Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review. 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 27,470 sentence-level quadruples and 17,092 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 experiments, establishing initial baselines. We hope the OATS dataset augments current resources, paving the way for an encompassing exploration of ABSA (https://github.com/RiTUAL-UH/OATS-ABSA).

翻译：方面级情感分析（ABSA）旨在深入理解用户生成评论中特定元素对应的情感倾向。其目标是通过分析用户生成的评论，确定：（a）被评论的目标实体；（b）该实体所属的高层方面；（c）用于表达观点情感词；以及（d）针对目标与方面所表达的情感。尽管各类基准数据集已推动ABSA领域取得进展，但它们常存在领域局限性与数据粒度不足等问题。针对这些不足，我们提出了OATS数据集，该数据集涵盖三个全新领域，包含27,470个句子级四元组与17,092个评论级元组。本工作旨在弥合当前研究中存在的具体空白：长期集中于餐厅、笔记本电脑等熟领域；面向复杂四元组抽取任务的数据资源匮乏；以及句子级与评论级情感协同关系的偶发性忽视。此外，为阐明OATS的潜在价值并揭示其能解决的各种ABSA子任务，我们通过实验建立了初始基线。期望OATS数据集能够丰富现有资源，为全面探索ABSA开辟新路径（https://github.com/RiTUAL-UH/OATS-ABSA）。