input: from typing import Dict
  from nltk.stem.snowball import SnowballStemmer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from nltk.sentiment.vader import SentimentIntensityAnalyzer 
  import re
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyze the sentiment of a given text using the VADER SentimentIntensityAnalyzer.
      The function processes the text by tokenizing it, stemming each token, removing punctuation,
      and excluding stopwords. It then computes the sentiment polarity scores.
      >>> sentiment_analyzer("I love this product! It's amazing.")
      {'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8439}
      >>> sentiment_analyzer("This is extremely disappointing.")
      {'neg': 0.656, 'neu': 0.344, 'pos': 0.0, 'compound': -0.5423}
      """
      tokens = word_tokenize(text)
      stemmer = SnowballStemmer('english')
      stemmed_words = [stemmer.stem(word) for word in tokens]
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score
output:     tokens = word_tokenize(text)
      stemmer = SnowballStemmer('english')
      stemmed_words = [stemmer.stem(word) for word in tokens]
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score