用情感维度取代分类标签，让机器更懂复杂观点的正负与强度。
SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
- 将情感建模为高低唤醒维度，突破传统极性分类限制。
- 新增立场分析任务，支持政治等公共议题的连续情感解析。
- 提供新评价指标cF1，兼顾结构抽取与维度回归效果。
我们介绍了 SemEval-2026 第3项共享任务：维度化方面感知情感分析（DimABSA），该任务通过在效价-唤醒（VA）维度上建模情感，改进了传统方面感知情感分析（ABSA）。为将ABSA从消费者评论拓展至公共议题讨论（如政治、能源、气候问题），我们引入额外任务——维度化立场分析（DimStance），将立场目标视为方面，并将立场检测重新定义为在VA空间中的回归任务。该任务包含两个赛道：赛道A（DimABSA）和赛道B（DimStance）。赛道A包括三个子任务：（1）维度化方面情感回归，（2）维度化方面情感三元组抽取，（3）维度化方面情感四元组抽取；赛道B仅包含立场目标的回归子任务。我们还提出一种连续F1（cF1）指标，用于联合评估结构化抽取与VA回归。任务吸引了超过400名参与者，提交112份最终结果，产出42篇系统描述论文。本文报告基线结果，分析顶尖系统表现，并探讨关键设计选择，为方面级与立场目标级的维度情感分析提供洞见。所有资源已公开于GitHub仓库。
原文摘要 · Abstract (English)
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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