ECO v1: Towards Event-Centric Opinion Mining
This work addresses a gap in opinion mining for events, which could aid decision-making and social applications, though it is incremental as it builds on existing theories and methods.
The paper introduces the task of event-centric opinion mining, which differs from entity-centric approaches, and demonstrates its feasibility through a new corpus and benchmark framework.
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.