input: from typing import List
  
  def sentiment_analysis(reviews: List[str]) -> List[str]:
      """ Analyze the sentiment of each movie review in the list using VADER sentiment analysis.
      Return a list of strings where each string indicates the sentiment of the corresponding review.
      >>> sentiment_analysis(['The movie was terrible. It felt like it would never end!'])
      ['The sentiment of the sentence is negative!']
      >>> sentiment_analysis(['I loved the movie!', 'The acting was superb.'])
      ['The sentiment of the sentence is positive!', 'The sentiment of the sentence is positive!']
      """
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      sid = SentimentIntensityAnalyzer()
      sentiments = []
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append('The sentiment of the sentence is negative!')
          else:
              sentiments.append('The sentiment of the sentence is positive!')
      return sentiments
output:     from nltk.sentiment.vader import SentimentIntensityAnalyzer
      sid = SentimentIntensityAnalyzer()
      sentiments = []
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append('The sentiment of the sentence is negative!')
          else:
              sentiments.append('The sentiment of the sentence is positive!')
      return sentiments