Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects. Numerous yet scattered corpora for ABSA make it difficult for researchers to identify corpora best suited for a specific ABSA subtask quickly. This study aims to present a database of corpora that can be used to train and assess autonomous ABSA systems. Additionally, we provide an overview of the major corpora for ABSA and its subtasks and highlight several features that researchers should consider when selecting a corpus. Finally, we discuss the advantages and disadvantages of current collection approaches and make recommendations for future corpora creation. This survey examines 65 publicly available ABSA datasets covering over 25 domains, including 45 English and 20 other languages datasets.

翻译：基于方面的情感分析（ABSA）是一个自然语言处理问题，需要分析用户生成的评论以确定：a) 被评论的目标实体，b) 该实体所属的高层次方面，以及c) 针对目标和方面所表达的情感。众多但分散的ABSA语料库使得研究人员难以快速识别最适合特定ABSA子任务的语料库。本研究旨在呈现一个可用于训练和评估自主ABSA系统的语料库数据库。此外，我们概述了ABSA及其子任务的主要语料库，并强调了研究人员在选择语料库时应考虑的若干特征。最后，我们讨论了当前收集方法的优缺点，并对未来语料库的创建提出了建议。本综述考察了65个公开可用的ABSA数据集，涵盖超过25个领域，包括45个英语数据集和20个其他语言数据集。