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 Jianfei Yu Rui Xia
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 explicit and
implicit aspects and opinions for ABSA research. Meanwhile, we evaluate
generative and non-generative baselines on multiple ABSA subtasks under the
open domain setting, and the results show that open domain ABSA as well as
mining implicit aspects and opinions remain ongoing challenges to be addressed.
The datasets are publicly released at \url{https://github.com/NUSTM/MEMD-ABSA}.