def train(spam_words, unlabeled_words):
spams = list(map(features, spam_words))
unlabeled = list(map(features, unlabeled_words))
model = PositiveNaiveBayesClassifier.train(spams, unlabeled, 0.5)
data = PickleData('bayesmodel.pickle')
data.write(model)
return model
def train_sentiments_classifier():
pairs = PairData('sentiments.txt', 'utf8')
model = NaiveBayesClassifier.train(pairs)
data = PickleData('sentiments.pickle')
data.write(model)
return model
