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

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

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.
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
Pages1358–1363
Number of pages6
DOIs
Publication statusPublished - Sep 2017
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 34292258