Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Emo-tica's Trait-State Affect Forecaster (TSAF) was developed for SemEval-2026 Task 2, focusing on modeling longitudinal affect by separating stable user traits from transient text-conditioned states, integrated through adaptive gating. On per-text prediction (Subtask 1), TSAF achieved composite Pearson correlations of 0.645 for valence and 0.409 for arousal, surpassing the Linear(BERT) baseline. Forecasting tasks revealed strong short-term affective inertia, where prior affect significantly influences immediate predictions, while long-term drift remains difficult to model under sparse supervision. TSAF demonstrated comparatively stronger performance gains for arousal in this forecasting context. Analyses across various user splits and modalities highlighted the specific strengths and trade-offs of explicit trait-state modeling, particularly in cold-start and short-text conditions.

Key takeaway

For NLP engineers developing systems for longitudinal affect prediction, TSAF's explicit trait-state decomposition offers a robust approach. You should consider this method to capture both stable user tendencies and transient textual signals, especially for improving arousal forecasting and performance in cold-start or short-text scenarios. This can lead to more accurate and nuanced affect models by addressing the complexities of dynamic emotional states.

Key insights

Decomposing affect into stable user traits and transient text-conditioned states enhances longitudinal affect modeling.

Principles

• Prior affect dominates next-step prediction in short-term forecasting.
• Long-term affective drift is challenging under sparse supervision.

Method

TSAF decomposes affect into persistent user traits and text-conditioned states, integrating them through adaptive gating for longitudinal affect modeling.

In practice

• Explicit trait-state modeling benefits cold-start conditions.
• This approach improves affect prediction for short-text scenarios.

Topics

• Longitudinal Affect Modeling
• Trait-State Affect Forecaster
• SemEval-2026
• Valence-Arousal Model
• Affective Computing
• Cold-Start Problem

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.