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

Using Argument-based Features to Predict and Analyse Review Helpfulness