An Empirical Study of Learning Based Happiness Prediction Approaches

Kong, Miao and Li, Lin and Wu, Renwei and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X (2021) An Empirical Study of Learning Based Happiness Prediction Approaches. Human-Centric Intelligent Systems, 1 (1-2). pp. 18-24.

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 24 Feb 2022 23:40
Last Modified: 30 Mar 2022 04:27
Uncontrolled Keywords: Happiness prediction; factor analysis; machine learning; model fusion
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
11 Medical and Health Sciences > 1117 Public Health and Health Services > 111714 Mental Health
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4608 Human-centred computing > 460899 Human-centred computing not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
E Expanding Knowledge > 97 Expanding Knowledge > 970117 Expanding Knowledge in Psychology and Cognitive Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.2991/hcis.k.210622.001
URI: http://eprints.usq.edu.au/id/eprint/46207

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