ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA
<|reference_start|>ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA: 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).<|reference_end|>
@article{chebolu2024roast:,
    title={ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for
  ABSA},
    author={Siva Uday Sampreeth Chebolu and Franck Dernoncourt and Nedim Lipka and
  Thamar Solorio},
    journal={arXiv preprint arXiv:2405.20274},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20274},
    primaryClass={cs.CL cs.AI cs.LG}
}