A Survey on Automated Emotion Recognition Using Different Classification Models and Approaches

Abstract

Abstract: Automated emotion detection, is a diverse field with a multitude of applications ranging from software engineering to web customization, education, etc. Several methods and approaches have been devised for automatic emotion recognition, which has taken inspiration from human/natural emotion recognition. I have studied and discussed categorical and dimensional models, which can be subdivided into Circumplex, PANA, vector, and Plutchik's models, for defining a myriad of emotions under varied circumstances. I have stratified the approaches used in emotion detection by trifurcating them into lexicon-based, statistical, and hybrid methods. And, I have presented information on different types of classifiers and classes of neural networks that fall under the category of statistical methods, in a systematized way. I have observed that Support Vector Machines provide the most accurate and clear-cut outcome.