Categorization of Emotion Based on Causality

Abstract

Background: Emotions come in all shapes and forms. Some of them can be external, visible, and clearly comprehensible, while others can seemingly be coming out of thin air. Knowing what causes an emotion is crucial for better therapy and mental health. Therefore, in this manuscript, we address the problem of emotions causality. Methods: We propose a comparison of three traditional clustering models: Gaussian mixture model, HDBSCAN, and fuzzy c-means, to categorize each emotion described in the DEAP database. It contains over 1700 points, and has no prior label as to which type of stressor the subject’s emotion is generated from. This labelling task has been conducted by a psychiatrist. Results: The fuzzy c-means yields the highest results, with an accuracy of 57.13%, followed by the Gaussian mixture model at 39.49% and the HDBSCAN method with only 18.86%. Another score computed is the mutual information score which shows how homogenous the clusters are for each model. Conclusion: The data from DEAP is very heterogeneous and its density is stable, which may indicate that classification would be the better option, in terms of accuracy and homogeneity of the clusters.