ECO v1: Towards Event-Centric Opinion Mining

Ruoxi Xu, Hongyu Lin, Meng Liao, Xianpei Han, Jin Xu, Wei Tan, Yingfei Sun, Le Sun


Abstract
Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.
Anthology ID:
2022.findings-acl.216
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2743–2753
Language:
URL:
https://aclanthology.org/2022.findings-acl.216
DOI:
10.18653/v1/2022.findings-acl.216
Bibkey:
Cite (ACL):
Ruoxi Xu, Hongyu Lin, Meng Liao, Xianpei Han, Jin Xu, Wei Tan, Yingfei Sun, and Le Sun. 2022. ECO v1: Towards Event-Centric Opinion Mining. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2743–2753, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
ECO v1: Towards Event-Centric Opinion Mining (Xu et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.216.pdf
Software:
 2022.findings-acl.216.software.zip