input: def classify_text_sentiment(text: str, classifier) -> str:
      """ Classify the given text into either 'positive' or 'negative' sentiment using a pre-trained NaiveBayesClassifier.
      >>> positiveText = "This is a great movie!"
      >>> negativeText = "This movie was terrible"
      >>> 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_text_sentiment("This is a great movie!", classifier)
      'positive'
      >>> classify_text_sentiment("This movie was terrible", classifier)
      'negative'
      """
output:     words = nltk.word_tokenize(text)
      features = label_words(words)
      sentiment = classifier.classify(features)
      return sentiment