The Emotional Machine: A Machine Learning Approach to Online Prediction of User´s Emotion and Intensity

This paper explores the feasibility of equipping computers with the ability to predict, in a context of a human computer interaction, the probable user´s emotion and its intensity for a given emotion-eliciting situation. More specifically, an online framework, the Emotional Machine, is developed enabling machines to “understand” situations using the Ortony, Clore and Collins (OCC) model of emotion and to predict user´s reaction by combining refined versions of Artificial Neural Network and k Nearest Neighbors algorithms. An empirical procedure including a web-based anonymous questionnaire for data acquisition was established to provide the chosen machine learning algorithms with a consistent knowledge and to test the application´s recognition performance. Results from the empirical investigation show that the proposed Emotional Machine is capable of producing accurate predictions. Such an achievement may encourage future using of our framework for automated emotion recognition in various application fields.