input: Write a function to analyze the sentiment of a given text. Use tokenization, stemming, removal of punctuation and stopwords, and then calculate the polarity score using a sentiment analysis tool. The function should return the polarity score of the processed text.
  
  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):
      # 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
  
  assert sentiment_analyzer("I love this product!")=={'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8316}
  assert sentiment_analyzer("This is the worst experience I've ever had.")=={'neg': 0.68, 'neu': 0.32, 'pos': 0.0, 'compound': -0.7845}
  assert sentiment_analyzer("The movie was okay.")=={'neg': 0.0, 'neu': 0.619, 'pos': 0.381, 'compound': 0.4404}
  assert sentiment_analyzer("")=={'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
output: def sentiment_analyzer(text):
      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