Bulk feature extraction

tw-10.py

#Read the tweets one by one and process it
inpTweets = csv.reader(open('data/sampleTweets.csv', 'rb'), delimiter=',', quotechar='|')
stopWords = getStopWordList('data/feature_list/stopwords.txt')
featureList = []

# Get tweet words
tweets = []
for row in inpTweets:
    sentiment = row[0]
    tweet = row[1]
    processedTweet = processTweet(tweet)
    featureVector = getFeatureVector(processedTweet, stopWords)
    featureList.extend(featureVector)
    tweets.append((featureVector, sentiment));
#end loop

# Remove featureList duplicates
featureList = list(set(featureList))

# Extract feature vector for all tweets in one shote
training_set = nltk.classify.util.apply_features(extract_features, tweets)