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

2026-04-08

AI Summary

This work addresses the limitations of traditional aspect-based sentiment analysis, which relies on discrete polarity labels and struggles to capture the nuanced emotions and stances prevalent in public discourse. To overcome this, the authors propose a dimensional modeling approach that introduces, for the first time, the valence-arousal (VA) continuous space into aspect-level sentiment and stance analysis. They define two parallel tasks: DimABSA, which extracts aspect sentiment triplets or quadruplets and regresses sentiment intensity in the VA space, and DimStance, which treats stance targets as aspects for dimensional stance modeling. To jointly evaluate structural extraction and regression performance, they design a continuous F1 (cF1) metric. The shared task attracted over 400 participants, yielding 112 submissions and 42 system papers, and provides comprehensive analyses of baseline and state-of-the-art systems, advancing research in fine-grained sentiment and stance understanding.

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.

Problem

• Aspect-Based Sentiment Analysis
• Dimensional Sentiment Analysis
• Valence-Arousal Space
• Stance Detection
• Public-Issue Discourse

Innovation

• Dimensional Sentiment Analysis
• Valence-Arousal Space
• Aspect-Based Sentiment Analysis
• Stance Detection
• Continuous F1 Metric