input: from typing import Union
  
  def classify_sentiment(review: str) -> Union[str, None]:
      """
      Classify the sentiment of a movie review using Natural Language Processing.
      The function uses the VADER (Valence Aware Dictionary and sEntiment Reasoner)
      from the NLTK library to classify the sentiment of the given review.
      Returns 'negative' if the 'neg' score is greater than the 'pos' score, 'positive' 
      otherwise. If the review is empty or cannot be classified, returns None.
      
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      'negative'
      >>> classify_sentiment('This movie was fantastic, definitely a must-watch!')
      'positive'
      """
      if not review:
          return None
      
      import nltk
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
  
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'
output:     if scores['neg'] == scores['pos']:
          return 'neutral'