def main_function():
    conn = MySQLdb.connect(host=DATABASES['date_cutoff']['HOST'],
                           user=DATABASES['date_cutoff']['USER'],
                           passwd=DATABASES['date_cutoff']['PASSWORD'],
                           db=DATABASES['date_cutoff']['NAME'])

    total_word_count = total_words(conn)
    training_feature_set = process_bigrams(conn, '+', total_word_count,
                                           best_words)
    training_feature_set += process_bigrams(conn, '-', total_word_count,
                                            best_words)
    training_feature_set += process_bigrams(conn, 'I', total_word_count,
                                            best_words)
    training_feature_set += process_bigrams(conn, 'O', total_word_count,
                                            best_words)

    config_megam('/opt/packages')
    #classifier = MaxentClassifier.train(training_feature_set, algorithm="megam", trace=0)
    classifier = NaiveBayesClassifier.train(training_feature_set)
    classifier.show_most_informative_features(10)

    test_tweets = classify.get_test_tweets(conn)
    test_feature_set = process_tweets(test_tweets)

    classifier_accuracy = accuracy(classifier, test_feature_set)

    print "classifier accuracy: " + repr(classifier_accuracy)
Beispiel #2
0
def main_function():
	conn = MySQLdb.connect(host=DATABASES['date_cutoff']['HOST'], 
			user=DATABASES['date_cutoff']['USER'], 
			passwd=DATABASES['date_cutoff']['PASSWORD'], 
			db=DATABASES['date_cutoff']['NAME'])

	training_tweets = classify.get_training_tweets(conn_analysis)
	training_feature_set = process_tweets(training_tweets)

	config_megam('/opt/packages')
	classifier = MaxentClassifier.train(training_feature_set, algorithm="megam", trace=0)

	error_dict = {'+':0, '-':0, 'I':0, 'O':0} 
	count_dict = {'+':0, '-':0, 'I':0, 'O':0} 
	guess_dict = {'+':0, '-':0, 'I':0, 'O':0} 

	full_matrix = {'+':{'+':0, '-':0, 'I':0, 'O':0}, 
				'-':{'+':0, '-':0, 'I':0, 'O':0}, 
				'I':{'+':0, '-':0, 'I':0, 'O':0}, 
				'O':{'+':0, '-':0, 'I':0, 'O':0}}


	test_tweets = classify.get_test_tweets(conn_analysis)
	test_feature_set = process_tweets(test_tweets)

	classifier.show_most_informative_features(10)
	classifier_accuracy = accuracy(classifier, test_feature_set)
	print "classifier accuracy: " + repr(classifier_accuracy)
def main_function():
    conn = MySQLdb.connect(host=DATABASES['default']['HOST'],
                           user=DATABASES['default']['USER'],
                           passwd=DATABASES['default']['PASSWORD'],
                           db=DATABASES['default']['NAME'])

    training_tweets = classify.get_training_tweets(conn)
    training_feature_set = classify.process_tweets(training_tweets)

    config_megam('/opt/packages')
    #classifier = MaxentClassifier.train(training_feature_set, algorithm="megam", trace=0)
    classifier = NaiveBayesClassifier.train(training_feature_set)
    #classifier.show_most_informative_features(50, show='pos')
    #classifier.show_most_informative_features(50, show='neg')

    #classifier.explain(training_feature_set[0][0])
    #print training_feature_set[0]

    error_dict = {'+': 0, '-': 0, 'I': 0, 'O': 0}
    count_dict = {'+': 0, '-': 0, 'I': 0, 'O': 0}
    guess_dict = {'+': 0, '-': 0, 'I': 0, 'O': 0}

    full_matrix = {
        '+': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        },
        '-': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        },
        'I': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        },
        'O': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        }
    }

    count_table = {'+': 0, '-': 0, 'I': 0, 'O': 0}

    test_tweets = classify.get_test_tweets(conn)
    test_feature_set = classify.process_tweets(test_tweets)

    classifier_accuracy = accuracy(classifier, test_feature_set)

    #print count_table
    print "classifier accuracy: " + repr(classifier_accuracy)
def main_function():
	conn = MySQLdb.connect(host=DATABASES['default']['HOST'], 
			user=DATABASES['default']['USER'], 
			passwd=DATABASES['default']['PASSWORD'], 
			db=DATABASES['default']['NAME'])

	training_tweets = classify.get_training_tweets(conn)
	training_feature_set = classify.process_tweets(training_tweets)

	bayes_classifier = NaiveBayesClassifier.train(training_feature_set)

	count_table = {'+':0, '-':0, 'I':0, 'O':0}  

	test_tweets = classify.get_test_tweets(conn)

	for tweet in test_tweets:
		text = classify.get_tweet_text(conn, tweet[0])[0][0]
		guess = bayes_classifier.classify(classify.process_tweet(text))
		classify.update_tweet_polarity(tweet[0], guess, conn)
		count_table[guess] += 1

	print "Naive Bayes"
	print count_table

	count_table = {'+':0, '-':0, 'I':0, 'O':0}  
	config_megam('/opt/packages')
	max_ent_classifier = MaxentClassifier.train(training_feature_set, algorithm="megam", trace=0)

	for tweet in test_tweets:
		text = classify.get_tweet_text(conn, tweet[0])[0][0]
		guess = max_ent_classifier.classify(classify.process_tweet(text))
		update_tweet_polarity_ensemble(tweet[0], guess, conn)
		count_table[guess] += 1

	print "Maximum Entropy"
	print count_table

	#generate the accuracy matrix
	full_matrix = {'+':{'+':0, '-':0, 'I':0, 'O':0}, 
				'-':{'+':0, '-':0, 'I':0, 'O':0}, 
				'I':{'+':0, '-':0, 'I':0, 'O':0}, 
				'O':{'+':0, '-':0, 'I':0, 'O':0}}

	for tweet in test_tweets:
		result = classify.run_sql(conn, classify.Statements.CHECK_CONSENSUS % tweet[0])
		guess = result[0][0]

		actual_result = classify.run_sql(conn, classify.Statements.CHECK_MAJORITY % tweet[0])
		actual = actual_result[0][0]

		if guess is not None:
			if actual is not None:
				full_matrix[actual][guess] += 1

	print full_matrix
def main_function():
	conn = MySQLdb.connect(host=DATABASES['date_cutoff']['HOST'], 
			user=DATABASES['date_cutoff']['USER'], 
			passwd=DATABASES['date_cutoff']['PASSWORD'], 
			db=DATABASES['date_cutoff']['NAME'])

	total_word_count = total_words(conn)
	training_feature_set = process_bigrams(conn, '+', total_word_count, best_words)
	training_feature_set += process_bigrams(conn, '-', total_word_count, best_words)
	training_feature_set += process_bigrams(conn, 'I', total_word_count, best_words)
	training_feature_set += process_bigrams(conn, 'O', total_word_count, best_words)

	config_megam('/opt/packages')
	#classifier = MaxentClassifier.train(training_feature_set, algorithm="megam", trace=0)
	classifier = NaiveBayesClassifier.train(training_feature_set)
	classifier.show_most_informative_features(10)

	test_tweets = classify.get_test_tweets(conn)
	test_feature_set = process_tweets(test_tweets)

	classifier_accuracy = accuracy(classifier, test_feature_set)

	print "classifier accuracy: " + repr(classifier_accuracy)
def main_function():
	conn = MySQLdb.connect(host=DATABASES['default']['HOST'], 
			user=DATABASES['default']['USER'], 
			passwd=DATABASES['default']['PASSWORD'], 
			db=DATABASES['default']['NAME'])

	training_tweets = classify.get_training_tweets(conn)
	training_feature_set = classify.process_tweets(training_tweets)

	config_megam('/opt/packages')
	#classifier = MaxentClassifier.train(training_feature_set, algorithm="megam", trace=0)
	classifier = NaiveBayesClassifier.train(training_feature_set)
	#classifier.show_most_informative_features(50, show='pos')
	#classifier.show_most_informative_features(50, show='neg')

	#classifier.explain(training_feature_set[0][0])
	#print training_feature_set[0]

	error_dict = {'+':0, '-':0, 'I':0, 'O':0} 
	count_dict = {'+':0, '-':0, 'I':0, 'O':0} 
	guess_dict = {'+':0, '-':0, 'I':0, 'O':0} 

	full_matrix = {'+':{'+':0, '-':0, 'I':0, 'O':0}, 
				'-':{'+':0, '-':0, 'I':0, 'O':0}, 
				'I':{'+':0, '-':0, 'I':0, 'O':0}, 
				'O':{'+':0, '-':0, 'I':0, 'O':0}}

	count_table = {'+':0, '-':0, 'I':0, 'O':0}  

	test_tweets = classify.get_test_tweets(conn)
	test_feature_set = classify.process_tweets(test_tweets)

	classifier_accuracy = accuracy(classifier, test_feature_set)

	#print count_table
	print "classifier accuracy: " + repr(classifier_accuracy)
def main_function():
    conn = MySQLdb.connect(host=DATABASES['ensemble']['HOST'],
                           user=DATABASES['ensemble']['USER'],
                           passwd=DATABASES['ensemble']['PASSWORD'],
                           db=DATABASES['ensemble']['NAME'])

    training_tweets = classify.get_training_tweets(conn)
    training_feature_set = classify.process_tweets(training_tweets)

    bayes_classifier = NaiveBayesClassifier.train(training_feature_set)

    count_table = {'+': 0, '-': 0, 'I': 0, 'O': 0}

    test_tweets = classify.get_test_tweets(conn)

    for tweet in test_tweets:
        text = classify.get_tweet_text(conn, tweet[0])[0][0]
        guess = bayes_classifier.classify(classify.process_tweet(text))
        classify.update_tweet_polarity(tweet[0], guess, conn)
        count_table[guess] += 1

    print "Naive Bayes"
    print count_table

    count_table = {'+': 0, '-': 0, 'I': 0, 'O': 0}
    config_megam('/opt/packages')
    max_ent_classifier = MaxentClassifier.train(training_feature_set,
                                                algorithm="megam",
                                                trace=0)

    for tweet in test_tweets:
        text = classify.get_tweet_text(conn, tweet[0])[0][0]
        guess = max_ent_classifier.classify(classify.process_tweet(text))
        update_tweet_polarity_ensemble(tweet[0], guess, conn)
        count_table[guess] += 1

    print "Maximum Entropy"
    print count_table

    #generate the accuracy matrix
    full_matrix = {
        '+': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        },
        '-': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        },
        'I': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        },
        'O': {
            '+': 0,
            '-': 0,
            'I': 0,
            'O': 0
        }
    }

    for tweet in test_tweets:
        result = classify.run_sql(
            conn, classify.Statements.CHECK_CONSENSUS % tweet[0])
        guess = result[0][0]

        actual_result = classify.run_sql(
            conn, classify.Statements.CHECK_MAJORITY % tweet[0])
        actual = actual_result[0][0]

        if guess is not None:
            if actual is not None:
                full_matrix[actual][guess] += 1

    print full_matrix