Abstract
In product reviews, user and product aspects are useful in sentiment analysis. Nevertheless, previous studies mainly focus on modeling user and product aspects without considering the relationship between users and products. The relationship between users and products is typically helpful in estimating the bias of a user toward a product. In this paper, we, therefore, introduce the Graph Neural Network-based model with the pre-trained Language Model (GNNLM), where the relationship between users and products is incorporated. We conducted experiments on three well-known benchmarks for sentiment classification with the user and product information. The experimental results show that the relationship between users and products improves the performance of sentiment analysis. Furthermore, GNNLM achieves state-of-the-art results on yelp-2013 and yelp-2014 datasets.- Anthology ID:
- 2023.findings-acl.547
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8611–8618
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.547
- DOI:
- 10.18653/v1/2023.findings-acl.547
- Cite (ACL):
- Natthawut Kertkeidkachorn and Kiyoaki Shirai. 2023. Sentiment Analysis using the Relationship between Users and Products. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8611–8618, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Sentiment Analysis using the Relationship between Users and Products (Kertkeidkachorn & Shirai, Findings 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.findings-acl.547.pdf