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 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()