MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
This provides a new dataset for researchers in opinion mining to address limitations in existing datasets, though it is incremental as it builds on prior ABSA work.
The authors tackled the lack of comprehensive datasets for aspect-based sentiment analysis by creating a large-scale Multi-Element Multi-Domain dataset covering four elements across five domains, with nearly 20,000 sentences and 30,000 annotated quadruples, and found that open domain ABSA and mining implicit aspects remain challenging based on baseline evaluations.
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}.