input: def classify_text_sentiment(positiveText: str, negativeText: str, newText: str) -> str:
      """ Classify a new text document into two classes: positive or negative based on the given example texts.
      >>> classify_text_sentiment("This is a great movie!", "This movie was terrible", "This movie was ok")
      'negative'
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier
      
      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) 
    
      # classify new text
      return classifier.classify(label_words(nltk.word_tokenize(newText)))
output:     new_tokens = nltk.word_tokenize(newText)
      return classifier.classify(label_words(new_tokens))