@inproceedings{lee-etal-2026-dimabsa,
    title = "{D}im{ABSA}: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis",
    author = "Lee, Lung-Hao  and
      Yu, Liang-Chih  and
      Loukachevitch, Natalia V  and
      Alimova, Ilseyar  and
      Panchenko, Alexander  and
      Lin, Tzu-Mi  and
      Xu, Zhe-Yu  and
      Zhou, Jian-Yu  and
      Zheng, Guangmin  and
      Wang, Jin  and
      Awasthi, Sharanya  and
      Becker, Jonas  and
      Wahle, Jan Philip  and
      Ruas, Terry  and
      Muhammad, Shamsuddeen Hassan  and
      Mohammad, Saif M.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1881/",
    doi = "10.18653/v1/2026.acl-long.1881",
    pages = "40492--40518",
    ISBN = "979-8-89176-390-6",
    abstract = "Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical labels (e.g., positive, negative), which limits its ability to capture nuanced affective states. To address this limitation, we adopt a dimensional approach that represents sentiment with continuous valence{--}arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels. To this end, we introduce DimABSA, the first multilingual, dimensional ABSA resource annotated with both traditional ABSA elements (aspect terms, aspect categories, and opinion terms) and newly introduced VA scores. This resource contains 76,958 aspect instances across 42,590 sentences, spanning six languages and four domains. We further introduce three subtasks that combine VA scores with different ABSA elements, providing a bridge from traditional ABSA to dimensional ABSA. Given that these subtasks involve both categorical and continuous outputs, we propose a new unified metric, continuous F1 (cF1), which incorporates VA prediction error into standard F1. We provide a comprehensive benchmark using both prompted and fine-tuned large language models across all subtasks. Our results show that DimABSA is a challenging benchmark and provides a foundation for advancing multilingual dimensional ABSA. We publicly released the DimABSA dataset, which was used for Track A of SemEval-2026 Task 3, attracting over 300 participants."
}