hllwan at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis via LLM Feature Fusion and Test-Time Adaptation

AbstractThis paper describes the system developed bythe team for SemEval-2026 Task 3: Di-mensional Aspect-Based Sentiment Analysis(DimABSA). Unlike traditional categorical sen-timent analysis, predicting continuous Valenceand Arousal (VA) scores across multiple lan-guages and domains poses significant theoret-ical and engineering challenges. To systemat-ically address data scarcity and cross-domaindistribution shifts, we propose a highly robustframework. First, we implement a translation-based data augmentation strategy with preciseHTML-tag alignment to mitigate low-resourceconstraints. Second, we introduce an unsuper-vised opinion extraction module based on syn-tactic dependency parsing to explicitly capturesentiment-bearing words. Third, we designa Tripartite Feature Fusion architecture builtupon both encoder-only (DeBERTa-v3) andcausal LLM (Qwen2.5) models to dynamicallyaggregate global and localized aspect-opinionembeddings. Finally, we apply an unsupervisedTest-Time Adaptation (TTA) mechanism to cal-ibrate normalization layers on the fly. Our sys-tem demonstrates highly competitive perfor-mance while offering critical insights into thelimitations of LLMs in cross-lingual sentimenttransfer.