Mapping Evolution of Happiness Prediction of Human with Machine Learning: A Bibliometric Analysis (2013–2023)

Happiness has consistently been the ultimate goal of humans which can improve quality of life. Various issues exist in computer science and machine learning for predicting happiness scores using prediction techniques. The ambition of this research work is to implement a bibliometric analysis of the research published in happiness prediction using Machine Learning between 2013 and 2023. The published articles were analyzed based on keywords used in the articles, year of publication, publication and document type, language of publication, author's name, affiliations and country, title of publication, and subject area. The above-mentioned information was downloaded from the SCOPUS database to get insights into publication trends. A final pool of 1187 relevant papers was obtained after compiling a pool of 1230 papers and applying a set of inclusion and exclusion criteria. It was found that the publications involving happiness prediction using Machine Learning grew from 2013 to 2023. This article reviewed a large number of research studies in the fields of Computer Science, Psychology, Medicine, Engineering, Social Sciences, Mathematics and so on, revealing that the most progressive countries are the United States followed by India and the United Kingdom. The keywords most used in Happiness prediction using Machine Learning research area are Machine Learning and Prediction, respectively.