title: word sense disambiguation in sentiwordnet python
body: <p>I'm currently doing research for sentiment analysis in twitter.
  i want to combine predefined lexicon resource like sentiwordnet polarity score. and then proceed it with machine learning.
  the problem is in getting the correct score of sentiwordnet, previous work always simply choose by the total score of negative and positive polarity of the word meaning.
  i mean for example the word "mad" can appear 3 times as negative and 2 times as positive words.
  most of previous work will automatically average of each polarity.
  so i want to disambiguate the words before getting the score so we can really use the sentiwordnet as it should be.
  i was thinking by comparing the similarity of target sentence and gloss sentence..
  is there any method to compare it? do you think it will works?
  if not please share your idea..</p>
  
  <p>i'm completely new to this field and novice python programmer, so i really need advice from you..
  thank you..</p>