How to do it...

1. Create a new file and import the chosen packages:

import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews

1. Describe a function to extract features:

def collect_features(word_list):
  word = []
  return dict ([(word, True) for word in word_list])

1. Adopt movie reviews in NLTK as training data:

if __name__=='__main__':
  plus_filenum = movie_reviews.fileids('pos')
  minus_filenum = movie_reviews.fileids('neg')

1. Divide the data into positive and negative reviews:

  feature_pluspts = [(collect_features(movie_reviews.words(fileids=[f])),
'Positive') for f in plus_filenum]
  feature_minuspts = [(collect_features(movie_reviews.words(fileids=[f])),
'Negative') for f in minus_filenum]

1. Segregate the data into training and testing datasets:

  threshold_fact = 0.8
  threshold_pluspts = int(threshold_fact * len(feature_pluspts))
  threshold_minuspts = int(threshold_fact * len(feature_minuspts))

1. Extract the features:

  feature_training = feature_pluspts[:threshold_pluspts] + feature_minuspts[:threshold_minuspts]
  feature_testing = feature_pluspts[threshold_pluspts:] + feature_minuspts[threshold_minuspts:]
  print "nNumber of training datapoints:", len(feature_training)
  print "Number of test datapoints:", len(feature_testing)

1. Consider the Naive Bayes classifier and train it with an assigned objective:

  # Train a Naive Bayes classifiers
  classifiers = NaiveBayesClassifier.train(feature_training)
  print "nAccuracy of the classifiers:",nltk.classify.util.accuracy(classifiers,feature_testing)
  print "nTop 10 most informative words:"
  for item in classifiers.most_informative_features()[:10]:print item[0]
  # Sample input reviews
  in_reviews = [
  "The Movie was amazing",
  "the movie was dull. I would never recommend it to anyone.",
  "The cinematography is pretty great in the movie",
  "The direction was horrible and the story was all over the place"
  ]
  print "nPredictions:"
  for review in in_reviews:
    print "nReview:", review
  probdist = classifiers.prob_classify(collect_features(review.split()))
  predict_sentiment = probdist.max()
  print "Predicted sentiment:", predict_sentiment
  print "Probability:", round(probdist.prob(predict_sentiment), 2)

1. The result obtained for sentiment analysis is shown as follows: