Modeling Emotions and Impressions Embedded in Gait Onomatopoeia Using Character-Level Features

Abstract

This study proposes a method for modeling the emotional and impression-related attributes of Japanese gait onomatopoeia based on character-level features. Fourteen representative gait-related onomatopoeic expressions were evaluated on six basic emotions and four impression-related dimensions through human annotation. Using these scores as supervised training data, a regression model employing a Character-level Convolutional Neural Network (Char-CNN) was trained. The model demonstrated the capability to predict emotional and impression values not only for known onomatopoeia but also for previously unseen ones. The results suggest that the structural features of onomatopoeic expressions contribute to impression formation and emotional perception.