An Analysis of Human Emotions by Utilizing Wavelet Features

Abstract

Human emotion recognition is critical to people managing their stress and emotions. Although many innovative techniques have been proposed to recognize human emotions, it is still challenging to understand the emotions due to individual differences in the diversity of emotions. This article focuses on analyzing the emotions computationally. In detail, a wavelet transform technique is utilized to extract significant features and find patterns in an emotion dataset. With the extracted features, both classification and visual analysis are performed. For the classification, Logistic Regression, C4.5, and Support Vector Machine are used. Visualization approaches are also utilized to represent similarities and differences among the emotion patterns. From the analysis, the authors found that the proposed method shows an improvement in identifying the differences among the emotions.

Digital society social interactions and trust analysis model

“…Emotions ( Ji, Jeong & Jeong, 2019 ) are the reasons ‘why rules/laws are to be followed and acted upon’. Emotion is not just a state that would be archived with the transformation of sense-data into a motor activity, but it is an axiomatic core of an individual that determines the value of judgment to act upon certain stimuli in the environment.…”