The Transnational Happiness Study with Big Data Technology

被引:6
作者
Peng, Lingxi [1 ]
Liu, Haohuai [2 ]
Nie, Yangang [3 ]
Xie, Ying [3 ]
Tang, Xuan [4 ]
Luo, Ping [4 ]
机构
[1] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Peoples R China
[2] Guangzhou Univ, Sch Chem, Guangzhou 510006, Peoples R China
[3] Guangzhou Univ, Sociol Dept, Guangzhou 510006, Peoples R China
[4] Guangzhou Univ, Sch Econ & Stat, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
Big Data; happiness; decision tree; feature selection;
D O I
10.1145/3412497
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Happiness is a hot topic in academic circles. The study of happiness involves many disciplines, such as philosophy, psychology, sociology, and economics. However, there are few studies on the quantitative analysis of the factors affecting happiness. In this article, we used the well-knownWorld Values Survey Wave 6 (WV6) dataset to quantitatively analyze the happiness of 57 countries with Big Data techniques. First, we obtained the seven most important factors by constructing happiness decision trees for each country. Calculating the frequencies of these factors, we obtained the 17 most important indicators for the prediction of happiness in the world. Then, we selected five representative countries, namely, Sweden, Japan, India, China, and the USA, and analyzed the indicators with the random forest method. We identified different patterns of factors that influence happiness in different countries. This study is a successful attempt to apply data mining technology in the social sciences, and the results are of practical significance.
引用
收藏
页数:12
相关论文
共 26 条
[1]  
Algan Yann, 2016, OECD Statistics Working Papers, V4, P6, DOI [10.1787/18152031, DOI 10.1787/18152031]
[2]  
[Anonymous], 1993, STUDIES SOCIOCULTURA
[3]  
Argyle M., 1999, WELL BEING FDN HEDON, P353
[4]   Big Data: A Survey [J].
Chen, Min ;
Mao, Shiwen ;
Liu, Yunhao .
MOBILE NETWORKS & APPLICATIONS, 2014, 19 (02) :171-209
[5]   An intelligent aerator algorithm inspired-by deep learning [J].
Deng, Hongjie ;
Peng, Lingxi ;
Zhang, Jiajing ;
Tang, Chunming ;
Fang, Haoliang ;
Liu, Haohuai .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (04) :2990-3002
[6]   SUBJECTIVE WELL-BEING [J].
DIENER, E .
PSYCHOLOGICAL BULLETIN, 1984, 95 (03) :542-575
[7]   A Comparison of Four Approaches to Discretization Based on Entropy [J].
Grzymala-Busse, Jerzy W. ;
Mroczek, Teresa .
ENTROPY, 2016, 18 (03)
[8]   Relative Income, Relative Assets, and Happiness in Urban China [J].
Huang, Jin ;
Wu, Shiyou ;
Deng, Suo .
SOCIAL INDICATORS RESEARCH, 2016, 126 (03) :971-985
[9]   A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest [J].
Huang, Nantian ;
Lu, Guobo ;
Xu, Dianguo .
ENERGIES, 2016, 9 (10)
[10]   Random forest for ordinal responses: Prediction and variable selection [J].
Janitza, Silke ;
Tutz, Gerhard ;
Boulesteix, Anne-Laure .
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2016, 96 :57-73