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The Transnational Happiness Study with Big Data Technology

Published:23 November 2020Publication History
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Editorial Notes

The editors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on February 9, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

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Abstract

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-known World 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.

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          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 1
          Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular Papers
          January 2021
          332 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/3439335
          Issue’s Table of Contents

          Copyright © 2020 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 23 November 2020
          • Revised: 1 July 2020
          • Accepted: 1 July 2020
          • Received: 1 February 2020
          Published in tallip Volume 20, Issue 1

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