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