Feature-Based Understanding of Human Emotions

Since human emotion recognition is considered as one of the priority research topics in academia and industries to help people manage their stress and emotions, many significant research studies have been performed by proposing innovative techniques to recognize emotions. However, it is still difficult to understand the emotions. In this paper, we focused on analyzing the emotions computationally. In detail, a wavelet transform technique is utilized to extract significant features to find patterns in an emotion dataset. With the features, both classification and visual analysis are performed. For the classification, Logistic Regression, C4.5, and Support Vector Machine are used. Visualization techniques are utilized to show the similarity and difference among the emotion patterns. From the analysis, we found that there is an improvement in identifying the difference among the emotions.