Emotiono: an ontology with rule-based reasoning for emotion recognition

Authors:
Xiaowei Zhang;Bin Hu;Philip Moore;Jing Chen;Lin Zhou
Affiliations:
The School of Information Science and Engineering, Lanzhou University, Lanzhou, China;The School of Information Science and Engineering, Lanzhou University, Lanzhou, China;The School of Computing, Telecommunications and Networks, Birmingham City University, Birmingham, UK;The School of Information Science and Engineering, Lanzhou University, Lanzhou, China;The School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

Recently, the field of automatic recognition of users' affective states has gained a great deal of attention. Automatic, implicit recognition of affective states has many applications, ranging from personalized content recommendation to automatic tutoring systems. In this work, we propose an ontology called ‘Emotiono' for the robust recognition of emotions through Electroencephalogram (EEG). In ‘Emotiono', we define entities such as users' emotions, EEG features and their relationships. With inference rules obtained by Decision Tree algorithm, users' current emotional state can be reasoned based on their EEG data. We implement ‘Emotiono' in Protégé 4.1 and evaluate its performance with EEG data gathered from the eNTERFACE06_EMOBRAIN Database. Using a 9-fold cross validation method for training and testing, ‘Emotiono' reaches an average classification rate of 97.80% for recognizing 5 subjects' emotional states.