naive_bayes.py

import nltk
import random
from nltk.corpus import movie_reviews


documents = [(list( movie_reviews.words(fileid), category)
            for category in movie_reviews.categories()
            for fileid in movie_reviews.fileids(category)]

random.shuffle(documents)

all_words = []

for w in movie_reviews.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3000]

def find_features(document):
    words = set(document)
    features = {}
    for w in word_features:
        features[w] = (w in words)

    return features

print ((find_features(movie_reviews.words('neg/cv000_29416.txt'))))

featuresets = [(find_features(rev) , category) for (rev , category) in documents]

training = featuresets[:1900]
testing = featuresets[1900:]

classifier = nltk.NaiveBayesClassifier.train(training)

print ("Naive Bayes Algo Accuracy: ", (nltk.classify.accuracy(classifier, testing))*100)

classifier.show_most_informative_features(20)