@inproceedings{noor-fatima-2026-emo,
    title = "Emo-tica at {S}em{E}val-2026 Task 2: Trait{--}State Affect Forecaster for Longitudinal Valence and Arousal",
    author = "Noor, Sadia  and
      Fatima, Mehwish",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.31/",
    pages = "213--220",
    ISBN = "979-8-89176-414-9",
    abstract = "Modeling longitudinal affect requires capturing both stable user tendencies and transient textual signals. For SemEval-2026 Task 2, we propose the Trait-State Affect Forecaster (TSAF), which decomposes affect into persistent user traits and text-conditioned states integrated through adaptive gating. On per-text prediction (Subtask 1), TSAF achieves composite Pearson correlations of 0.645 for valence and 0.409 for arousal, outperforming the Linear(BERT) baseline. In forecasting tasks, results reveal strong short-term affective inertia, where prior affect dominates next-step prediction, while long-term drift remains challenging under sparse supervision; TSAF shows comparatively stronger gains for arousal in this setting. Analyses across user splits and modalities highlight the strengths and trade-offs of explicit trait-state modeling, particularly under cold-start and short-text conditions."
}