@inproceedings{chen-etal-2026-csiro,
    title = "{CSIRO}-{LT} at {S}em{E}val-2026 Task 2: In-the-Wild Valence and Arousal Forecasting on Ecological Text Time Series",
    author = {Chen, Jiyu  and
      B{\"o}l{\"u}c{\"u}, Necva  and
      Karimi, Sarvnaz  and
      Molla, Diego  and
      Paris, Cecile},
    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-1.24/",
    pages = "160--166",
    ISBN = "979-8-89176-414-9",
    abstract = "Predicting emotional valence and arousal in text is challenging due to the continuous, dynamic, and context-dependent nature of emotions. The SemEval 2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays shared task investigates longitudinal affect prediction from real-world personal essays, including forecasting short-term state and longer-term dispositional changes. We compare Pre-trained Language Models (PLMs) and Large Language Models (LLMs) for these subtasks, examining different input representations and feature formulations. We show that sentiment-aware PLMs are most effective for continuous valence and arousal prediction, and LLMs are effective for short-term state forecasting. Modelling dispositional changes remains challenging, and none of our neural approaches surpass simple a historical baseline approach in this setting."
}