input: def sentiment_analyzer(text: str) -> dict:
      """
      Analyze the sentiment of the given text using NLP techniques. Tokenizes the text, 
      stems the words, removes punctuation and stopwords, and then computes the polarity 
      score using VADER sentiment analysis.
      >>> sentiment_analyzer("I love this product! It's amazing.")
      {'neg': 0.0, 'neu': 0.268, 'pos': 0.732, 'compound': 0.8516}
      >>> sentiment_analyzer("I hate this product, it's terrible.")
      {'neg': 0.696, 'neu': 0.304, 'pos': 0.0, 'compound': -0.7964}
      """
      import re
      from nltk.stem.snowball import SnowballStemmer
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.sentiment.vader import SentimentIntensityAnalyzer 
  
      # Tokenize the text
      tokens = word_tokenize(text)
  
      # Create stemmer object
      stemmer = SnowballStemmer('english')
  
      # Stem each token
      stemmed_words = [stemmer.stem(word) for word in tokens]
  
      # Remove punctuation
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
  
      # Remove stopwords
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
  
      # Create a SentimentIntensityAnalyzer object
      sid = SentimentIntensityAnalyzer()
  
      # Get the polarity score of the text
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
  
      return polarity_score
output:     return polarity_score