Bug fix

contrast_feature.py

@@ -49,10 +49,9 @@ def extract(corpus_instance):
 			sent_verb = analyser.polarity_scores(verb)['compound']
 			sent_situation = analyser.polarity_scores(situation)['compound']
 
-			if (sent_verb > 0.0 and sent_situation < 0.0) or (sent_verb < 0.0 and sent_situation > 0.0):
-				print("phrase: {} {} sent verb: {}  sent situation: {}".format(verb, situation, sent_verb, sent_situation))
-
-
+			#if (sent_verb > 0.0 and sent_situation < 0.0) or (sent_verb < 0.0 and sent_situation > 0.0):
+			print("phrase: {} {} sent verb: {}  sent situation: {}".format(verb, situation, sent_verb, sent_situation))
+				
 
 if __name__ == '__main__':
 	corpus = corpus.read_corpus("corpus_shuffled.csv")[:1000]

ngram_feature.py

@@ -15,7 +15,7 @@ def extract(corpus_instance, corpus_dict_key, vocabulary):
 	return vector.toarray()[0]
 
 
-def get_vocabulary(corpus, corpus_dict_key, n):
+def get_vocabulary(corpus, corpus_dict_key, n_range):
 	"""
 	Creates vocabulary based on given corpus.
 	"""
@@ -23,7 +23,7 @@ def get_vocabulary(corpus, corpus_dict_key, n):
 	for line in corpus:
 		all_reviews.append(line[corpus_dict_key])
 
-	vectorizer = CountVectorizer(ngram_range=(n, n))
+	vectorizer = CountVectorizer(ngram_range=n_range)
 	vectorizer.fit(all_reviews)
 
 	return vectorizer.vocabulary_