input: def classify_text(text: str) -> str:
      """
      Classify a given text as either 'positive' or 'negative' using a Naive Bayes classifier.
  
      >>> classify_text("This is a great movie!")
      'positive'
      >>> classify_text("This movie was terrible")
      'negative'
      """
      from nltk.classify import NaiveBayesClassifier
  
      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)))
output:     from nltk.classify import NaiveBayesClassifier
      import nltk
  
      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)))