This paper describes our system for SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal. We approached the task by fine-tuning a weighted ensemble of DeBERTa-v3-base models. Our system achieved the second-highest Valence composite correlation and ranked 5th in the overall V&A average in Subtask 1. More importantly, we provide an empirical analysis of our model’s performance on longitudinal tasks, where it exhibited significant inverse cor- relations. We quantify the Venting Effect, showing a systematic tendency for the model to over-index on negative lexical cues despite self-reported relief. Furthermore, we analyze the structural trade-off between Mean Absolute Error and Pearson correlation induced by smoothing techniques.