input: 
  import nltk
  from nltk.classify import NaiveBayesClassifier 
    
  def text_classification(text_data: str) -> str:
      """A Python program to classify text documents into two classes: positive and negative.
      The text data should be processed to extract tokens and then labeled as either positive or negative.
      The NaiveBayesClassifier from nltk is used to train the model on given examples and then classify new text.
      Example:
      >>> 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) 
      >>> text_classification("This movie was ok")
      'positive'
      """
  
  def label_words(words): 
      return dict([(word, True) for word in words]) 
  
output:     
      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) 
      
      tokens = nltk.word_tokenize(text_data)
      labeled_tokens = label_words(tokens)
      return classifier.classify(labeled_tokens)
  