This paper studies the emotion as affective circumplex model representing valence and arousal in continuous two dimensional space. It also explores the disposition of emotion over time to identify the behavioural cues and self-identified affective states. while traditional methods use categorical emotion classes, SemEval 2026 Task 2 studies emotions in continuous space. In this paper, we proposes a transformer-based LongVA-RoBERTa model for emotion modeling in regression for ecological essays. For subtask 1 , we develop an affect prediction framework employing RoBERTa with attention pooling and a regression head for valence and arousal prediction. In subtask 2A , we employ BiLSTM to capture the temporal dependencies and fuse surface, contextual, user-level features to predict short-term affect variation. Our results outperform the baseline, paving ways to continue emotion prediction in continuous dimensional space

We study the problem of assessing the clarity of political question–answer pairs, where the goal is to determine whether a response directly addresses the question, avoids it, or remains ambiguous. This task is particularly challenging in political discourse, where evasiveness can be subtle and context-dependent.To tackle this problem, we propose an ensemble-based approach built on the transformer-based model DeBERTa-v3-base, fine-tuned on concatenated question–answer inputs. Special attention is given to class imbalance during training to ensure robust performance across all categories.To better capture uncertainty in borderline cases, we train multiple models with different random seeds and employ Monte Carlo Dropout at inference time. Final predictions are obtained by averaging logits across ensemble models and stochastic forward passes, yielding more stable and robust predictions.Our system achieves a Macro-F1 score of 0.76 on the evaluation dataset. Error analysis reveals that responses that partially engage with the question while failing to provide a direct answer remain the most challenging, highlighting the inherent difficulty of detecting nuanced evasiveness in political communication.