  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).
