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.
BibTeX
@inproceedings{xu-etal-2022-eco,
title = "{ECO} v1: Towards Event-Centric Opinion Mining",
author = "Xu, Ruoxi and
Lin, Hongyu and
Liao, Meng and
Han, Xianpei and
Xu, Jin and
Tan, Wei and
Sun, Yingfei and
Sun, Le",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.216/",
doi = "10.18653/v1/2022.findings-acl.216",
pages = "2743--2753"
}