input: Write a function to classify the sentiment of movie reviews using Natural Language Processing. The function should take a string input representing the movie review, calculate the sentiment scores using the VADER sentiment analysis tool, and print whether the sentiment of the sentence is negative or positive based on the score. If the negative score is higher, print "The sentiment of the sentence is negative!"; otherwise, print "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("Absolutely fantastic! This movie was amazing from start to finish.") == "The sentiment of the sentence is positive!"
  assert classify_sentiment("I felt neutral about the movie, neither good nor bad.") == "The sentiment of the sentence is positive!"
  assert classify_sentiment("The acting was bad and the plot was confusing.") == "The sentiment of the sentence is negative!"
  assert classify_sentiment("A decent film, but nothing special.") == "The sentiment of the sentence is positive!"
output: from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
  
  def classify_sentiment(review):
      analyzer = SentimentIntensityAnalyzer()
      scores = analyzer.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!"