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ExtractFeatures.py
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ExtractFeatures.py
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import csv
import tokenize
import nltk
__author__ = 'Ritvika'
from PreProcessTweets import processTweet
from FeatureWords import *
tweets = []
featureList = []
#Read the tweets one by one and process it
def extractBulkFeatures():
inpTweets = csv.reader(open('TrainingDATA/flipkartTrainingData.csv', 'rb'), delimiter=',')
global tweets
global featureList
t = open('fkTweetsTS.txt','w')
pt = open('fkProcessedTweets.txt','w')
fv = open('fkFeatureVector.txt','w')
fl = open('fkFeatureList.txt','w')
for row in inpTweets:
sentiment = row[0]
tweet = row[1]
processedTweet = processTweet(tweet)
pt.write(" , ".join(processedTweet)+"\n")
#pickle.dump(processedTweet,pt)
featureVector = getFeatureVector(processedTweet)
fv.write(" , ".join(featureVector)+"\n")
#pickle.dump(featureVector,fv)
#featureList.append(word for word in getFeatureVector(tweet))
featureList.extend(featureVector)
tweets.append((featureVector, sentiment))
#end loop
#print tweets
pt.close()
fv.close()
#t.write(tweets)
featureList = list(set(featureList))
#fl.write(" , ".join(featureList)+"\n")
#print "Feature List :",featureList
#pickle.dump(featureList,fl)
fl.close()
#printFeatureVector()
#print "printin featureList \n \n"
#print featureList
def extractBulkBigramFeatures():
inpTweets = csv.reader(open('TrainingDATA/flipkartTrainingData.csv', 'rb'), delimiter=',')
global tweets
global featureList
t = open('fkBigramsTweetsTS.txt','w')
featureList = []
pt = open('fkBigramsProcessedTweets.txt','w')
fv = open('fkBigramsFeatureVector.txt','w')
fl = open('fkBigramsFeatureList.txt','w')
for row in inpTweets:
sentiment = row[0]
tweet = row[1]
processedTweet = processTweet(tweet)
pt.write(" , ".join(processedTweet)+"\n")
featureVector = getBigramFeatureVector(processedTweet)
#print featureVector
for lis in featureVector:
fv.write("( "+lis[0]+" , "+lis[1]+" ) , ")
fv.write("\n")
featureList.extend(featureVector)
tweets.append((featureVector, sentiment))
#print tweets
#pt.close()
#fv.close()
#t.write(tweets)
featureList = list(set(featureList))
#print "BgFeature List :",featureList
#for lis in featureList:
#fl.write("( "+lis[0]+" , "+lis[1]+" ) , ")
#pickle.dump(featureList,fl)
#fl.close()
#printFeatureVector()
#print "printin featureList \n \n"
#print featureList
def extract_bigram_features(tweet):
#print "extracting features of given tweet \n"
features = {}
for lis in featureList:
features['contains(%s ,%s)' % (lis[0],lis[1])] = (lis in tweet)
#print "features = ",features
return features
def extract_features(tweet):
#print "extracting features of given tweet \n"
tweet_words = (re.split(r'[\s\'",.!?]\s*',' '.join(tweet)))
#print "tweet-words : \n", tweet_words
features = {}
for word in featureList:
features['contains(%s)' % word] = (word in tweet_words)
#print "features = ",features
return features
#end
#s_tweet = 'Yeah! So excited ! Just updated my @Flipkart for #TheBigBillionDays. https://t.co/cApJf5DCnC'
#print extract_features(list(s_tweet.lower()))
#extractBulkFeatures()
#print extract_features(getFeatureVector(processTweet('Yeah! So excited ! Just updated my @Flipkart for #TheBigBillionDays. https://t.co/cApJf5DCnC')))
#extractBulkBigramFeatures()
#print extract_bigram_features(getBigramFeatureVector(processTweet('Yeah! So excited ! Just updated my @Flipkart for #TheBigBillionDays. https://t.co/cApJf5DCnC')))