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arXiv · 1707.07279

Using Argument-based Features to Predict and Analyse Review Helpfulness

Haijing Liu·Yang Gao·Pin Lv·Mengxue Li·Shiqiang Geng·Minglan Li·Hao Wang
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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.

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Haijing Liu, Yang Gao, Pin Lv, Mengxue Li, Shiqiang Geng, Minglan Li, Hao Wang. 2017-07-23. Using Argument-based Features to Predict and Analyse Review Helpfulness. https://arxiv.org/abs/1707.07279

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