input: from typing import Dict
  
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      # 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
  
      """
      >>> sentiment_analyzer("I love this product so much! It's amazing.")
      {'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8316}
      >>> sentiment_analyzer("This is a very bad experience. I hate it.")
      {'neg': 0.508, 'neu': 0.492, 'pos': 0.0, 'compound': -0.6249}
      """
output:     from nltk.tokenize import word_tokenize
      from nltk.stem import SnowballStemmer
      import re
      from nltk.corpus import stopwords
      from nltk.sentiment import SentimentIntensityAnalyzer