Integrating Emotional Intelligence Assessment with Sentiment Analysis Using Explainable Machine Learning for Predicting the Developmental Potential of Children: A Literature Review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: Purpose: Within the current literature, the writer’s objective in this literature review is to examine the integration of the analysis of sentiment with the measurement of Emotional Intelligence (EI) utilizing explainable machine learning in predicting the development of children. The current research on the measurement of EI, sentiment analysis, and the utilization of machine learning in child development prediction is focused on in this research study. Methodology: The reviewed studies were released between 1990 and 2021 and focused on the intersection of EI assessment, sentiment analysis, and explainable AI in child development research. Relevant peer-reviewed articles were systematically gathered from databases such as IEEE, Wiley, ScienceDirect, MDPI, and others to identify relevant and credible literature. Various machine learning and deep learning methods were predominantly used in the studies to analyze emotional and cognitive growth in children based on sentiment and behavioral data. Findings/Result: Recent advancements in sentiment analysis and machine learning have significantly contributed to understanding children's developmental potential. However, existing studies reveal research gaps in integrating EI assessment with sentiment analysis through explainable AI models. In any current approaches, although lack transparency, interpretability, and flexibility, both of which are essential for accurately predicting developmental outcomes. Additionally, although deep learning models improve prediction accuracy, they frequently have interpretability issues, which makes it difficult to use them practically in developmental psychology. Originality: In order to develop a systematic framework for further research, this review synthesizes findings from a variety of studies that are accessible through internet sources It highlights the potential of integrating EI assessment with sentiment analysis and explainable machine learning, aiming to bridge existing research gaps and provide a foundation for further exploration. Paper Type: Literature Review.
Authors: Shyamala B, P. Sridhara Acharya
Institution: Srinivas University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412812217