Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2016
Abstract
Product reviews greatly influence purchase decisions in online shopping. A common burden of online shopping is that consumers have to search for the right answers through massive reviews, especially on popular products. Hence, estimating and predicting the helpfulness of reviews become important tasks to directly improve shopping experience. In this paper, we propose a new approach to helpfulness prediction by leveraging aspect analysis of reviews. Our hypothesis is that a helpful review will cover many aspects of a product at different emphasis levels. The first step to tackle this problem is to extract proper aspects. Because related products share common aspects to different degrees, we propose an aspect extraction model making use of product category information to balance the aspects of a general category and those of subcategories under it. On top of this model, a two-layer regressor is trained for helpfulness prediction. Experiment results show that we can improve helpfulness prediction by 7% than the baseline on 5 popular product categories from Amazon.com.
Keywords
Natural language processing, Semantic analysis, Text mining, artificial intelligence
Discipline
Databases and Information Systems | E-Commerce | Numerical Analysis and Scientific Computing | Sales and Merchandising
Publication
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI): San Jose, CA, 6-8 November: Proceedings
First Page
836
Last Page
843
ISBN
9781509044597
Identifier
10.1109/ICTAI.2016.0127
Publisher
IEEE
City or Country
Piscataway, NJ
Embargo Period
6-29-2021
Citation
YANG, Yinfei; CHEN, Cen; and BAO, Forrest Sheng.
Aspect-based helpfulness prediction for online product reviews. (2016). 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI): San Jose, CA, 6-8 November: Proceedings. 836-843.
Available at: https://ink.library.smu.edu.sg/sis_research/6017
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICTAI.2016.0127
Included in
Databases and Information Systems Commons, E-Commerce Commons, Numerical Analysis and Scientific Computing Commons, Sales and Merchandising Commons