Our system is built on transformer encoders (BERT and DeBERTa) fine-tuned in a multi-task learning framework. For the regression subtask (evaluated with RMSE), we jointly predict Valence–Arousal (VA) scores and token-level opinion spans using a shared encoder with task-specific output heads. This formulation introduces auxiliary supervision at the token level, which stabilizes training and improves regression accuracy compared to single-task optimization.When gold abstracts and opinion annotations are provided, our models achieve strong performance. However, in fully end-to-end settings requiring automatic span extraction, performance degrades substantially due to error propagation from token-level predictions.Our findings highlight the benefits of joint affective regression and span modeling, while exposing the limitations of transformer-based sequence labeling under strict end-to-end evaluation constraints.