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

2024-05-30 · Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio

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).

📄 PDF Abstract BibTeX arXiv:2405.20274

Code (1)

ritual-uh/roast-absa 공식 구현

Tasks

Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)DiversitySentenceSentiment Analysis

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Aspect-Based Sentiment Analysis Aspect Sentiment Triplet Extraction Sentiment Analysis Triplet