A Framework of Human Emotion Prediction Based on a Multi-Dimensional Emotion Model

In this paper, we propose a framework of emotion prediction based on a multi-dimensional emotion model using object similarity. The proposed framework predicts the user's level of emotional response to an emotional stimulus, based on a sensitivity database that consists of self-assessment manikin-based integer-scale data, rated on various object stimulations by many users. We experimented on 72 users, with 1,073 stimulation objects, based on the International Affective Picture System, and used Thayer's arousal-valence 2-dimensional emotion model to verify the proposed framework. As a result, we have confirmed that the proposed framework can predict user emotion using an arousal-valence model.