지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.10
- 수록면
- 45 - 62 (18page)
- DOI
- 10.37272/JIECR.2022.10.22.5.45
이용수
초록· 키워드
오류제보하기
Newspaper articles are text data that record major issues or events that occur in the society we live in. Analyzing newspaper article text can derive interesting patterns of social phenomena. In this paper, topic modeling analysis was conducted by dividing the text of COVID-19-related newspaper articles by article writing period and newspaper type, and its meaning and implications were derived from a social and technical perspective.
Texts of COVID-19-related articles were collected from Bigkinds, which provides news articles from various newspapers published in Korea, basic text analysis and major topics were extracted to examine major issues and countermeasures of COVID-19. The extracted topics were divided into the article writing period (2021 and 2022) and the newspaper type (national and local newspapers), and some implications were derived accordingly. First, the main topics of COVID-19-related news articles were "political support", "social response", "corona quarantine", and "overseas situation". Second, the "policy support" topic was widely covered in national newspapers in 2022 due to the influence of the 2022 presidential election. Third, it can be seen that local governments are more interested in support and consideration for the socially disadvantaged than the central government, and that they were also more active ahead of the presidential election. Fourth, the "COVID-19 Quarantine" topic was more covered in local newspapers than in national newspapers. Fifth, from a technical point of view, it was expected that performing T-verification as a method of measuring the accuracy of the extracted topics could be a good technical method for optimizing topic modeling.
Texts of COVID-19-related articles were collected from Bigkinds, which provides news articles from various newspapers published in Korea, basic text analysis and major topics were extracted to examine major issues and countermeasures of COVID-19. The extracted topics were divided into the article writing period (2021 and 2022) and the newspaper type (national and local newspapers), and some implications were derived accordingly. First, the main topics of COVID-19-related news articles were "political support", "social response", "corona quarantine", and "overseas situation". Second, the "policy support" topic was widely covered in national newspapers in 2022 due to the influence of the 2022 presidential election. Third, it can be seen that local governments are more interested in support and consideration for the socially disadvantaged than the central government, and that they were also more active ahead of the presidential election. Fourth, the "COVID-19 Quarantine" topic was more covered in local newspapers than in national newspapers. Fifth, from a technical point of view, it was expected that performing T-verification as a method of measuring the accuracy of the extracted topics could be a good technical method for optimizing topic modeling.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Literature Review
Ⅲ. Research Design
Ⅳ. Data Analysis
V. Conclusion
References
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