MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis

TL;DR

This paper introduces MEMD-ABSA, a large-scale, multi-element, multi-domain dataset for aspect-based sentiment analysis, addressing limitations of existing datasets by including implicit aspects and opinions across diverse domains.

Contribution

The paper presents a comprehensive, annotated dataset covering multiple elements and domains, and evaluates baseline models, highlighting ongoing challenges in open domain ABSA.

Findings

Open domain ABSA remains challenging.

Implicit aspects and opinions are difficult to mine.

Large-scale dataset facilitates future research.

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 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}.