Abstract
To automatically assess the helpfulness of a customer review online, conventional approaches generally acquire various linguistic and neural embedding features solely from the textual content of the review itself as the evidence. We, however, find out that a helpful review is largely concerned with the metadata (such as the name, the brand, the category, etc.) of its target product. It leaves us with a challenge of how to choose the correct key-value product metadata to help appraise the helpfulness of free-text reviews more precisely. To address this problem, we propose a novel framework composed of two mutual-benefit modules. Given a product, a selector (agent) learns from both the keys in the product metadata and one of its reviews to take an action that selects the correct value, and a successive predictor (network) makes the free-text review attend to this value to obtain better neural representations for helpfulness assessment. The predictor is directly optimized by SGD with the loss of helpfulness prediction, and the selector could be updated via policy gradient rewarded with the performance of the predictor. We use two real-world datasets from Amazon.com and Yelp.com, respectively, to compare the performance of our framework with other mainstream methods under two application scenarios: helpfulness identification and regression of customer reviews. Extensive results demonstrate that our framework can achieve state-of-the-art performance with substantial improvements.- Anthology ID:
- D19-1177
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1675–1683
- Language:
- URL:
- https://aclanthology.org/D19-1177
- DOI:
- 10.18653/v1/D19-1177
- Cite (ACL):
- Miao Fan, Chao Feng, Mingming Sun, and Ping Li. 2019. Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1675–1683, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (Fan et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D19-1177.pdf