Dimensional Aspect-Based Sentiment Analysis models sentiment using continuous valence and arousal scores instead of discrete polaritylabels, enabling fine-grained affect representation at the aspect level. SemEval 2026 Task3 defines this setting through three subtaskscovering aspect-level regression and structuredextraction of aspect–opinion pairs with continuous scoring. We implement transformer-basedbaselines for all subtasks within a unified, reproducible framework. For aspect-level regression, we fine-tune pretrained encoders in anaspect-conditioned setup to predict valence and arousal. RoBERTa-large achieves the best development performance, with average RMSEsof 0.884 (restaurant) and 0.789 (laptop).