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executable file 42 lines (30 sloc) 1.402 kB
import numpy as np
import twitter
def readSentimentList(file_name):
ifile = open(file_name, 'r')
happy_log_probs = {}
sad_log_probs = {}
ifile.readline()
for line in ifile:
tokens = line[:-1].split(',')
happy_log_probs[tokens[0]] = float(tokens[1])
sad_log_probs[tokens[0]] = float(tokens[2])
return happy_log_probs, sad_log_probs
def classifySentiment(words, happy_log_probs, sad_log_probs):
happy_probs = [happy_log_probs[word] for word in words if word in happy_log_probs]
sad_probs = [sad_log_probs[word] for word in words if word in sad_log_probs]
tweet_happy_log_prob = np.sum(happy_probs)
tweet_sad_log_prob = np.sum(sad_probs)
prob_happy = np.reciprocal(np.exp(tweet_sad_log_prob - tweet_happy_log_prob) + 1)
prob_sad = 1 - prob_happy
return prob_happy, prob_sad
def main():
api = twitter.Api()
tweets = api.GetSearch(term="#sport", per_page=100, page=1)
happy_log_probs, sad_log_probs = readSentimentList('twitter_sentiment_list.csv')
for s in tweets:
tweet1 = s.text.split(" ")
tweet1_happy_prob, tweet1_sad_prob = classifySentiment(tweet1, happy_log_probs, sad_log_probs)
print "The probability that tweet1 (", tweet1, ") is happy is ", tweet1_happy_prob, "and the probability that it is sad is ", tweet1_sad_prob
if __name__ == '__main__':
main()
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