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

TL;DR

This paper introduces ROAST, a new review-level ABSA task that detects sentiment aspects, targets, and opinions across multiple languages and domains, addressing previous limitations of sentence-level focus and low-resource language evaluation.

Contribution

The paper proposes a novel review-level ABSA task, extends datasets for multilingual and multi-domain analysis, and aims to improve practical understanding of ABSA in diverse contexts.

Findings

Extended datasets for low-resource languages

Evaluation across multiple languages and domains

Enhanced understanding of review-level ABSA

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).