Aspect-based sentiment Analysis (ABSA) delves into understanding sentiments specific to distinct elements within textual content. It aims to analyze user-generated reviews to determine a) the target entity being reviewed, b) the high-level aspect to which it belongs, c) the sentiment words used to express the opinion, and d) the sentiment expressed toward the targets and the aspects. While various benchmark datasets have fostered advancements in ABSA, they often come with domain limitations and data granularity challenges. Addressing these, we introduce the OATS dataset, which encompasses three fresh domains and consists of 20,000 sentence-level quadruples and 13,000 review-level tuples. Our initiative seeks to bridge specific observed gaps: the recurrent focus on familiar domains like restaurants and laptops, limited data for intricate quadruple extraction tasks, and an occasional oversight of the synergy between sentence and review-level sentiments. Moreover, to elucidate OATS's potential and shed light on various ABSA subtasks that OATS can solve, we conducted in-domain and cross-domain experiments, establishing initial baselines. We hope the OATS dataset augments current resources, paving the way for an encompassing exploration of ABSA.

翻译：方面级情感分析（ABSA）旨在深入理解文本内容中特定要素的情感倾向。该任务的目标是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，c）用于表达意见的情感词，以及d）对目标和方面所表达的情感。尽管各种基准数据集推动了ABSA的发展，但它们通常存在领域局限性和数据粒度不足的问题。为解决这些问题，我们引入了OATS数据集，该数据集涵盖三个全新领域，包含20,000个句子级四元组和13,000个评论级元组。我们旨在弥合以下具体观察到的缺口：对餐厅和笔记本电脑等常见领域的重复关注、面向复杂四元组抽取任务的有限数据，以及偶尔忽略句子级与评论级情感之间协同作用的问题。此外，为阐明OATS数据集的潜力并揭示其能够解决的ABSA子任务，我们开展了域内和跨域实验，建立了初始基线。我们期望OATS数据集能够丰富现有资源，为ABSA的全面探索铺平道路。