def main_program():
option = '0'
tweet_samples, filtered_samples, classifier = init()
while option != '7':
menu()
option = raw_input('Enter new option: ')
if option == '1':
print_data(tweet_samples)
elif option == '2':
print_data(filtered_samples)
elif option == '3':
print classifier.show_most_informative_features(n=30)
elif option == '4':
sentence = raw_input("Sentence: ")
cleaned_sentence = [
word.lower() for word in sentence.split() if len(word) >= 3
]
print 'This sentence is ' + classifier.classify(
feature_extractor(cleaned_sentence))
elif option == '5':
negative, positive = find_overall_sentiment(
filtered_samples, classifier)
print_sentiment(positive, negative)
elif option == '6':
plot_bar(filtered_samples, classifier)
clear()
def find_overall_sentiment(samples, classifier):
positive = 0
negative = 0
for sample in samples:
if classifier.classify(feature_extractor(sample)) == 'positive':
positive += 1
else:
negative += 1
return positive, negative
def find_overall_sentiment(samples, classifier):
positive = 0
negative = 0
for sample in samples:
if classifier.classify(feature_extractor(sample)) == 'positive':
positive += 1
else:
negative += 1
return positive, negative
def main_program():
option = '0'
tweet_samples, filtered_samples, classifier = init()
while option != '7':
menu()
option = raw_input('Enter new option: ')
if option == '1':
print_data(tweet_samples)
elif option == '2':
print_data(filtered_samples)
elif option == '3':
print classifier.show_most_informative_features(n=30)
elif option == '4':
sentence = raw_input("Sentence: ")
cleaned_sentence = [word.lower() for word in sentence.split() if len(word) >=3]
print 'This sentence is ' + classifier.classify(feature_extractor(cleaned_sentence))
elif option == '5':
negative, positive = find_overall_sentiment(filtered_samples, classifier)
print_sentiment(positive, negative)
elif option == '6':
plot_bar(filtered_samples, classifier)
clear()
