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.
Cite
CITATION STYLE
Xu, R., Lin, H., Liao, M., Han, X., Xu, J., Tan, W., … Sun, L. (2022). ECO v1: Towards Event-Centric Opinion Mining. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 2743–2753). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.findings-acl.216
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