  Sentiment polarity classification is perhaps the most widely studied topic.
It classifies an opinionated document as expressing a positive or negative
opinion. In this paper, using movie review dataset, we perform a comparative
study with different single kind linguistic features and the combinations of
these features. We find that the classic topic-based classifier(Naive Bayes and
Support Vector Machine) do not perform as well on sentiment polarity
classification. And we find that with some combination of different linguistic
features, the classification accuracy can be boosted a lot. We give some
reasonable explanations about these boosting outcomes.
