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

Chengyan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng +5 more
2/17/2025
cs.CL

No official code found. Best implementation: swaggy66/M-ABSA (21 stars, last commit 2025-11-24).

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.

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Code Implementations(1)

swaggy66/M-ABSACommunity100%

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

215Shell, PythonFeb 14, 202511 months ago
multilingualmultilingual-absamultilingual-nlp

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Cite this paper

@article{wu2025mabsa,
  title  = {M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis},
  author = {Chengyan Wu and Bolei Ma and Yihong Liu and Zheyu Zhang and Ningyuan Deng and Yanshu Li and Baolan Chen and Yi Zhang and Yun Xue and Barbara Plank},
  year   = {2025},
  eprint = {2502.11824},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.11824v3}
}

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