Using Argument-based Features to Predict and Analyse Review Helpfulness. / Liu, Haijing; Gao, Yang; Lv, Ping; Li, Mengxue; Geng, Shiqiang; Li, Minglan; Wang, Hao.

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark : Association for Computational Linguistics, 2017. p. 1358–1363.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Haijing Liu
  • Yang Gao
  • Ping Lv
  • Mengxue Li
  • Shiqiang Geng
  • Minglan Li
  • Hao Wang


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.
Original languageEnglish
Title of host publicationProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Place of PublicationCopenhagen, Denmark
PublisherAssociation for Computational Linguistics
Number of pages6
Publication statusPublished - Sep 2017
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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