Year: 2026
Type: conference-paper
Abstract: Dimensional Aspect-Based Sentiment Analysis models sentiment using continuous valence and arousal scores instead of discrete polarity labels, enabling fine-grained affect representation at the aspect level.SemEval 2026 Task 3 defines this setting through three subtasks covering aspect-level regression and structured extraction of aspect-opinion pairs with continuous scoring.We implement transformer-based baselines for all subtasks within a unified, reproducible framework.For aspect-level regression, we fine-tune pretrained encoders in an aspect-conditioned setup to predict valence and arousal.RoBERTa-large achieves the best development performance, with average RMSEs of 0.884 (restaurant) and 0.789 (laptop).For the structured subtasks, we adopt an instruction-based sequence-to-sequence generation approach using Flan-T5.The model generates triplets or quadruplets in a canonical textual format, thereby jointly producing aspect terms, opinion terms, valence-arousal scores, and, for Subtask 3, aspect categories.Our best model attains continuous F1 scores of 0.742 and 0.648 for triplet extraction, and 0.604 and 0.385 for quadruplet extraction on the restaurant and laptop domains, respectively.Results show that continuous aspect-level regression is relatively stable under standard fine-tuning, whereas jointly extracting structured elements and predicting continuous affect remains considerably more challenging.Our systems provide reproducible baselines under the official evaluation protocol for future work on dimensional aspect-based sentiment analysis.
Institution: Goethe University Frankfurt
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166854583