input: Write a function to classify the sentiment of a movie review using Natural Language Processing. The function should take a movie review as input and determine if the sentiment is negative or positive based on the VADER sentiment analysis model. If the negative sentiment score is higher, the function should return "The sentiment of the sentence is negative!". Otherwise, it should return "The sentiment of the sentence is positive!". The function should use the `vader` module from `nltk.sentiment` for sentiment analysis.
  
  
  import nltk
  from nltk.sentiment import vader
  
  def classify_sentiment(review):
      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_sentiment("The movie was terrible. It felt like it would never end!") == 'The sentiment of the sentence is negative!'
  assert classify_sentiment("I loved that movie. It was so captivating!") == 'The sentiment of the sentence is positive!'
  assert classify_sentiment("The film was okay.") == 'The sentiment of the sentence is positive!'
  assert classify_sentiment("It was the worst experience ever.") == 'The sentiment of the sentence is negative!'
  assert classify_sentiment("") == 'The sentiment of the sentence is positive!'
  
output: def classify_sentiment(review):
      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!'