#helper.filtered_tweet buat data latih (jadi dipisah tweet, sama targetnya yang dari tweet.dat)
training_set = nltk.classify.apply_features(helper.extract_features, helper.filtered_tweet)
#ini buat mulai ngetrain (ngehasilin model)
classifier = nltk.NaiveBayesClassifier.train(training_set)
# classifier = nltk.MaxentClassifier.train(training_set)
# helper.save_model('maxent.mdl', classifier)
#ini mah cuma ngeprint yang didalem file question.txt
print helper.question_text
# classifier = helper.load_model('naivebayes.mdl')
# classifier2 = helper.load_model('sentiment-maxent.mdl')
#mecah kalimat jadi kata2
print helper.replace_two_or_more_liat(tweet.split())
#corpus = data uji
corpus_tag = helper.get_tag_from_corpus("tweets.dat")
corpus_text = helper.get_text_from_corpus("tweets.dat")
test_tag = []
#untuk nguji
corpus_text = ["Aku ga suka sama kamu"]
for text in corpus_text:
#untuk klasifikasi si "text" di corpus
result = classifier.classify(helper.extract_features(text.split()))
print result
# if result == '1':
# result = classifier2.classify(helper.extract_features(text.split()))
test_tag.append(result)
corpus_tag.reverse()
