ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA

• URL: http://arxiv.org/abs/2405.20274v2
• Date: Thu, 18 Jul 2024 18:05:04 GMT
• Title: ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA
• Authors: Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio,
• Abstract summary: 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.
• Score: 50.90538760832107
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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).

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