Predicting individual sentiment for emotion-evoking pictures using metrics of oculo-motors

Relationships between features of oculo-motors and perceptual impressions of Valence and Arousal are analysed using viewer's reactions to 67 emotion-evoking photographs. Individual rating scores are compensated for using item response theory, and chronological changes of oculo-motor indices are analysed in response to two-dimensional ratings. These reactions are summarised as regression models, and predicted emotional categories based on oculo-motor reactions are evaluated. Prediction performance is also evaluated using mean similarities for the predicted categories of emotion. While performance improved when these features were added, individual reactions to features should be included in order to improve prediction performance for each participant. Also, temporal features of oculo-motors for Valence and Arousal are selected independently of their contribution to prediction.