input: 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):
      """ Analyzes the sentiment of a given text by tokenizing it, stemming the words,
      removing punctuation and stopwords, and then calculating the sentiment polarity
      score using the VADER sentiment analysis tool.
      >>> sentiment_analyzer("I am very happy today.")
      {'neg': 0.0, 'neu': 0.383, 'pos': 0.617, 'compound': 0.6249}
      >>> sentiment_analyzer("This is so bad!")
      {'neg': 0.539, 'neu': 0.461, 'pos': 0.0, 'compound': -0.4767}
      """
      # 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:     # 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