A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification

TL;DR

This paper compares various linguistic features and their combinations for sentiment polarity classification using movie reviews, showing that feature combination significantly improves accuracy over classic classifiers.

Contribution

It provides a comparative analysis of linguistic features and demonstrates how their combinations enhance sentiment classification performance.

Findings

Feature combinations boost classification accuracy.

Classic classifiers like Naive Bayes and SVM perform less effectively.

Certain linguistic features contribute more significantly to accuracy.

Abstract

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.