@inproceedings{zhang-etal-2026-mcmaster,
    title = "{M}c{M}aster {NLP} at {S}em{E}val-2026 Task 2: A Lightweight Multi-Feature System for Predicting Emotional Valence and Arousal over Time",
    author = "Zhang, Hongyi  and
      Hu, Daniel  and
      Lahnala, Allison",
    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-1.98/",
    pages = "686--698",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a lightweight, feature-based regression system for predicting {\textbackslash}textbf{\{}valence{\}} (pleasantness) and {\textbackslash}textbf{\{}arousal{\}} (activation) from longitudinal language data. The language data ranges from longer free-form ecological essays to short affect-word, organized by user and time, reflecting natural variation in affective expression and experience. Our approach combines three complementary signals: (i) sentence-level semantic embeddings, (ii) psycholinguistic category features capturing affect- and function-related word usage, (iii) similarity measures between the language data with archetypal sentences, and (iv) trainable user-embeddings to account for between-user differences. The resulting feature vector is passed to a multi-layer perceptron trained to jointly predict valence and arousal. Our design provides a strong and interpretable baseline by making it possible to isolate the contribution of semantic, psycholinguistic, similarity, and user-specific signals. We further analyze our model{'}s predictions to identify which feature groups are most informative and where errors are concentrated across users and input types."
}