SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)

TL;DR

The paper introduces SemEval-2026's shared task on Dimensional Aspect-Based Sentiment Analysis, extending traditional ABSA to VA dimensions and including stance analysis, with new evaluation metrics and resources.

Contribution

It presents a novel dimensional approach to ABSA and stance detection, along with a comprehensive shared task, datasets, and evaluation metrics for these tasks.

Findings

Over 400 participants engaged in the task.

112 system submissions and 42 system descriptions were received.

Baseline results and analysis of top systems are provided.

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.