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http://dx.doi.org/10.7319/kogsis.2017.25.1.037

Citizen Sentiment Analysis of the Social Disaster by Using Opinion Mining  

Seo, Min Song (Dept. of Urban engineering, Gyeongsang National University)
Yoo, Hwan Hee (Dept. of Urban engineering, Gyeongsang National University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.25, no.1, 2017 , pp. 37-46 More about this Journal
Abstract
Recently, disaster caused by social factors is frequently occurring in Korea. Prediction about what crisis could happen is difficult, raising the citizen's concern. In this study, we developed a program to acquire tweet data by applying Python language based Tweepy plug-in, regarding social disasters such as 'Nonspecific motive crimes' and 'Oxy' products. These data were used to evaluate psychological trauma and anxiety of citizens through the text clustering analysis and the opinion mining analysis of the R Studio program after natural language processing. In the analysis of the 'Oxy' case, the accident of Sewol ferry, the continual sale of Oxy products of the Oxy had the highest similarity and 'Nonspecific motive crimes', the coping measures of the government against unexpected incidents such as the 'incident' of the screen door, the accident of Sewol ferry and 'Nonspecific motive crime' due to misogyny in Busan, had the highest similarity. In addition, the average index of the Citizens sentiment score in Nonspecific motive crimes was more negative than that in the Oxy case by 11.61%p. Therefore, it is expected that the findings will be utilized to predict the mental health of citizens to prevent future accidents.
Keywords
Tweet Data; Text Clustering; Opinion Mining; Social Disasters;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Kim, M. J. and Lee, S. J., 2014, Measures of abnormal user activities in online comments based on cosine similarity, Vol. 24, No. 2, pp. 335-343.   DOI
2 Kwon, H. Y., 2016, A study on the risk analysis & applicability of SNS data for detecting signs of disaster, Master's theses, Ewha Womans University, pp. 58-60.
3 Lee, S. H., 2016, Complex disasters and social conflict in south korea: the "Sacrificial System" and process of social cleavage, Discourse 201, Vol. 19 No. 2, pp. 37-61.   DOI
4 Lim, D. H., 2016, Big data analysis with R, Free academy, Korea, pp. 103-217.
5 Lim, S. Y., Lim, Y. M. and Lee, J. Y., 2014, Study on the trends of U-City and smart city researches using text mining technology, Journal of the Korean Society for Geospatial Information System, Vol. 22, No. 3, pp. 87-88.   DOI
6 Park, D. B., 2016, An analysis frame of MERS disease using text and photo images in instagram, Master's thesis, Sungkyounkwan University, pp. 68-74.
7 Seo, T. W., 2012, A Study of Real-time Disaster Information Extraction and Displayusing the Mash-up based on SNS : using the Twitter API and Google map API, Master's thesis, Pukyong National University, pp. 69-72.
8 Turney, P. D., 2002, Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews, Proc. of the 40th Annual Meeting of the Association for Computational Linguistics, ACL, Philadelphia, USA, pp. 417-424.
9 Yoo, C. H. and Hong, S. H., 2015, R visualization, Kyobobook, Korea, p. 672.