import nltk

 from nltk.corpus import movie_reviews as mr   

 def document_features(document,words_features):

     document_words=set(document)

     features={}

     for word in words_features:

         features['has(%s)' %word] = (word in document_words)

     return features   

 def test_doc_classification():

     documents=[(list(mr.words(fileid)),category)

                 for category in mr.categories()

                 for fileid in mr.fileids(categories=category)]

     all_words_dist=nltk.FreqDist(w.lower() for w in mr.words())

     words_freq =sorted(all_words_dist.items(), key=lambda x: (-1*x[1], x[0]))[:2000]

     words_features=[word[0] for word in words_freq]

     featuresets=[(document_features(doc,words_features),c) for (doc,c) in

                     documents]

     train_set, test_set= featuresets[100:],featuresets[:100]

     classifier=nltk.NaiveBayesClassifier.train(train_set)

     print nltk.classify.accuracy(classifier,test_set)

     classifier.show_most_informative_features(5)