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

Paper · 2025 · ChengYan Wu, Bolei Ma, Yihong Liu et al.

Summary

Research on aspect-based sentiment analysis (ABSA) relies mostly on English data. Earlier multilingual resources either use different content per language or cover only a few languages without aspect categories, which makes controlled cross-lingual comparison hard. The authors built M-ABSA, a parallel dataset for triplet extraction (aspect term, aspect category, sentiment polarity), framed as the TASD task. They took six existing annotated English corpora and annotated one new domain, giving 7 domains in total. They then machine-translated all sentences into 20 further languages. To keep labels aligned with the translated text, they inserted span markers around aspect terms before translation. Automatic quality checks and human review followed. Baseline experiments cover zero-shot cross-lingual transfer, cross-domain transfer and zero-shot prompting of LLMs. They show the benchmark is challenging and that transfer works best between related domains. The resource lets researchers evaluate multilingual ABSA under strictly comparable conditions. The marker-based projection pipeline can also be reused to extend other monolingual span-annotated datasets.

Key points

  • Coverage: 21 languages (English plus 20 translations) across 7 domains: Coursera, Food, Hotel, Laptop, Phone, Restaurant and Sight. The source corpora come from Chebolu et al. (2024a), Cai et al. (2021), Zhou et al. (2023), Pontiki et al. (2016) and Wang et al. (2023).
  • Balanced subsets: Domain sizes are kept similar, for example 2,147 Hotel sentences (TripAdvisor), 2,136 Food sentences (Amazon fine food) and 2,156 Coursera sentences.
  • Cross-domain results: In English cross-domain UABSA, in-domain F1 is highest (e.g., Phone 73.36, Food 72.75). Restaurant→Hotel reaches 62.68, second only to in-domain Hotel at 68.01. Phone→Hotel gives the weakest transfer into Hotel, at 27.44.
  • Translation evaluation: Human judges recruited via Prolific rated translation quality and were paid £9 per hour. The authors note that opinion phrases were left out because long phrases translate poorly.
  • Availability: The data is on Hugging Face (Multilingual-NLP/M-ABSA) and the code on GitHub (swaggy66/M-ABSA), for research use only.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Brown UniversityProvidence · United States76 works · 25,952 citations🎓Ludwig-Maximilians-Universität MünchenMunich · Germany107 works · 56,478 citations🎓Renmin University of ChinaBeijing · China114 works · 16,118 citations🎓Technical University of MunichMunich · Germany55 works · 48,474 citations🎓Munich Center for Machine LearningMunich · Germany57 works · 1,094 citations🎓ShanghaiTech UniversityShanghai · China13 works · 1,974 citations🎓Friedrich-Alexander-Universität Erlangen-NürnbergErlangen · Germany8 works · 48 citations🎓South China Normal UniversityGuangzhou · China5 works · 24 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Sentiment & emotion · 972 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue and Barbara Plank. 2025. M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.128

@inproceedings{wu2025m,
  title = {{M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis}},
  author = {ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue and Barbara Plank},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.128},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.128}
}

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