input: Write a function to analyze the sentiment of a given text using tokenization, stemming, and sentiment analysis. The function should return a dictionary of polarity scores for the text. If the input text is empty, the function should return an empty dictionary.
  assert sentiment_analyzer("I am very happy today.")=={'neg': 0.0, 'neu': 0.333, 'pos': 0.667, 'compound': 0.6369}
  assert sentiment_analyzer("This is a terrible day.")=={'neg': 0.688, 'neu': 0.312, 'pos': 0.0, 'compound': -0.7579}
  assert sentiment_analyzer("The weather is neither good nor bad.")=={'neg': 0.138, 'neu': 0.762, 'pos': 0.099, 'compound': 0.2263}
  assert sentiment_analyzer("")=={}
  assert sentiment_analyzer("Go away!")=={'neg': 0.518, 'neu': 0.482, 'pos': 0.0, 'compound': -0.4767}
output: from nltk.sentiment import SentimentIntensityAnalyzer
  
  def sentiment_analyzer(text):
      if not text:
          return {}
      sia = SentimentIntensityAnalyzer()
      return sia.polarity_scores(text)