Using Argument-based Features to Predict and Analyse Review Helpfulness

TL;DR

This paper explores the use of argument-based features, such as argumentative sentence ratios, to improve the prediction and analysis of helpful product reviews, demonstrating significant performance gains when combined with existing features.

Contribution

It introduces argument-based features for review helpfulness prediction and shows their effectiveness in boosting performance over state-of-the-art methods.

Findings

Argument-based features improve review helpfulness prediction.

Combining argument features with baseline features increases F1 score by 11.01%.

Manual annotation of arguments in hotel reviews supports the hypothesis.

Abstract

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.