Aspect-Based Sentiment Analysis (ABSA) has experienced tremendous expansion and diversity due to various shared tasks spanning several languages and fields and organized via SemEval workshops and Germeval. Nonetheless, a few shortcomings still need to be addressed, such as the lack of low-resource language evaluations and the emphasis on sentence-level analysis. To thoroughly assess ABSA techniques in the context of complete reviews, this research presents a novel task, Review-Level Opinion Aspect Sentiment Target (ROAST). ROAST seeks to close the gap between sentence-level and text-level ABSA by identifying every ABSA constituent at the review level. We extend the available datasets to enable ROAST, addressing the drawbacks noted in previous research by incorporating low-resource languages, numerous languages, and a variety of topics. Through this effort, ABSA research will be able to cover more ground and get a deeper comprehension of the task and its practical application in a variety of languages and domains (https://github.com/RiTUAL-UH/ROAST-ABSA).

翻译：基于方面的情感分析（ABSA）通过涵盖多种语言和领域的各类共享任务（主要通过SemEval研讨会和Germeval组织）实现了巨大扩展和多样化。然而，仍有一些缺陷亟待解决，例如缺乏低资源语言评估以及对句子级分析的过度侧重。为在完整评论语境下全面评估ABSA技术，本研究提出了一项新颖任务——评论级观点方面情感目标（ROAST）。ROAST旨在通过识别评论层面的所有ABSA要素，弥合句子级与文本级ABSA之间的鸿沟。我们通过纳入低资源语言、多语种及多样化主题来扩展现有数据集以支持ROAST任务，从而解决先前研究中指出的不足。此项工作将推动ABSA研究覆盖更广领域，并深化对该任务及其在多语言多领域实际应用的理解（https://github.com/RiTUAL-UH/ROAST-ABSA）。