title: how to improve sentiment analysis model?
body: <p>I am using SVM to determine whether a review is positive or negative. </p>
  
  <p>So far I've done stemming, putting a neg tag before words like "no good", reduce dimensionalities by CHI score of each word, getting rid of stop words and transforming the reviews into a TF vector, but the performance is still poor, what else can I do to improve it? How can I use the information of whether a word is positive or negative?
  I've tried TF_IDF, it didn't work as well as TF model.</p>