import numpy as np

from nltk.probability import FreqDist

from nltk.classify import SklearnClassifier

from sklearn.feature_extraction.text import TfidfTransformer

from sklearn.feature_selection import SelectKBest, chi2

from sklearn.naive_bayes import MultinomialNB

from sklearn.pipeline import Pipeline

pipeline = Pipeline([('tfidf', TfidfTransformer()), ('chi2', SelectKBest(chi2, k=1000)), ('nb', MultinomialNB())])

classif = SklearnClassifier(pipeline)

from nltk.corpus import movie_reviews

pos = [FreqDist(movie_reviews.words(i))

for i in movie_reviews.fileids('pos')]

neg = [FreqDist(movie_reviews.words(i))

for i in movie_reviews.fileids('neg')]

add_label = lambda lst, lab: [(x, lab) for x in lst] classif.train(add_label(pos[:100], 'pos') + add_label(neg[:100], 'neg'))

l_pos = np.array(classif.classify_many(pos[100:]))

l_neg = np.array(classif.classify_many(neg[100:]))

print "Confusion matrix:\n%d\t%d\n%d\t%d" nada ( (l_pos == 'pos').sum(), (l_pos == 'neg').sum(), (l_neg == 'pos').sum(), (l_neg == 'neg').sum())