input: 
  from nltk.classify import NaiveBayesClassifier
  import nltk
  
  def text_classification_example():
      """
      Demonstrates how to classify text into 'positive' or 'negative' using a simple NaiveBayesClassifier from nltk.
      
      >>> text_classification_example()
      'positive'
      """
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
  
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_tokens = nltk.word_tokenize(positiveText) 
      negative_tokens = nltk.word_tokenize(negativeText)
  
      positive_data = [(label_words(positive_tokens), 'positive')] 
      negative_data = [(label_words(negative_tokens), 'negative')]
   
      train_data = positive_data + negative_data 
      classifier = NaiveBayesClassifier.train(train_data) 
    
      return classifier.classify(label_words(nltk.word_tokenize("This movie was ok")))
  
output:     test_text = "This movie was ok"
      test_tokens = nltk.word_tokenize(test_text)
      return classifier.classify(label_words(test_tokens))