MEMD-ABSA: a multi-element multi-domain dataset for aspect-based sentiment analysis

Hongjie Cai, Nan Song, Zengzhi Wang, Qiming Xie, Qiankun Zhao, Ke Li, Siwei Wu, Shijie Liu, Heqing Ma, Jianfei Yu, Rui Xia

Abstract: Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elements of specific tasks, usually focusing on in-domain settings, ignoring implicit aspects and opinions, and with a small data scale. To address these issues, we propose a large-scale Multi-Element Multi-Domain dataset (MEMD) that covers the four elements across five domains, including nearly 20,000 review sentences and 30,000 quadruples annotated with both explicit and implicit aspects and opinions for ABSA research. Meanwhile, we conduct experiments on multiple ABSA subtasks under the open domain setting to verify the effectiveness of several generative and non-generative baselines, and the results show that open domain ABSA as well as mining implicit aspects and opinions remain ongoing challenges to be addressed.

External IDs:dblp:journals/lre/CaiSWXZLWLMYX25