A Study of Factors Influencing Happiness in Korea: Topic Modelling and Neural Network Analysis

Ji-Hyun  Jang; Nemoto Masatsuku

doi:10.56294/dm2024238

Authors

Ji-Hyun Jang Associate Professor, College of Liberal Arts Sangmyung University, Cheonan, Korea Author https://orcid.org/0009-0009-5622-9158
Nemoto Masatsuku Research Professor, Chungbuk National University, Cheongju, Korea Author https://orcid.org/0000-0003-4967-2257

DOI:

https://doi.org/10.56294/dm2024238

Keywords:

Happiness Research, Topic Modelling, Machine Learning

Abstract

The aim of this study is to derive the important factors that influence levels of happiness in Korea, and to identify which factors are particularly important among these influencing factors. To achieve this goal, topic modelling analysis, machine learning analysis and neural network analysis methods were utilized. The Netminer 4.5 program was used for topic modelling analysis and machine learning analysis, and SPSS MODELER 18 was used to perform neural network analysis. Two types of analysis data were used in this study. The first consisted of 1 000 papers relating to happiness published in academic journals managed by the Springer publishing company, which were used to derive happiness-influencing factors. The second consisted of a survey conducted in 2020 by the Community Well-being Center of the Graduate School of Public Administration at Seoul National University in Korea. A total of 16 655 people responded to this survey. The analysis results of the study are as follows. Important variables that affect the level of happiness of Korean residents are: family life, social status, income, health, and perceptions of inequality. Analysis using neural network analysis of the most important factors influencing happiness showed that satisfaction with family life had the most important influence. This suggests that policies that can improve the quality of family life, such as family-friendly work environments, childcare support, and domestic violence prevention and response programmes, will become important in the future

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