Call for Participation: SemEval-2026 Task 3 – Dimensional Aspect-Based Sentiment Analysis and Dimensional Stance Detection

[Shamsuddeen Muhammad]

Dear Colleagues, 

We are pleased to invite you to SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis on Customer Reviews and Stance Datasets.

Aspect-Based Sentiment Analysis (ABSA) is widely used to analyze opinions and sentiments at the aspect level. However, most current ABSA research relies on coarse-grained categorical sentiment labels (e.g., positive, negative, neutral). This contrasts with long-established theories in psychology and affective science, where sentiment is represented along fine-grained, real-valued dimensions of valence (negative ↔ positive) and arousal (sluggish ↔ excited). This valence–arousal (VA) framework has driven the emergence of dimensional sentiment analysis, enabling more nuanced interpretations of emotional expression and supporting a broader range of applications.

To address this gap, we introduce Dimensional ABSA (DimABSA), a shared task integrating dimensional sentiment analysis into the traditional ABSA paradigm. We further note a conceptual similarity between stance detection and ABSA when the stance target is treated as an aspect. Building on this connection, we propose Dimensional Stance Analysis (DimStance), a Stance-as-DimABSA formulation that represents stance using continuous VA scores. This approach extends ABSA beyond consumer reviews into public-issue discourse (e.g., social, political, energy, climate) and generalizes stance analysis from categorical labels to continuous emotion-based representations.

Languages

We provide data in 11 languages, including: German (deu), English (eng), Hausa (hau), Japan (jpn), Kinyarwanda (kin), Russian (rus), Swahili (swa), Tatar (tat), Twi（twi), Ukrainian (ukr), and Chinese (zho)

Subtasks

Track A – Dimensional Aspect-Based Sentiment Analysis (DimABSA): Predict real-valued valence–arousal (VA) scores for aspects and extract their associated information from text. Its subtasks include:

- Subtask 1: DimASR – Dimensional Aspect Sentiment Regression

- Subtask 2: DimASTE – Dimensional Aspect Sentiment Triplet Extraction

- Subtask 3: DimASQP – Dimensional Aspect Sentiment Quad Prediction

Track B – Dimensional Stance Analysis (DimStance): A Stance-as-DimABSA task, where the target in stance detection is treated as an aspect. Its subtasks include:

- Subtask 1: DimASR for stance analysis

Website (checkout details):

https://github.com/DimABSA/DimABSA2026

Codabench (register and submit results)

- Track A: https://www.codabench.org/competitions/10918/

- Track B: To be announced soon.

Discord (community and discussion)

https://discord.gg/xWXDWtkMzu 

Important Dates 

- Sample Data Ready: 15 July 2025

- Training Data Ready: 30 September 2025

- Evaluation Start: 10 January 2026

- Evaluation End 31 January 2026

- System Description Paper Due: February 2026

- Notification to Authors: March 2026

- Camera Ready Due: April 2026

- SemEval Workshop 2026:  co-located with ACL 2026 (San Diego, CA, USA)

We warmly invite the community to participate in this exciting shared task

and contribute to advancing NLP research.

Best regards,

SemEval-2026 Task 3 Organizers