UNF-BMI at SemEval-2026 Task 3: Research Domain Criteria-Guided Large Language Models for Dimensional Aspect-Based Sentiment Analysis

AbstractWe present UNF-BMI system for SemEval-2026 Task 3, Track A, Subtask 1 (Dimensional Aspect Sentiment Regression, DimASR), which focuses on predicting continuous Valence–Arousal (VA) scores for aspects in text. Our approach integrates psychologically grounded affective signals inspired by the Research Domain Criteria (RDoC) framework. We investigate two complementary methods: first, an in-context learning framework using Mistral-7B-Instruct with semantically retrieved few-shot examples augmented by lexicon-derived RDoC valence and arousal cues; second, a supervised multi-task learning model based on RoBERTa, where VA regression is the primary objective and RDoC-based positive/negative signal prediction serves as an auxiliary task to regularize shared representations. Experiments on english laptop and restaurant review datasets demonstrate that incorporating RDoC-inspired affective priors reduces RMSE compared to baselines, particularly in low-signal text where explicit sentiment cues are sparse.