Using Argument-based Features to Predict and Analyse Review Helpfulness
Haijing Liu | Yang Gao | Pin Lv | Mengxue Li | Shiqiang Geng | Minglan Li | Hao Wang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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.