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.

摘要

研究证明，拥有高水平的情商（EI）可以减少患精神病的机会。EI及其组件可以通过培训进行改进，但是目前该过程灵活性较差且非常耗时。另一方面，机器学习（ML）可以分析大量数据，以在尽可能短的时间内发现有用的趋势和模式。尽管有这些好处，但在EI训练中使用ML的情况仍然很少。在本文中，我们研究了92篇期刊文章，以发现EI及其组件研究中ML利用的趋势。这项调查旨在为可能导致在EI培训中实施ML的未来研究铺平道路，并吸引心理学和计算机科学领域的研究人员寻找针对每个EI组件采用通用ML算法的可能性。我们的发现表明，将ML应用于EI组件的趋势正在增加，并且支持向量机和神经网络是这些研究中使用的两种最受欢迎​​的ML算法。我们还发现，社交技能和同理心是ML暴露最少的EI组件。最后，我们为EI领域的ML和ML领域的EI的未来研究方向提供了建议。