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A Study on Monitoring Method of Citizen Opinion based on Big Data : Focused on Gyeonggi Lacal Currency (Gyeonggi Money)

빅데이터 기반 시민의견 모니터링 방안 연구 : "경기지역화폐"를 중심으로

  • Ahn, Soon-Jae (Smart Governance Research Center, Dong-A University) ;
  • Lee, Sae-Mi (Smart Governance Research Center, Dong-A University) ;
  • Ryu, Seung-Ei (Smart Governance Research Center, Dong-A University)
  • 안순재 (동아대학교 스마트거버넌스 연구센터) ;
  • 이새미 (동아대학교 스마트거버넌스 연구센터) ;
  • 유승의 (동아대학교 스마트거버넌스 연구센터)
  • Received : 2020.06.02
  • Accepted : 2020.07.20
  • Published : 2020.07.28

Abstract

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

본 연구에서는 비정형적인 대용량의 텍스트 자료로부터 유의미한 정보를 추출하는 빅데이터 분석방법 중 텍스트 마이닝을 이용하여 시행 중인 정책과 제도에 대한 시민의견을 모니터링 할 수 있는지 확인하였다. '경기지역화폐'와 관련된 5,108건의 신문기사와 748건의 온라인 카페글을 수집하여 빈도분석, TF-IDF분석, 연관분석, 워드트리 시각화 분석을 수행하였다. 그 결과로 기사에서는 지역화폐의 도입 목적, 제공되는 혜택, 사용방법에 관련된 내용이 많았고 카페글에서는 지역화폐의 실사용과 관련된 내용 위주로 작성이 되어있음을 확인하였다. 또한 지역화폐 활성화를 위해서 뉴스는 정보전달자로서 지역화폐의 홍보에 관여하고 있었고 카페글은 지역화폐 사용자인 시민들의 의견으로 이루어져 사용과 관련된 실제적인 정보 교환의 장으로 기능하고 있었다. 지역화폐뿐만 아니라 다양한 정책과 제도에 관해서도 SNS와 텍스트 마이닝을 통해 시민들의 의견을 수렴하여 효과적으로 활성화시킬 수 있을 것으로 보인다.

Keywords

References

  1. M. M. Yoo. (2019). A Study On Types, Characteristics and Establishment of Regional Currency Movement. The Journal of Labor Studies, 39(2019.12), 131-157.
  2. J. Choi, D. Jeon & S. Yoon. (2016). A Study on the Utilization plan of the Community Currency in Gyeonggi-Do. Suwon : Gyeionggi Research Institute.
  3. H. J. Woo & Y. H. Kim. (2017). Spatial Distribution Patterns of Twitter Data with Topic Modeling. Journal of the Korean Association of Regional Geographers, 23(2), 376-387. DOI : 10.26863/jkarg.2017.05.23.2.376
  4. S. Son, D. Kim, S. Lee, M. Gil, & Y. Moon. (2017). Storm-Based Dynamic Tag Cloud for Real-Time SNS Data. KIPS Transactions on Software and Data Engineering, 6(6), 309-314. DOI : 10.3745/PKIPS.y2016m10a.47
  5. Y. Kim & H. Kang. (2016). An Analysis of Relationship Between Word Frequency in Social Network Service Data and Crime Occurences. KIPS Transactions on Computer and Communication Systems, 5(9). 229-236. DOI : 10.3745/KTCCS.2016.5.9.229
  6. S. Hong, S. Ryu & S. Ahn. (2019). Sentimental analysis on urban regeneration policy: Focused on reviews of Gamcheon culture village. Journal of the Korean Data & Information Science Society, 30(6), 1233-1244. DOI : 10.7465/jkdi.2019.30.6.1233
  7. S. Ahn, S. Ryu & S. Hong (2020). A Sentiment Analysis Model for Small-Scale Unstructured Policy Data Using Transfer Learning. Journal of the Korean Data & Information Science Society, 31(2), 405-414. DOI : 10.7465/jkdi.2020.31.2.405
  8. I. H. Kim. (2019). Newspaper Big Data and Text Mining for Digital Humanities. The Journal of Language & Literature, 78, 41-62. DOI : 10.15565/jll.2019.06.78.41
  9. S. Lee & H. Kim. (2009). Keyword Extraction from News Corpus using Modified TF-IDF. The jounal of society for e-Business Studies, 14(4), 59-73.
  10. Y. Noh, J. Lim, K. bok & J. Yoo. (2020). Hot Topic Prediction Scheme Using Modified TF-IDF in Social Network Environments. KIISE Transactions on Computing Practices, 23(4), 217-225. DOI : 10.5626/KTCP.2017.23.4.217
  11. J. H. Ryu & Y. Y. Yoo. (2018). The Fourth Industrial Recolution Core Technology Association Analysis Using Text Mining. Journal of Digital Convergence, 16(9), 129-136. DOI : 10.14400/JDC.2018.16.8.129
  12. S. H. Cha. (2007). Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Science, 1(4), 300-307.
  13. S. Lee & H. Kim. (2009). 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)-. Journal of Digital Convergence, 18(2), 277-284. DOI : 10.14400/JDC.2020.18.2.277
  14. S. Kim & J. Lee. (2016). Naver's influence on the public opinion, ahead of KBS.Chosun, Journalists Association of korea, http://www.journalist.or.kr/news/article.html?no=38363
  15. M. Kobayashi and K. Takeda. (2000). Information Retrieval on the Web. ACM Computing Serveys, 32(2), 144-173 DOI : 10.1145/358923.358934