input: def classify_text(positiveText: str, negativeText: str) -> NaiveBayesClassifier:
      """
      Given two text documents, one labeled as positive and the other as negative, this function will train a Naive Bayes classifier to classify new text documents into two classes: positive and negative.
      
      >>> positiveText = "This is a great movie!"
      >>> negativeText = "This movie was terrible"
      >>> classifier = classify_text(positiveText, negativeText)
      >>> classifier.classify(label_words(nltk.word_tokenize("This movie was ok")))
      'positive'
      """
output:     from nltk.classify import NaiveBayesClassifier
      from nltk.tokenize import word_tokenize
  
      def label_words(words):
          return {word: True for word in words}
  
      positive_features = label_words(word_tokenize(positiveText))
      negative_features = label_words(word_tokenize(negativeText))
  
      train_set = [(positive_features, 'positive'), (negative_features, 'negative')]
      classifier = NaiveBayesClassifier.train(train_set)
  
      return classifier