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ngrams.py
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import nltk
import util
import codecs
import operator
import progressbar
from nltk.collocations import *
def get_unigram(twitter):
preprocessed = twitter.preprocessed
count = preprocessed.count()
tweets = preprocessed.find({}, {'text':1, '_id':0})
tokenizer = util.Tokenizer()
terms = []
print "generate unigram"
bar = progressbar.ProgressBar(maxval = count, widgets = [progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]).start()
i = 0
for tweet in tweets:
terms.extend(tokenizer.tokenize(tweet['text']))
bar.update(i + 1)
i = i + 1
bar.finish()
freqdist = nltk.FreqDist(terms)
sorted_freqdist = sorted(freqdist.items(), key=operator.itemgetter(1))
print "calculate frequency"
bar = progressbar.ProgressBar(maxval = len(freqdist), widgets = [progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]).start()
i = 0
for item in sorted_freqdist:
twitter.unigram.insert({'collocation':item[0], 'frequency':item[1]})
bar.update(i + 1)
i = i + 1
bar.finish()
def get_bigram(twitter):
# bigram_measures = BigramAssocMeasures()
preprocessed = twitter.preprocessed
count = preprocessed.count()
tweets = preprocessed.find({}, {'text':1, '_id':0})
tokenizer = util.Tokenizer()
bigrams = []
print "generate bigrams"
bar = progressbar.ProgressBar(maxval = count, widgets = [progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]).start()
i = 0
for tweet in tweets:
bigram = [collocation for collocation in nltk.bigrams(tokenizer.tokenize(tweet['text']))]
bigrams.extend(bigram)
bar.update(i + 1)
i = i + 1
bar.finish()
freqdist = nltk.FreqDist(bigrams)
sorted_freqdist = sorted(freqdist.items(), key=operator.itemgetter(1))
print "calculate frequency"
bar = progressbar.ProgressBar(maxval = len(freqdist), widgets = [progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]).start()
i = 0
file = codecs.open('bigram', 'w', 'utf-8')
for item in sorted_freqdist:
twitter.bigram.insert({'collocation':item[0], 'frequency':item[1]})
file.write('%s\t%d\n' % (item[0], item[1]))
bar.update(i + 1)
i = i + 1
bar.finish()
def get_trigram(twitter):
# trigram_measures = TrigramAssocMeasures()
preprocessed = twitter.preprocessed
count = preprocessed.count()
tweets = preprocessed.find({}, {'text':1, '_id':0})
tokenizer = util.Tokenizer()
trigrams = []
print "generate trigrams"
bar = progressbar.ProgressBar(maxval = count, widgets = [progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]).start()
i = 0
for tweet in tweets:
trigram = [collocation for collocation in nltk.trigrams(tokenizer.tokenize(tweet['text']))]
trigrams.extend(trigram)
bar.update(i + 1)
i = i + 1
bar.finish()
freqdist = nltk.FreqDist(trigrams)
sorted_freqdist = sorted(freqdist.items(), key=operator.itemgetter(1))
print "calculate frequency"
bar = progressbar.ProgressBar(maxval = len(freqdist), widgets = [progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]).start()
i = 0
file = codecs.open('trigram', 'w', 'utf-8')
for item in sorted_freqdist:
twitter.trigram.insert({'collocation':item[0], 'frequency':item[1]})
file.write('%s\t%d\n' % (item[0], item[1]))
bar.update(i + 1)
i = i + 1
bar.finish()