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

arXiv: https://arxiv.org/abs/2604.07066 · HTML: https://mlsanity.com/paper/2604.07066/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 94 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis](https://mlsanity.com/paper/2205.01996/) (2022)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://mlsanity.com/paper/2003.08385/) (2020)
- [Emotion Intensities in Tweets](https://mlsanity.com/paper/1708.03696/) (2017)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)
- [Multilingual Stance Detection: The Catalonia Independence Corpus](https://mlsanity.com/paper/2004.00050/) (2020)
- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [Zero-Shot Conversational Stance Detection: Dataset and Approaches](https://mlsanity.com/paper/2506.17693/) (2025)
- [M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2502.11824/) (2025)
- [EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks](https://mlsanity.com/paper/2508.17008/) (2025)
- [What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse](https://mlsanity.com/paper/2505.16592/) (2025)
- [AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.04933/) (2026)
- [QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.07766/) (2026)
- [LogSigma at SemEval-2026 Task 3: Uncertainty-Weighted Multitask Learning for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.24896/) (2026)

## Built on by (most cited first, 4 of 4)

- [ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression](https://mlsanity.com/paper/2605.10560/) (2026)
- [NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression](https://mlsanity.com/paper/2604.08923/) (2026)
- [The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2607.03414/) (2026)
- [Decide, Don't Generate: Competitive Dimensional ABSA with Jev's Typed Decisions](https://mlsanity.com/paper/2609.35293/) (2026)

## Abstract (arXiv)

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.