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http://dx.doi.org/10.14400/JDC.2021.19.11.001

Analysis of Public Perception and Policy Implications of Foreign Workers through Social Big Data analysis  

Ha, Jae-Been (Business Analytics and Information Systems, Ajou University)
Lee, Do-Eun (Korea University Legal Research Institute)
Publication Information
Journal of Digital Convergence / v.19, no.11, 2021 , pp. 1-10 More about this Journal
Abstract
This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.
Keywords
Foreign Worker; Social Big Data Analysis; Text Mining; TF-IDF; Degree Centrality; Semantic Network Analysis;
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