input: from typing import List
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Given a list of movie reviews, classify the sentiment of each review
      using Natural Language Processing (NLP) techniques. The sentiment is
      categorized as either 'positive' or 'negative'.
  
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['negative']
      >>> classify_sentiment(['This was an amazing film with great performances!'])
      ['positive']
      """
  
      import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer
      nltk.download('vader_lexicon')
      
      sid = SentimentIntensityAnalyzer()
      sentiments = []
      
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append('negative')
          else:
              sentiments.append('positive')
      
      return sentiments
output:     import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer
      nltk.download('vader_lexicon')
      
      sid = SentimentIntensityAnalyzer()
      sentiments = []
      
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append('negative')
          else:
              sentiments.append('positive')
      
      return sentiments