10:57:31,204 : INFO : PROGRESS: at sentence #2510000, processed 18904238 words, keeping 498117 word types 2017-06-13 10:57:31,366 : INFO : PROGRESS: at sentence #2520000, processed 18976858 words, keeping 499574 word types 2017-06-13 10:57:31,505 : INFO : PROGRESS: at sentence #2530000, processed 19049308 words, keeping 501088 word types 2017-06-13 10:57:31,660 : INFO : PROGRESS: at sentence #2540000, processed 19120492 words, keeping 502613 word types 2017-06-13 10:57:31,816 : INFO : PROGRESS: at sentence #2550000, processed 19189525 words, keeping 503994 word types 2017-06-13 10:57:31,983 : INFO : PROGRESS: at sentence #2560000, processed 19258353 words, keeping 505378 word types 2017-06-13 10:57:32,139 : INFO : PROGRESS: at sentence #2570000, processed 19328881 words, keeping 507053 word types 2017-06-13 10:57:32,217 : INFO : PROGRESS: at sentence #2580000, processed 19400483 words, keeping 508646 word types 2017-06-13 10:57:32,326 : INFO : PROGRESS: at sentence #2590000, processed 19472900 words, keeping 510268 word types 2017-06-13 10:57:32,448 : INFO : PROGRESS: at sentence #2600000, processed 19543458 words, keeping 511744 word types 2017-06-13 10:57:32,557 : INFO : PROGRESS: at sentence #2610000, processed 19616321 words, keeping 513191 word types 2017-06-13 10:57:32,723 : INFO : PROGRESS: at sentence #2620000, processed 19690897 words, keeping 514556 word types 2017-06-13 10:57:32,838 : INFO : PROGRESS: at sentence #2630000, processed 19762575 words, keeping 515998 word types 2017-06-13 10:57:32,968 : INFO : PROGRESS: at sentence #2640000, processed 19832273 words, keeping 517455 word types 2017-06-13 10:57:33,103 : INFO : PROGRESS: at sentence #2650000, processed 19902527 words, keeping 518878 word types 2017-06-13 10:57:33,238 : INFO : PROGRESS: at sentence #2660000, processed 19974472 words, keeping 520522 word types 2017-06-13 10:57:33,349 : INFO : PROGRESS: at sentence #2670000, processed 20047879 words, keeping 522139 word types 2017-06-13 10:57:33,423 : INFO : PROGRESS: at sentence #2680000, processed 20119566 words, keeping 523676 word types 2017-06-13 10:57:33,491 : INFO : collected 524149 word types from a corpus of 20141378 raw words and 2683104 sentences 2017-06-13 10:57:33,494 : INFO : Loading a fresh vocabulary 2017-06-13 10:57:34,436 : INFO : min_count=30 retains 18374 unique words (3% of original 524149, drops 505775) 2017-06-13 10:57:34,438 : INFO : min_count=30 leaves 19045643 word corpus (94% of original 20141378, drops 1095735) 2017-06-13 10:57:34,771 : INFO : deleting the raw counts dictionary of 524149 items 2017-06-13 10:57:34,788 : INFO : sample=0.001 downsamples 58 most-common words 2017-06-13 10:57:34,791 : INFO : downsampling leaves estimated 14855910 word corpus (78.0% of prior 19045643) 2017-06-13 10:57:34,794 : INFO : estimated required memory for 18374 words and 400 dimensions: 67983800 bytes 2017-06-13 10:57:35,070 : INFO : resetting layer weights 2017-06-13 10:57:36,857 : INFO : training model with 4 workers on 18374 vocabulary and 400 features, using sg=0 hs=0 sample=0.001 negative=5 window=10 2017-06-13 10:57:37,986 : INFO : PROGRESS: at 0.10% examples, 72433 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:39,125 : INFO : PROGRESS: at 0.22% examples, 78360 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:40,199 : INFO : PROGRESS: at 0.34% examples, 79737 words/s, in_qsize 8, out_qsize 0 2017-06-13 10:57:41,253 : INFO : PROGRESS: at 0.44% examples, 79045 words/s, in_qsize 6, out_qsize 1 2017-06-13 10:57:42,471 : INFO : PROGRESS: at 0.57% examples, 80287 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:43,526 : INFO : PROGRESS: at 0.67% examples, 78639 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:44,527 : INFO : PROGRESS: at 0.76% examples, 78026 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:45,540 : INFO : PROGRESS: at 0.88% examples, 79979 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:46,552 : INFO : PROGRESS: at 0.99% examples, 80740 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:47,687 : INFO : PROGRESS: at 1.13% examples, 81806 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:48,702 : INFO : PROGRESS: at 1.27% examples, 84141 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:49,729 : INFO : PROGRESS: at 1.41% examples, 86009 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:50,768 : INFO : PROGRESS: at 1.56% examples, 88067 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:51,916 : INFO : PROGRESS: at 1.69% examples, 88188 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:52,926 : INFO : PROGRESS: at 1.79% examples, 87698 words/s, in_qsize 7, out_qsize 1 2017-06-13 10:57:54,058 : INFO : PROGRESS: at 1.89% examples, 86651 words/s, in_qsize 8, out_qsize 1 2017-06-13 10:57:55,140 : INFO : PROGRESS: at 2.02% examples, 86764 words/s, in_qsize 8, out_qsize 0 2017-06-13 10:57:56,143 : INFO : PROGRESS: at 2.14% examples, 86838 words/s, in_qsize 6, out_qsize 0 2017-06-13 10:57:57,248 : INFO : PROGRESS: at 2.29% examples, 87200 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:58,277 : INFO : PROGRESS: at 2.42% examples, 87476 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:57:59,424 : INFO : PROGRESS: at 2.57% examples, 87595 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:00,428 : INFO : PROGRESS: at 2.69% examples, 87299 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:01,521 : INFO : PROGRESS: at 2.82% examples, 87009 words/s, in_qsize 8, out_qsize 0 2017-06-13 10:58:02,638 : INFO : PROGRESS: at 2.93% examples, 86101 words/s, in_qsize 6, out_qsize 1 2017-06-13 10:58:03,743 : INFO : PROGRESS: at 3.06% examples, 86123 words/s, in_qsize 6, out_qsize 2 2017-06-13 10:58:04,874 : INFO : PROGRESS: at 3.22% examples, 86596 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:05,883 : INFO : PROGRESS: at 3.36% examples, 86878 words/s, in_qsize 7, out_qsize 1 2017-06-13 10:58:06,943 : INFO : PROGRESS: at 3.50% examples, 87244 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:07,946 : INFO : PROGRESS: at 3.62% examples, 87039 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:09,065 : INFO : PROGRESS: at 3.75% examples, 86764 words/s, in_qsize 6, out_qsize 1 2017-06-13 10:58:10,139 : INFO : PROGRESS: at 3.87% examples, 86628 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:11,183 : INFO : PROGRESS: at 3.97% examples, 86142 words/s, in_qsize 8, out_qsize 0 2017-06-13 10:58:12,224 : INFO : PROGRESS: at 4.10% examples, 86320 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:13,348 : INFO : PROGRESS: at 4.23% examples, 86495 words/s, in_qsize 8, out_qsize 0 2017-06-13 10:58:14,444 : INFO : PROGRESS: at 4.36% examples, 86721 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:15,555 : INFO : PROGRESS: at 4.48% examples, 86701 words/s, in_qsize 6, out_qsize 1 2017-06-13 10:58:16,577 : INFO : PROGRESS: at 4.58% examples, 86508 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:17,727 : INFO : PROGRESS: at 4.69% examples, 86061 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:18,801 : INFO : PROGRESS: at 4.79% examples, 85796 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:19,813 : INFO : PROGRESS: at 4.88% examples, 85492 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:20,966 : INFO : PROGRESS: at 4.97% examples, 84765 words/s, in_qsize 7, out_qsize 0 2017-06-13 10:58:22,152 : INFO : PROGRESS: at 5.07% examples, 84169 words/s, in_qsize 7, out_qsize 0 2017-06-13 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finished; awaiting finish of 3 more threads 2017-06-13 11:16:35,826 : INFO : worker thread finished; awaiting finish of 2 more threads 2017-06-13 11:16:35,834 : INFO : worker thread finished; awaiting finish of 1 more threads 2017-06-13 11:16:35,836 : INFO : worker thread finished; awaiting finish of 0 more threads 2017-06-13 11:16:35,876 : INFO : training on 100706890 raw words (74280580 effective words) took 1139.0s, 65215 effective words/s 2017-06-13 11:16:38,765 : INFO : precomputing L2-norms of word weight vectors 2017-06-13 11:16:39,853 : INFO : saving Word2Vec object under 400features_30minwords_10context_twitter, separately None 2017-06-13 11:16:40,163 : INFO : not storing attribute syn0norm 2017-06-13 11:16:40,186 : INFO : not storing attribute cum_table 2017-06-13 11:16:58,133 : INFO : saved 400features_30minwords_10context_twitter
# Load Model - Restart Point
from gensim.models import Word2Vec
model = Word2Vec.load("output/400features_30minwords_10context_twitter")
word_vectors = model.wv.syn0
num_clusters = int(word_vectors.shape[0] / 10)
print(num_clusters)
1837
model = Word2Vec.load("output/300features_40minwords_10context")
print(model['girl'].size)
print(model['girl'][0:10])
300 [-0.041204 -0.02197601 0.00092597 -0.01058798 -0.06259201 -0.04384787 0.06327199 0.06846711 0.09706726 0.03667281]
model.most_similar_cosmul(positive=['woman', 'king'],\
negative=['man'])
[('queen', 0.8604776859283447),
('princess', 0.8214038014411926),
('prince', 0.7916637063026428),
('juliet', 0.7827329039573669),
('lion', 0.7793508768081665),
('jane', 0.7760097980499268),
('bride', 0.7709850668907166),
('countess', 0.7636075615882874),
('stepmother', 0.7608541250228882),
('stella', 0.7595590949058533)]
model.most_similar_cosmul(positive=['woman'])
[('lady', 0.846655547618866),
('prostitute', 0.8460174202919006),
('girl', 0.8330182433128357),
('widow', 0.825380265712738),
('man', 0.8194788098335266),
('heiress', 0.8098702430725098),
('housewife', 0.8089634776115417),
('waitress', 0.8070055842399597),
('nun', 0.7900646328926086),
('nurse', 0.7890486121177673)]
# Capability Testing
print( model.doesnt_match("man woman child kitchen".split()))
for item in model.most_similar("man"):
print(item[0]," ",end="")
print()
for item in model.most_similar("queen"):
print(item[0]," ",end="")
print()
for item in model.most_similar("awful"):
print(item[0]," ",end="")
print()
2017-06-14 12:49:07,943 : INFO : precomputing L2-norms of word weight vectors
kitchen boy dude woman guy girl kid gosh beast chick lady king lion goddess dancer rendition caribbean course) clark mary baker terrible horrible horrid aweful icky rubbish incredible unpleasant miserable overwhelming
from sklearn.cluster import KMeans
import time
start = time.time() # Start time
print ("Start Clustering.")
# Set "k" (num_clusters) to be 1/5th of the vocabulary size, or an
# average of 5 words per cluster
word_vectors = model.wv.syn0
num_clusters = int(word_vectors.shape[0] / 10)
# Initalize a k-means object and use it to extract centroids
kmeans_clustering = KMeans( n_clusters = num_clusters, n_jobs = -1)
idx = kmeans_clustering.fit_predict( word_vectors )
# Get the end time and print how long the process took
end = time.time()
elapsed = end - start
print ("Time taken for K Means clustering: ", elapsed, "seconds.")
Start Clustering. Time taken for K Means clustering: 800.1646106243134 seconds.
# Create a Word / Index dictionary, mapping each vocabulary word to a cluster number
word_centroid_map = dict(zip( model.wv.index2word, idx ))
save_obj(word_centroid_map,"twitter_word_centroid_map")
word_centroid_map = load_obj("twitter_word_centroid_map")
import csv
with open('dict.csv', 'w') as csv_file:
writer = csv.writer(csv_file)
for key, value in word_centroid_map.items():
writer.writerow([key, value])
# Test the first 10 clusters
for cluster in range(0,10):
#
# Print the cluster number
print ("\nCluster %d" % cluster)
#
# Find all of the words for that cluster number, and print them out
words = []
for i in range(0,len(word_centroid_map.values())):
if( list(word_centroid_map.values())[i] == cluster ):
words.append(list(word_centroid_map.keys())[i])
print (words)
Cluster 0 ['supporting', 'promoting', 'amongst', 'interviewing'] Cluster 1 ["baby's", "husband's", 'fir', "boyfriend's", 'baptism', "gf's", 'crate', 'nans', 'arrangements', 'inlaws', "grandmother's", 'grandmothers', 'granddaughter'] Cluster 2 ['spore'] Cluster 3 ['burning', 'watering', 'weeding', 'peeled', 'scrubbing'] Cluster 4 ['windsor', 'lancaster', 'courts', 'daytona', 'ballpark'] Cluster 5 [':0', '*high', 'owell'] Cluster 6 ['phone', 'computer', 'bb', 'blackberry', 'cell', 'battery', 'comp', 'phones', 'charger', 'contacts', 'headphones', 'fone', 'batteries', 'sidekick', 'cellphone', "phone's", 'earphones', 'crackberry', 'ipods', 'splinter', 'puter', 'i-pod'] Cluster 7 ['dim', 'snickers', 'granola', 'twix', 'pastry', 'gourmet', 'boxed', 'choclate', 'mochi'] Cluster 8 ['me"', "'", 'seek', "me'", 'people"', '"fly', 'dance"'] Cluster 9 ['brady', 'musicals', 'rugrats', 'hogs']
from gensim.models.word2vec import Word2Vec
from sklearn.manifold import TSNE
from sklearn.datasets import fetch_20newsgroups
import re
import matplotlib.pyplot as plt
X = model[model.wv.vocab]
tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(X)
plt.scatter(X_tsne[:, 0], X_tsne[:, 1])
plt.show()
def create_bag_of_centroids( wordlist, word_centroid_map ):
#
# The number of clusters is equal to the highest cluster index
# in the word / centroid map
num_centroids = max( word_centroid_map.values() ) + 1
#
# Pre-allocate the bag of centroids vector (for speed)
bag_of_centroids = np.zeros( num_centroids, dtype="float32" )
#
# Loop over the words in the review. If the word is in the vocabulary,
# find which cluster it belongs to, and increment that cluster count
# by one
for word in wordlist:
if word in word_centroid_map:
index = word_centroid_map[word]
bag_of_centroids[index] += 1
#
# Return the "bag of centroids"
return bag_of_centroids
# Pre-allocate an array for the training set bags of centroids (for speed)
train_centroids = np.zeros( (train["tweet"].size, 1837), \
dtype="float32" )
counter = 0
for tweet in train["tweet"]:
train_centroids[counter] = create_bag_of_centroids( process_tweet( tweet, True ), word_centroid_map )
counter += 1
from sklearn.ensemble import RandomForestClassifier
# Fit a random forest and extract predictions
forest = RandomForestClassifier(n_estimators = 200, n_jobs=-1)
# Fitting the forest may take a few minutes
print ("Fitting a random forest to labeled training data...")
forest = forest.fit(train_centroids,train["sentiment"])
print ("Saveing Forest as file...")
save_obj(forest,"twitter_forest")
forest = load_obj("twitter_forest")
# Pre-allocate an array for the training set bags of centroids (for speed)
test_centroids = np.zeros( (test["tweet"].size, 1837), \
dtype="float32" )
counter = 0
for tweet in test["tweet"]:
test_centroids[counter] = create_bag_of_centroids( process_tweet( tweet, True ), word_centroid_map )
counter += 1
from sklearn.metrics import accuracy_score
print ("Predicting test sets...")
result = forest.predict(test_centroids)
print ("Computing acuraccy score..")
print(accuracy_score(test["sentiment"], result))
Predicting test sets... Computing acuraccy score.. 0.766662560059
# Load Forest
load_forest = load_obj("twitter_forest")
# User Define
query = "Comey"
max_tweets = 500
print ("Loading Tweets...")
# Example Search Result
searched_tweets = [status.text for status in tweepy.Cursor(api.search, q=query, lang="en").items(max_tweets)]
print ("Pre-allocating an Array...")
# Pre-allocate an array for the training set bags of centroids (for speed)
user_centroids = np.zeros( (max_tweets, num_clusters), \
dtype="float32" )
print ("Producing Test Centroids...")
counter = 0
for tweet in searched_tweets:
user_centroids[counter] = create_bag_of_centroids( process_tweet( tweet, True ), word_centroid_map )
counter += 1
print ("Predicting Test Sets...")
result = load_forest.predict(user_centroids)
import numpy as np
unique, counts = np.unique(result, return_counts=True)
result_dict = dict(zip(unique, counts))
print ("\n\nPrediction :")
print (" Positive - %.2f%%,\n Negative - %.2f%%" %
(result_dict.get(4, 0)*100/len(result),\
result_dict.get(0, 0)*100/len(result)))
Loading Tweets...
Pre-allocating an Array...
Producing Test Centroids...
Predicting Test Sets...
Prediction :
Positive - 63.40%,
Negative - 36.60%
def switch(x):
if x == 4:
return 'Negative'
else:
return 'Positive'
formatted_result = list(map(switch, result))
output = list(zip(formatted_result,searched_tweets))
for item in output[0:10]:
print(item[0]+"\n"+item[1]+"\n")
Positive RT @CapehartJ: The Comey hearing is clearest sign I've seen since Inauguration Day that the check and balance of the co-equal branch of gov… Negative RT @20committee: Trump fired Comey. Now he's floating firing Mueller. That a lot of political capital expended to kill an investigation tha… Negative EXCLUSIVE: DOJ Brass Says Comey Struck Insider Immunity Deal With Mueller To Avoid Criminal Charges https://t.co/Nt6MKJMUht Deep State Negative RT @MalcolmNance: Sen Cotton spy movies joke is cover for Seasions to deny personal collusion. Ignores entire RU effort while insulting Com… Negative RT @HarvardBiz: In the Trump-Comey case, there’s a lot of disagreement about whether there is a metamessage concealed in “hope” https://t.c… Positive RT @ricklevy67: Law professor: “If the allegations are true, #Trump has committed a serious federal crime” https://t.co/YEioOsOwzu #uspoli… Negative RT @HawaiiDelilah: Comey: There is “no fuzz” that Russians interfered with 2016 election https://t.co/SzAKQ5JElD Negative RT @Newsweek: Trump’s tweets about Comey leaks left former FBI director laughing https://t.co/XZGm5FMQfb https://t.co/pqRsMZ4Nqf Positive RT @RepAdamSchiff: Sessions confirmed Comey was uncomfortable meeting President alone. He had to know it involved Russia: why else would Co… Negative RT @Allen_Clifton: Body language tells you a lot. Sessions is defensive, agitated, and nervous. Comey was calm, cool, and collected. #Sessi…