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