mlsanityData to 7 Oct 2026

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

arXiv 2604.07066 ↗First posted 8 Apr 2026cs.CLcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 94 references that are in the map. First posted on arXiv 8 Apr 2026.

Builds on

24 of 94 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2025Qwen3 Technical Reportcited by 8,195
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  5. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  6. 2019Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysiscited by 123
  7. 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 99
  8. 2020Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representationscited by 97
  9. 2020Position-Aware Tagging for Aspect Sentiment Triplet Extractioncited by 90
  10. 2022EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysiscited by 75
  11. 2016Stance and Sentiment in Tweetscited by 62
  12. 2020X-Stance: A Multilingual Multi-Target Dataset for Stance Detectioncited by 42
12 more
  1. 2017Emotion Intensities in Tweetscited by 32
  2. 2022Best Practices in the Creation and Use of Emotion Lexiconscited by 27
  3. 2019SemEval-2015 Task 10: Sentiment Analysis in Twittercited by 16
  4. 2020Multilingual Stance Detection: The Catalonia Independence Corpuscited by 16
  5. 2023A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trendscited by 13
  6. 2025Zero-Shot Conversational Stance Detection: Dataset and Approachescited by 11
  7. 2025M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysiscited by 9
  8. 2025EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Taskscited by 5
  9. 2025What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discoursecited by 3
  10. 2026AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysiscited by 1
  11. 2026QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysiscited by 1
  12. 2026LogSigma at SemEval-2026 Task 3: Uncertainty-Weighted Multitask Learning for Dimensional Aspect-Based Sentiment Analysiscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regressioncited by 1
  2. 2026NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regressioncited by 0
  3. 2026The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysiscited by 0
  4. 2026Decide, Don't Generate: Competitive Dimensional ABSA with Jev's Typed Decisionscited by 0