def getSentimentProb_FromEV(wordList, textList):
sentimentVal = dict()
sentimentProb = dict()
sentiWordIndex = dict()
for words in wordList:
sentimentProb[words] = [0.0, 0.0]
sentimentVal[words] = [0.0, 0.0]
sentiWordIndex[words] = []
keyword = wordList
[sentimentVal, countAll] = Ev.EvalSentiment(textList, keyword)
for i in range(len(sentimentVal)):
sentimentProb[wordList[i]] = [
sentimentVal[wordList[i]][0] / float(100.0),
sentimentVal[wordList[i]][1] / float(100.0)
]
print sentimentProb
return sentimentProb
sentimentProbGen = {}
for word in sentimentHistogram.keys():
if ((sentimentHistogram[word][0] == 0.0) &
(sentimentHistogram[word][1] == 0.0)):
sentimentProbGen[word] = [0.5, 0.5]
else:
sumVal = sum(sentimentHistogram[word])
sentimentProbGen[word] = [
(sentimentHistogram[word][0] / float(sumVal)),
(sentimentHistogram[word][1] / float(sumVal))
]
#Sentiment based Evaluation:
#keyword=['bjp', 'congress', 'modi', 'rahul', 'gandhi', 'aap'];
keyword = pModel.keys()
[SentimentScoreALLText, countAll] = Ev.EvalSentiment(textList, keyword)
[SentimentScoreMRS, countMRS] = Ev.EvalSentiment(summTweetMRS, keyword)
[SentimentScoreRS, countRS] = Ev.EvalSentiment(textListsummRS, keyword)
[SentimentScoreLR, countLR] = Ev.EvalSentiment(summLexRank, keyword)
#print "LDA starts"
#final.ldafinal(textList)
#w = printtopics.printtopics1('Actual')
#final.ldafinal(summTweetMRS)
#x = printtopics.printtopics1('MRS')
#final.ldafinal(textListsummRS)
#y = printtopics.printtopics1('RS')
#final.ldafinal(summLexRank)
#z = printtopics.printtopics1('LR')
#print "LDA done finally"
#ft = open('topics.csv','w')
