def run(self):
rev = ReviewParser(open(settings.reviews_path + ReviewParser.map_cid_to_name(self.cid), 'rb',), review_files[choice].split('.')[-1])
rev.parse()
print "Mining", len(rev.reviews), "reviews"
text = rev.get_raw_text()
f = FeatureExtractor(text, ReviewParser.map_cid_to_name(self.cid))
self.features = f.get_frequent_features(self.min_support)
for ftr in self.features:
self.ratings[ftr[0]] = {'positive': 0, 'negative': 0, 'neutral': 0}
o = OpinionSentenceFinder(self.features, f.feature_sentences)
#Extract all sentences which express some opinion
opinion_sents = map(lambda y: y['opinion_sent'], filter(lambda x: len(x['opinion_sent']) > 1, o.feature_sentences))
temp = []
for os in opinion_sents:
temp.extend(os)
opinion_sents = temp
for ftr, sentiment in opinion_sents:
if sentiment[0] is True:
self.ratings[ftr]['positive'] += 1
elif sentiment[0] is False:
self.ratings[ftr]['negative'] += 1
else:
self.ratings[ftr]['neutral'] += 1
pp = pprint.PrettyPrinter(indent = 4)
print "Is this a %s?" % f.product_category
print "%d features are interesting" % len(self.features)
#pp.pprint(opinion_sents)
pp.pprint(self.ratings)
