Comparison of responses to issues in SNS and Traditional Media using Text Mining -Focusing on the Termination of Korea-Japan General Security of Military Information Agreement(GSOMIA)-

텍스트 마이닝을 이용한 SNS와 언론의 이슈에 대한 반응 비교 -"한일군사정보보호협정(GSOMIA) 종료"를 중심으로-

  • Lee, Su Ryeon (Department of Information Security, Seoul Women's University) ;
  • Choi, Eun Jung (Department of Information Security, Seoul Women's University)
  • 이수련 (서울여자대학교 정보보호학과) ;
  • 최은정 (서울여자대학교 정보보호학과)
  • Received : 2019.12.17
  • Accepted : 2020.02.20
  • Published : 2020.02.28


Text mining is a representative method of big data analysis that extracts meaningful information from unstructured and large amounts of text data. Social media such as Twitter generates hundreds of thousands of data per second and acts as a one-person media that instantly and directly expresses public opinions and ideas. The traditional media are delivering informations, criticizing society, and forming public opinions. For this, we compare the responses of SNS with the responses of media on the issue of the termination of the Korea-Japan GSOMIA (General Security of Military Information Agreement), one of the domestic issues in the second half of 2019. Data collected from 201,728 tweets and 20,698 newspaper articles were analyzed by sentiment analysis, association keyword analysis, and cluster analysis. As a result, SNS tends to respond positively to this issue, and the media tends to react negatively. In association keyword analysis, SNS shows positive views on domestic issues such as "destruction, decision, we," while the media shows negative views on external issues such as "disappointment, regret, concern". SNS is faster and more powerful than media when studying or creating social trends and opinions, rather than the function of information delivery. This can complement the role of the media that reflects public perception.


Supported by : Seoul Women's University


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