| --- |
| license: apache-2.0 |
| task_categories: |
| - token-classification |
| - text-classification |
| language: |
| - ar |
| - da |
| - de |
| - en |
| - es |
| - fr |
| - hi |
| - hr |
| - id |
| - ja |
| - ko |
| - nl |
| - pt |
| - ru |
| - sk |
| - sv |
| - sw |
| - th |
| - tr |
| - vi |
| - zh |
| tags: |
| - aspect-based-sentiment-analysis |
| size_categories: |
| - 100K<n<1M |
| --- |
| # M-ABSA |
|
|
| This repo contains the data for our paper ****M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis****. |
|
|
| [](https://arxiv.org/abs/2502.11824) |
|
|
|
|
| # Data Description: |
| This is a dataset suitable for the __multilingual ABSA__ task with __triplet extraction__. |
|
|
| All datasets are stored in the data/ folder: |
|
|
| - All dataset contains __7__ domains. |
|
|
| ``` |
| domains = ["coursera", "hotel", "laptop", "restaurant", "phone", "sight", "food"] |
| ``` |
| - Each dataset contains __21__ languages. |
| ``` |
| langs = ["ar", "da", "de", "en", "es", "fr", "hi", "hr", "id", "ja", "ko", "nl", "pt", "ru", "sk", "sv", "sw", "th", "tr", "vi", "zh"] |
| ``` |
|
|
| - The labels contain triplets with __[aspect term, aspect category, sentiment polarity]__. Each sentence is separated by __"####"__, with the first part being the sentence and the second part being the corresponding triplet. Here is an example, where the triplet includes __[aspect term, aspect category, sentiment polarity]__. |
|
|
| ``` |
| This coffee brews up a nice medium roast with exotic floral and berry notes .####[['coffee', 'food quality', 'positive']] |
| ``` |
|
|
| - Each dataset is divided into training, validation, and test sets. |
|
|
|
|
| ## Citation |
|
|
| If the code or dataset is used in your research, please star our repo and cite our paper as follows: |
| ``` |
| @misc{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}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2502.11824}, |
| } |
| ``` |
