def run(self):
#Calling the ReviewsExtractor class to read the csv file
#and extract,concatenate the reviews
rev = ReviewsExtractor()
rev.extract_review_content()
total_content = rev.get_concatenated_reviews()
f = FeatureExtractor(total_content)
self.features = f.get_frequent_features_list(self.min_support)
o = OpinionSentenceCollector(self.features, f.feature_sentences)
for feature in o.opinion_features:
self.ratings[feature] = {'positive': 0, 'negative': 0, 'neutral': 0, 'total_reviews': 0, 'negative_review': '', 'positive_review': ''}
for feature, sentiment_score, sentence in o.opinion_sentences:
self.ratings[feature]['total_reviews'] += 1
if sentiment_score > 0:
self.ratings[feature]['positive'] += 1
self.ratings[feature]['positive_review'] = sentence
elif sentiment_score < 0:
self.ratings[feature]['negative'] += 1
self.ratings[feature]['negative_review'] = sentence
else:
self.ratings[feature]['neutral'] += 1
for feature in o.opinion_features:
self.final_features.append((feature,self.ratings[feature]['total_reviews']))
self.sorted_features = sorted(set(self.final_features), key=lambda x: x[1], reverse=True)
pp = pprint.PrettyPrinter(indent=4)
print self.sorted_features
print len(self.sorted_features)
for index in range(0, 10):
iter_feature = self.sorted_features[index][0]
print "Feature: ", iter_feature
pp.pprint(self.ratings[iter_feature])
