def loadScoreTs(stock, wordCounterTsFilename=TS_FILENAME): if wordCounterTsFilename not in wordCounters: wordCounters[wordCounterTsFilename] = Preprocess.loadTs(wordCounterTsFilename) wordCounterTs = wordCounters[wordCounterTsFilename] topicWordCountTs = wordCounterTs.getTopicTs(stock) return sent.getScoreTimeseries(topicWordCountTs)
def getDayScore(self, wordCount):
positiveScore = 0
negativeScore = 0
for word, count in wordCount.iteritems():
# print word.encode(ignore), count
wordDict = self.wl.getWordDict(word)
if wordDict != None:
if wordDict['polarity'] == POLARITY['positive']:
positiveScore += count
elif wordDict['polarity'] == POLARITY['negative']:
negativeScore += count
if negativeScore == 0:
return float(positiveScore)
return float(positiveScore) / negativeScore
# Return the score timeseries as a list
def getScoreTimeseries(self, wordCounterTs):
return wordCounterTs.mapValues(self.getDayScore)
if __name__ == '__main__':
ts = Preprocess.loadTs()
sent = SentimentAnalysis()
t = time.clock()
a = sent.getScoreTimeseries(TimeSeries(ts.getTopicTs('msft')))
print time.clock() - t
