input: Write a function to classify text documents into two classes: positive and negative using the Naive Bayes classifier from the nltk library. The function should return 'positive' if the given text is classified as positive and 'negative' if the given text is classified as negative. If the text is an empty string, it should return 'neutral'.
  
  
  import nltk
  from nltk.classify import NaiveBayesClassifier
  
  def label_words(words):
      return dict([(word, True) for word in words])
  
  def classify_text(text):
      if text == "":
          return 'neutral'
      
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      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)
      
      text_tokens = nltk.word_tokenize(text)
      classification = classifier.classify(label_words(text_tokens))
      return classification
  
  assert classify_text("This movie was ok") == 'positive'
  assert classify_text("This movie was terrible") == 'negative'
  assert classify_text("") == 'neutral'
  assert classify_text("This is a great movie!") == 'positive'
  assert classify_text("I did not like this movie at all.") == 'negative'
  
output: def classify_text(text):
      if text == "":
          return 'neutral'
      
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      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)
      
      text_tokens = nltk.word_tokenize(text)
      classification = classifier.classify(label_words(text_tokens))
      return classification