input: Write a function to classify the sentiment of movie reviews using Natural Language Processing. The function should use the VADER sentiment analysis tool from the NLTK library. If the negative sentiment score is greater than the positive sentiment score, the function should return 'The sentiment of the sentence is negative!', otherwise, it should return 'The sentiment of the sentence is positive!'. If no sentence is provided, the function should return 'No input provided!'.
  def classify_review(review=None):
    if not review:
      return 'No input provided!'
    import nltk
    from nltk.sentiment import vader
    sid = vader.SentimentIntensityAnalyzer()
    scores = sid.polarity_scores(review)
    if scores['neg'] > scores['pos']:
      return 'The sentiment of the sentence is negative!'
    else:
      return 'The sentiment of the sentence is positive!'
  
  assert classify_review('The movie was terrible. It felt like it would never end!') == 'The sentiment of the sentence is negative!'
  assert classify_review('The movie was fantastic! I wish it never ended.') == 'The sentiment of the sentence is positive!'
  assert classify_review('') == 'No input provided!'
output: def classify_review(review=None):
    if not review:
      return 'No input provided!'
    import nltk
    from nltk.sentiment import vader
    sid = vader.SentimentIntensityAnalyzer()
    scores = sid.polarity_scores(review)
    if scores['neg'] > scores['pos']:
      return 'The sentiment of the sentence is negative!'
    else:
      return 'The sentiment of the sentence is positive!'