Investigation of Behavioral Characteristic Parameters Intended for Emotional State Classification

In the present, we can use more than 200 different methods and algorithms for automatic recognition systems, the basis of which is the recognition of a face in an image, subsequent extraction of areas of interest and classification of individual parameters using neural networks or other classifications. Currently, one of the most discussed research topics connecting the fields of psychology and artificial intelligence is the classification of emotions from behavioral characteristics. We created the Emotnizer application for collecting and processing behavioral characteristics. The input characteristics record 24 different ways of user behavior when working with a mouse and keyboard, e.g., when rewriting text (determining the method of clicking / key pressing speed, cursor changes, errors in the text, etc.). In the article, we analyze the obtained parameters using the decision tree method and find out whether it is possible to successfully classify the emotional state using these parameters. We found that for the successful classification of emotional states using behavioral characteristics, it is appropriate to classify mainly emotional states with a high value of valence and arousal.