@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://aclanthology.org/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."
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<abstract>We present a lightweight, feature-based regression system for predicting \textbackslashtextbf{valence} (pleasantness) and \textbackslashtextbf{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.</abstract>
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%0 Conference Proceedings
%T McMaster NLP at SemEval-2026 Task 2: A Lightweight Multi-Feature System for Predicting Emotional Valence and Arousal over Time
%A Zhang, Hongyi
%A Hu, Daniel
%A Lahnala, Allison
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F zhang-etal-2026-mcmaster
%X We present a lightweight, feature-based regression system for predicting \textbackslashtextbf{valence} (pleasantness) and \textbackslashtextbf{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.
%U https://aclanthology.org/2026.semeval-1.98/
%P 686-698Markdown (Informal)
[McMaster NLP at SemEval-2026 Task 2: A Lightweight Multi-Feature System for Predicting Emotional Valence and Arousal over Time](https://aclanthology.org/2026.semeval-1.98/) (Zhang et al., SemEval 2026)
ACL