@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
%A Yu, Liang-Chih
%A Becker, Jonas
%A Muhammad, Shamsuddeen Hassan
%A Abdulmumin, Idris
%A Lee, Lung-Hao
%A Lin, Ying-Lung
%A Wang, Jin
%A Wahle, Jan Philip
%A Lima Ruas, Terry
%A Loukachevitch, Natalia
%A Panchenko, Alexander
%A Alimova, Ilseyar
%A Wanzare, Lilian Diana Awuor
%A Odhiambo, Nelson
%A Gipp, Bela
%A Chang, Kai-Wei
%A Mohammad, Saif
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F yu-etal-2026-semeval
%X 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.
%U https://aclanthology.org/2026.semeval-1.452/
%P 3753-3778
Markdown (Informal)
[SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://aclanthology.org/2026.semeval-1.452/) (Yu et al., SemEval 2026)
ACL
- Liang-Chih Yu, Jonas Becker, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Lung-Hao Lee, Ying-Lung Lin, Jin Wang, Jan Philip Wahle, Terry Lima Ruas, Natalia Loukachevitch, Alexander Panchenko, Ilseyar Alimova, Lilian Diana Awuor Wanzare, Nelson Odhiambo, Bela Gipp, Kai-Wei Chang, and Saif Mohammad. 2026. SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3753–3778, San Diego, California, USA. Association for Computational Linguistics.