A Review of Machine Learning Applications in Assessing Emotional Intelligence across Social and Professional Domain

Emotional Intelligence (EI) has emerged as a critical determinant of success in both social interactions and professional environments. Recent advancements in Machine Learning (ML) and Data Mining have enabled researchers to move beyond traditional psychometric analysis toward predictive behavioral modeling. Some of the algorithms implemented according to specific research questions that were applied, were the classification algorithms (ID3 and J48) for the production of decision trees, regarding the four more general factors (welfare, self-control, emotionality and sociability) and in overall emotional intelligence. The results obtained, after weighing and criteria basis, present consumers' rates, which in turn analyze the degree of emotional intelligence. This review examines how ML techniques like decision trees and clustering are used to evaluate EI in diverse contexts, ranging from young adult social media users to professional employees. This paper reviews existing literature on the application of classification and clustering algorithms in evaluating EI among social network consumers and corporate employees. By synthesizing these findings, we propose a new research objective: the development of a unified ML framework to predict how EI traits developed in social networking environments translate to professional performance and work-life balance.