input: 
  def sentiment_analyzer(text):
      """Analyze the sentiment of a given text.
      >>> sentiment_analyzer("I am very happy today!")
      {'neg': 0.0, 'neu': 0.421, 'pos': 0.579, 'compound': 0.6486}
      >>> sentiment_analyzer("This is not a good product at all.")
      {'neg': 0.508, 'neu': 0.492, 'pos': 0.0, 'compound': -0.6249}
      """
      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:     from nltk.tokenize import word_tokenize
      from nltk.stem.snowball import SnowballStemmer
      from nltk.corpus import stopwords
      from nltk.sentiment import SentimentIntensityAnalyzer
      import re