@inproceedings{yu-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis ({D}im{ABSA})",
    author = "Yu, Liang-Chih  and
      Becker, Jonas  and
      Muhammad, Shamsuddeen Hassan  and
      Abdulmumin, Idris  and
      Lee, Lung-Hao  and
      Lin, Ying-Lung  and
      Wang, Jin  and
      Wahle, Jan Philip  and
      Lima Ruas, Terry  and
      Loukachevitch, Natalia  and
      Panchenko, Alexander  and
      Alimova, Ilseyar  and
      Wanzare, Lilian Diana Awuor  and
      Odhiambo, Nelson  and
      Gipp, Bela  and
      Chang, Kai-Wei  and
      Mohammad, Saif",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.452/",
    pages = "3753--3778",
    ISBN = "979-8-89176-414-9",
    abstract = "We present the SemEval-2026 shared task on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which improves traditional ABSA by modeling sentiment along valence{--}arousal (VA) dimensions rather than using categorical polarity labels. To extend ABSA beyond consumer reviews to public-issue discourse (e.g., political, energy, and climate issues), we introduce an additional task, Dimensional Stance Analysis (DimStance), which treats stance targets as aspects and reformulates stance detection as regression in the VA space. The task consists of two tracks: Track A (DimABSA) and Track B (DimStance). Track A includes three subtasks: (1) dimensional aspect sentiment regression, (2) dimensional aspect sentiment triplet extraction, and (3) dimensional aspect sentiment quadruplet extraction, while Track B includes only the regression subtask for stance targets. We also introduce a continuous F1 (cF1) metric to jointly evaluate structured extraction and VA regression.The task attracted more than 400 participants, resulting in 112 final submissions and 42 system description papers. We report baseline results, discuss top-performing systems, and analyze key design choices to provide insights into dimensional sentiment analysis at the aspect and stance-target levels. All resources are available on our GitHub repository."
}