input: from typing import List
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie reviews using NLP and VADER sentiment analysis.
      >>> classify_sentiment(["The movie was terrible. It felt like it would never end!", "This movie was amazing!"])
      ['The sentiment of the sentence is negative!', 'The sentiment of the sentence is positive!']
      """
  
      nltk.download('vader_lexicon')
      from nltk.sentiment import vader
      sentiment_results = []
      
      sid = vader.SentimentIntensityAnalyzer()
      
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiment_results.append('The sentiment of the sentence is negative!')
          else:
              sentiment_results.append('The sentiment of the sentence is positive!')
      
      return sentiment_results
output:     nltk.download('vader_lexicon')
      from nltk.sentiment import vader
      sentiment_results = []
      
      sid = vader.SentimentIntensityAnalyzer()
      
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiment_results.append('The sentiment of the sentence is negative!')
          else:
              sentiment_results.append('The sentiment of the sentence is positive!')
      
      return sentiment_results