Interweaving deep learning and semantic techniques for emotion analysis in human-machine interaction

This paper presents a new data classification approach which is based on the one hand on deep learning neural networks for effectively extracting well defined categorical information from data and on the other hand on an adaptable support vector machine, which appropriately represents existing related knowledge about user and context specific data. The proposed approach is implemented and successfully tested experimentally for emotion analysis in human machine interaction.