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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.</abstract>
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%0 Conference Proceedings
%T Using Argument-based Features to Predict and Analyse Review Helpfulness
%A Liu, Haijing
%A Gao, Yang
%A Lv, Pin
%A Li, Mengxue
%A Geng, Shiqiang
%A Li, Minglan
%A Wang, Hao
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F liu-etal-2017-using
%X 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.
%R 10.18653/v1/D17-1142
%U https://aclanthology.org/D17-1142/
%U https://doi.org/10.18653/v1/D17-1142
%P 1358-1363

Markdown (Informal)

[Using Argument-based Features to Predict and Analyse Review Helpfulness](https://aclanthology.org/D17-1142/) (Liu et al., EMNLP 2017)

• Using Argument-based Features to Predict and Analyse Review Helpfulness (Liu et al., EMNLP 2017)

ACL

• Haijing Liu, Yang Gao, Pin Lv, Mengxue Li, Shiqiang Geng, Minglan Li, and Hao Wang. 2017. Using Argument-based Features to Predict and Analyse Review Helpfulness. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1358–1363, Copenhagen, Denmark. Association for Computational Linguistics.