Using Argument-based Features to Predict and Analyse Review Helpfulness
Haijing Liu, Yang Gao, Pin 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.- Anthology ID:
- D17-1142
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1358–1363
- Language:
- URL:
- https://aclanthology.org/D17-1142
- DOI:
- 10.18653/v1/D17-1142
- Cite (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.
- Cite (Informal):
- Using Argument-based Features to Predict and Analyse Review Helpfulness (Liu et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D17-1142.pdf