Human-Centric Intelligent Systems

Volume 1, Issue 1-2, June 2021, Pages 18 - 24

An Empirical Study of Learning Based Happiness Prediction Approaches

Authors
Miao Kong1, Lin Li1, *, Renwei Wu1, Xiaohui Tao2
1School of Computer Science and Technology, Wuhan University of Technology, China
2School of Sciences, University of Southern Queensland, Australia
*Corresponding author. Email: cathylilin@whut.edu.cn
Corresponding Author
Lin Li
Received 30 April 2021, Accepted 20 June 2021, Available Online 8 July 2021.
DOI
https://doi.org/10.2991/hcis.k.210622.001How to use a DOI?
Keywords
Happiness prediction; factor analysis; machine learning; model fusion
Abstract

In today’s society, happiness has attracted more and more attentions from researchers. It is interesting to study happiness from the perspective of data mining. In psychology domain, the application of data mining gradually becomes widespread and popular, which works from a novel data-driven viewpoint. Current researches in machine learning, especially in deep learning provide new research methods for traditional psychology research and bring new ideas. This paper presents an empirical study of learning based happiness predicition approaches and their prediction quality. Conducted on the data provided by the “China Comprehensive Social Survey (CGSS)” project, we report the experimental results of happiness prediction and explore the influencing factors of happiness. According to the four stages of factor analysis, feature engineering, model establishment and evaluation, this paper analyzes the factors affecting happiness and studies the effect of different ensembles for happiness prediction. Through experimental results, it is found that social attitudes (fairness), family variables (family capital), and individual variables (mental health, socioeconomic status, and social rank) have greater impacts on happiness than others. Moreover, among the happiness prediction models established by these five features, boosting shows the most effective in model fusion.

Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Human-Centric Intelligent Systems
Volume-Issue
1 - 1-2
Pages
18 - 24
Publication Date
2021/07/08
ISSN (Online)
2667-1336
DOI
https://doi.org/10.2991/hcis.k.210622.001How to use a DOI?
Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Miao Kong
AU  - Lin Li
AU  - Renwei Wu
AU  - Xiaohui Tao
PY  - 2021
DA  - 2021/07/08
TI  - An Empirical Study of Learning Based Happiness Prediction Approaches
JO  - Human-Centric Intelligent Systems
SP  - 18
EP  - 24
VL  - 1
IS  - 1-2
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.210622.001
DO  - https://doi.org/10.2991/hcis.k.210622.001
ID  - Kong2021
ER  -