input: 
  from typing import List, Tuple
  
  def classify_text(positiveText: str, negativeText: str, newText: str) -> str:
      """
      Classify a new text document into two classes: positive or negative.
      The classification is based on two pre-defined text documents representing positive and negative sentiment.
      
      >>> classify_text("This is a great movie!", "This movie was terrible", "This movie was ok")
      'positive'
      >>> classify_text("I love this!", "I hate this", "This is just average")
      'negative'
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier
  
      nltk.download('punkt')
  
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      def tokenize_and_label(text: str) -> Tuple[dict, str]:
          tokens = nltk.word_tokenize(text)
          return (label_words(tokens), text.split()[0].lower())
      
      train_data = [tokenize_and_label(positiveText), tokenize_and_label(negativeText)]
      classifier = NaiveBayesClassifier.train(train_data)
      
      return classifier.classify(label_words(nltk.word_tokenize(newText)))
  
output:     result = classify_text("This is a great movie!", "This movie was terrible", "This movie was ok")
      assert result == 'positive', f"Expected 'positive', but got {result}"
  
      result = classify_text("I love this!", "I hate this", "This is just average")
      assert result == 'negative', f"Expected 'negative', but got {result}"