This paper describes our submission to SemEval 2026 Subtask 1: Longitudinal Affect Assessment, which aims to predict continuous valence and arousal scores from chronologically ordered texts. Implement two regression based configurations built on DeBERTa fine tuning: a contextual model and a hybrid model that incorporates normalized lexical features derived from the NRC VAD lexicon. Both systems preserve temporal ordering and apply user level data splits to ensure generalization to unseen individuals. Results show competitive performance, with stronger outcomes in valence than in arousal. The integration of lexical features does not yield consistent improvements for arousal, highlighting the difficulty of modeling emotional intensity dynamics. Error analysis indicates challenges in handling implicit emotions, pragmatic ambiguity, and subtle affective shifts over time. Overall, findings underscore the importance of combining contextual representations with structured lexical knowledge while addressing longitudinal variability in emotional activation.