McMaster NLP at SemEval-2026 Task 2: A Lightweight Multi-Feature System for Predicting Emotional Valence and Arousal over Time
Year: 2026
Type: conference-paper
Abstract: We present a lightweight, feature-based regression system for predicting valence (pleasantness) and 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 functionrelated 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.
Authors: Hongyi Zhang, Daniel Hu, Allison Lahnala
Institution: McMaster University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166885753