input: 
  from typing import Dict
  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
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyzes the sentiment of a given text and returns a dictionary containing 
      the polarity scores.
      >>> sentiment_analyzer("I love this product! It's amazing.")
      {'neg': 0.0, 'neu': 0.253, 'pos': 0.747, 'compound': 0.8466}
      >>> sentiment_analyzer("This is a terrible experience.")
      {'neg': 0.687, 'neu': 0.313, '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 word not 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 word not in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score