input: from typing import List
  
  def classify_movie_review_sentiment(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of each movie review in the given list using Natural Language Processing.
      Each review's sentiment is classified as either 'positive' or 'negative' based on the polarity scores computed by VADER.
      >>> result = classify_movie_review_sentiment(['The movie was terrible. It felt like it would never end!'])
      >>> result[0]
      'negative'
      """
      import nltk
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
  
      for review in reviews:
          scores = sid.polarity_scores(review)
          sentiment = 'positive' if scores['pos'] > scores['neg'] else 'negative'
          sentiments.append(sentiment)
      
      return sentiments
output:     import nltk
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
  
      for review in reviews:
          scores = sid.polarity_scores(review)
          sentiment = 'positive' if scores['pos'] > scores['neg'] else 'negative'
          sentiments.append(sentiment)
      
      return sentiments