Discovery of Human Emotion using Fuzzy based Cognitive Model

The emotion of a human could be identified using Speech, Image and Question and answer session. Also, the emotion in speech is identified using the pitch and intensity. The emotion identification with image is done using Support Vector Machine. The present chapter envisages into an intelligent system, which is designed to understand human emotions more precisely speech emotion identification and intends to generate actions via cognitive system. It has mainly focused on developing an online incremental learning system of human emotions using Takagi-Sugeno (TS) Fuzzy model. The main objective of this system is to detect whether the observed emotion needs a new corresponding multi-model action to be generated or it can be attributed to one of the existing actions in memory. The multi-model consists of voice input, facial expression. The combined results have been classified using TS Fuzzy Model.

OKO-SVM: Online kernel optimization-based support vector machine for the incremental learning and classification of the sentiments in the train reviews

Online incremental learning is one of the emerging research interests among the researchers in the recent years. The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews. This work has introduced an online incremental learning algorithm for classifying the train reviews. The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service. This work proposes the online kernel optimization-based support vector machine (OKO-SVM) classifier for the sentiment classification of the train reviews. This paper is the extension of the previous work kernel optimization-based support vector machine (KO-SVM). The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time duration. The simulation uses the standard train review and the movie review database for the classification. From the simulation results, it is evident that the proposed model has achieved a better performance with the values of 84.42%, 93.86%, and 74.56% regarding the accuracy, sensitivity, and specificity while classifying the train review database.