A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification

Sentiment polarity classification is perhaps the most widely studied topic. It classifies an opinionated document as expressing a posi-tive or negative opinion. In this paper, us-ing movie review dataset, we perform a com-parative study with different single kind lin-guistic features and the combinations of these features. We find that the classic topic-based classifier(Naive Bayes and Support Vec-tor Machine) do not perform as well on senti-ment polarity classification. And we find that with some combination of different linguis-tic features, the classification accuracy can be boosted a lot. We give some reasonable expla-nations about these boosting outcomes.