A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification
This is an incremental study that addresses feature selection for sentiment analysis, primarily benefiting researchers in natural language processing.
The paper tackled sentiment polarity classification by comparing various linguistic features and their combinations on a movie review dataset, finding that certain feature combinations significantly boost classification accuracy.
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.