2020년 소셜 빅데이터 기반 보건복지 이슈 동향 분석

Abstract

The health and social welfare sector is changing from a provider-oriented to a consumer-oriented customized service system. It is important to use social big data to readily recognize these changes and to identify issues in the health and welfare sector. The use of social big data is on increasing demand as big data analytics has improved rapidly and an ever-growing amount of unstructured big data is available for collection and analysis. In this study, documents were collected monthly and looked at various analysis results to find out what topics are being issued in documents related to “health,” “welfare,” and “social security,” the main keywords in the health and welfare sectors. We also elaborate on the relevant methodologies to extend the utilization of unstructured big data. In this work, we conducted a theoretical review for expanding the utilization of unstructured big data, focusing on embedding methodologies. We examine linkage methods based on embedding methodology, and describe the methodologies of canonical correlation analysis and representation learning and upgrading that are utilized in linkage methodology. Social big data analysis can serve as an important competitive edge in understanding the current situation of issues of national and social interest in the area of ​​health and welfare policy.