input: def classify_text_documents(positiveText: str, negativeText: str) -> NaiveBayesClassifier:
      """
      Create a Python program to classify text documents into two classes: positive and negative.
      
      Example:
      >>> classify_text_documents("This is a great movie!", "This movie was terrible")
      <nltk.classify.naivebayes.NaiveBayesClassifier at 0x...>
      """
output:     from nltk.classify import NaiveBayesClassifier
      from nltk.tokenize import word_tokenize
  
      def word_feats(words):
          return dict([(word, True) for word in words])
  
      positive_features = word_feats(word_tokenize(positiveText))
      negative_features = word_feats(word_tokenize(negativeText))
  
      train_set = [(positive_features, 'positive'), (negative_features, 'negative')]
      classifier = NaiveBayesClassifier.train(train_set)
  
      return classifier