Modeling and Prediction of Online Product Review Helpfulness: A Survey

Gerardo Ocampo Diaz, Vincent Ng


Abstract
As the amount of free-form user-generated reviews in e-commerce websites continues to increase, there is an increasing need for automatic mechanisms that sift through the vast amounts of user reviews and identify quality content. Review helpfulness modeling is a task which studies the mechanisms that affect review helpfulness and attempts to accurately predict it. This paper provides an overview of the most relevant work in helpfulness prediction and understanding in the past decade, discusses the insights gained from said work, and provides guidelines for future research.
Anthology ID:
P18-1065
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
698–708
Language:
URL:
https://aclanthology.org/P18-1065
DOI:
10.18653/v1/P18-1065
Bibkey:
Cite (ACL):
Gerardo Ocampo Diaz and Vincent Ng. 2018. Modeling and Prediction of Online Product Review Helpfulness: A Survey. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 698–708, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Modeling and Prediction of Online Product Review Helpfulness: A Survey (Ocampo Diaz & Ng, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1065.pdf
Data
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