input: Write a function to classify text documents into two classes: positive and negative using a Naive Bayes classifier. The function should take a string as input and return the classification ('positive' or 'negative').
  
  import nltk
  from nltk.classify import NaiveBayesClassifier
  
  def classify_text(text):
      def label_words(words): 
          return dict([(word, True) for word in words])
  
      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)
  
      return classifier.classify(label_words(nltk.word_tokenize(text)))
  
  assert classify_text("This movie was ok") == 'positive'
  assert classify_text("This movie was terrible") == 'negative'
  assert classify_text("This is a great movie!") == 'positive'
output: def classify_text(text):
      def label_words(words): 
          return dict([(word, True) for word in words])
  
      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)
  
      return classifier.classify(label_words(nltk.word_tokenize(text)))