Citation

Dollmat, Khairi Shazwan and Abdullah, Nor Aniza (2022) Machine learning in emotional intelligence studies: a survey. Behaviour & Information Technology, 41 (7). pp. 1485-1502. ISSN 0144-929X

Text dollmat2021.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI’s components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, emotional intelligence, Support Vector Machine, Neural Network
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Jul 2022 01:53
Last Modified:	01 Jul 2022 01:53
URII:	http://shdl.mmu.edu.my/id/eprint/10111

Downloads

Edit (login required)