Emotion Recognition and Soft Skills Prediction: A Bibliometric Exploration

Abstract

This study, based on a 25-year dataset (2000–2025) collected from the Scopus database, provides a comprehensive bibliometric analysis of the intellectual structure of research on emotion recognition, prediction, and skills. Using bibliographic coupling as the primary method, the analysis examines the titles, abstracts, keywords, frameworks, and review literature, presenting the most significant articles in this area, along with the headings of 202 relevant papers. The study investigates the temporal distribution of research outputs, focusing particularly on trends from the last decade. To visualize the scientific landscape, the study uses VOSviewer to map co-authorship, keyword co-occurrence, and citation networks. The analysis highlights the most prolific journals, influential authors, dominant subject areas, and frequently used keywords. Additionally, it identifies the algorithms used for emotion recognition in predicting soft skills, along with the objectives of the studies, as well as the data and results involved. The study also identifies the leading countries and educational institutions contributing to this research domain. The findings offer a detailed overview of the field’s development and intellectual trends, providing insights and recommendations for future research directions. This research also helps to understand how emotion recognition can contribute to human development across various domains, as discussed in this article.