def calculate_sentiment_1(review_string):
#return str(os.getcwd())
os.chdir('/home/ubuntu/Src')
bow_model = joblib.load('bow_model.pkl')
rf_model = joblib.load('rf_bow_model.pkl')
single_sample_review_list = []
mreview_obj = MovieReview(review_string)
mreview_obj.clean_review()
mreview_obj.remove_punctuation_and_nums()
mreview_obj.split_review_into_words()
mreview_obj.remove_stop_words()
single_sample_review_list.append(mreview_obj.mreview_clean)
query_features = bow_model.transform(single_sample_review_list)
query_features = query_features.toarray()
return str(os.getcwd())
def calculate_sentiment(review_string):
os.chdir('/home/ubuntu/Src')
bow_model = joblib.load('bow_model.pkl')
rf_model = joblib.load('rf_bow_model.pkl')
single_sample_review_list = []
mreview_obj = MovieReview(review_string)
mreview_obj.clean_review()
mreview_obj.remove_punctuation_and_nums()
mreview_obj.split_review_into_words()
mreview_obj.remove_stop_words()
single_sample_review_list.append(mreview_obj.mreview_clean)
query_features = bow_model.transform(single_sample_review_list)
query_features = query_features.toarray()
# use the trained forest to make predictions
prediction = rf_model.predict(query_features)
#print "Result: " , prediction[0]
return prediction[0]