input: Write a Python function to classify the sentiment of movie reviews using Natural Language Processing. If the sentiment is negative, return "negative". If the sentiment is positive, return "positive". Assume the function will use VADER, an NLP tool within the NLTK library.
  import nltk
  from nltk.sentiment import vader
  
  def sentiment_analysis(review):
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'
  
  assert sentiment_analysis('The movie was terrible. It felt like it would never end!') == 'negative'
  assert sentiment_analysis('The movie was absolutely fantastic and I loved every minute of it!') == 'positive'
  assert sentiment_analysis('') == 'positive'
output: def sentiment_analysis(review):
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'