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M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis

MCML Authors

Link to Profile Barbara Plank PI Matchmaking

Barbara Plank

Prof. Dr.

Principal Investigator

AI and Computational Linguistics

Abstract

Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multi-domain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research.

inproceedings

EMNLP 2025

Conference on Empirical Methods in Natural Language Processing. Suzhou, China, Nov 04-09, 2025. To be published. Preprint available.
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A* Conference

Authors

C. Wu • B. Ma • Y. Liu • Z. Zhang • N. Deng • Y. Li • B. Chen • Y. Zhang • Y. Xue • B. Plank

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Research Areas

 A1 | Statistical Foundations & Explainability

 B2 | Natural Language Processing

 C4 | Computational Social Sciences

BibTeXKey: WML+25

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