Overview
SemEval-2026 Task 3 invites participants to engage in a shared task centered on Dimensional Aspect-Based Sentiment Analysis (DimABSA) and Dimensional Stance Detection. This initiative aims to enhance the understanding of sentiments expressed in customer reviews and stance datasets by integrating dimensional sentiment analysis into the traditional aspect-based sentiment analysis framework. This task is significant for the AI and ML community as it pushes the boundaries of sentiment analysis beyond simple categorical labels, allowing for a more nuanced interpretation of emotional expressions.
Background & Relevance
Aspect-Based Sentiment Analysis (ABSA) has been a vital tool in understanding opinions at a granular level. Traditionally, ABSA has relied on broad sentiment categories such as positive, negative, and neutral. However, this approach does not align with the more sophisticated frameworks established in psychology and affective science, which advocate for a representation of sentiment along continuous dimensions of valence and arousal. The introduction of DimABSA seeks to bridge this gap, offering a more detailed analysis of sentiments that can be applied across various domains, including consumer feedback and public discourse.
Key Details
- 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 in San Diego, CA, USA
- Website: DimABSA2026
- Codabench for Track A: Codabench Track A
- Discord Community: Join here
Eligibility & Participation
This task is open to researchers, practitioners, and students interested in advancing the field of sentiment analysis and stance detection. Participants from diverse backgrounds are encouraged to contribute, fostering a collaborative environment for knowledge sharing and innovation.
Submission or Application Guidelines
- Register for the competition on Codabench.
- Access the sample and training data as they become available.
- Develop your models for both DimABSA and DimStance tasks.
- Submit your results during the evaluation period.
- Prepare a system description paper to be submitted by the deadline.
More Information
The integration of dimensional sentiment analysis into ABSA represents a significant advancement in natural language processing. By allowing for continuous representations of sentiment, this task not only enhances the granularity of sentiment analysis but also broadens its applicability to various fields, including social and political discourse. This shared task is a valuable opportunity for researchers to contribute to the evolving landscape of NLP and sentiment analysis.
Conclusion
SemEval-2026 Task 3 represents an exciting opportunity for the community to engage with cutting-edge research in sentiment analysis and stance detection. Researchers and practitioners are encouraged to participate, share their findings, and contribute to the advancement of this important area in AI and ML. Explore the provided links for more details and join the discussion in the community.
Category: CFP & Deadlines
Tags: sentiment analysis, stance detection, natural language processing, dimensional analysis, aspect-based sentiment analysis, machine learning, NLP, SemEval, ACL, evaluation, data science, emotion recognition, multilingual analysis
