Twitter를 활용한 기상예보서비스에 대한 사용자들의 만족도 분석
Public Satisfaction Analysis of Weather Forecast Service by Using Twitter
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, ‘False alarm’ and ‘Miss’ in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a ‘False alarm’ error. In addition, this study found that people’s dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users’ opinion in almost real time, which is impossible through survey or interview.
Abstract
This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, ‘False alarm’ and ‘Miss’ in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a ‘False alarm’ error. In addition, this study found that people’s dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users’ opinion in almost real time, which is impossible through survey or interview.
참고문헌 (24)
- Agarwal, R., "Fast Algorithms for Mining Association Rules in Large Databases", Proceedings of 20th International Conference on Very Large DataBases, pp.487~499 (1994)
- Anderson, E.W., "Customer Satisfaction And Word of Mouth", Journal of Service Research, 1(1), pp.5~17 (1998)[DOI]
- Baghestani, H., "Does Customer Satisfaction Have Directional Predictability for U.S. Discretionary Spending?", Applied Economics, 49(54), pp.5504~5511 (2017)[DOI]
- Demuth, J.L., "Creation And Communication of Hurricane Risk Information", Bulletin of American Meteorological Society, 93(8), pp.1113~1145 (2012)
- Drobot, S., "U.S. Public Preferences for Weather And Road Condition Information", Bulletin of American Meteorological Society, 95, pp.849~859 (2014)[DOI]
- Emanuel, K., "Will Global Warming Make Hurricane Forecasting More Difficult?", Bulletin of American Meteorological Society, 98, pp.495~501 (2017)[DOI]
- Gregow, H., "Worldwide Survey of Awareness And Needs Concerning Reanalyses And Respondents Views on Climate Sservices", Bulletin of American Meteorological Society, 97(8), pp.1461~1473 (2016)[DOI]
- Jansen, B.J., "Twitter Power : Tweets as Electronic Word of Mouth", Journal of American Society of Information Science and Technology, 60(11), pp.2169~2188 (2009)[DOI]
- Joslyn, S., "Communicating Forecast Uncertainty : Public Perception of Weather Forecast Uncertainty", Meteorological Applications, 17(2), pp.180~195 (2010)[DOI]
- Kim, I., "The Collective Value of Weather Probabilistic Forecasts According to Public Threshold Distribution Patterns", Meteorological Applications, 21(3), pp.795~802 (2014)[DOI]
- 김경애, "소셜 분석을 통한 사전제작 드라마의 가능성과 한계에 관한 연구-jtbc <맨투맨>을 중심으로-", 한국산학기술학회논문지, 19(2), pp.164~172 (2018), 한국산학기술학회[DOI]
- 이기광, "가치스코어 모형을 이용한 기상정보의 기업 의사결정에 미치는 영향 평가", 한국산업경영시스템학회지, 30(2), pp.89~98 (2007), 한국산업경영시스템학회
- Morrow, B.H., "Improving Storm Surge Risk Communication : Stakeholder Perspectives", Bulletin of American Meteorological Society, 96(1), pp.35~48 (2015)[DOI]
- Morss, R.E., "Examining the Use of Weather Forecasts in Decision Scenarios : Results from a US Survey with Implications for Uncertainty Communication", Meteorological Applications, 17(2), pp.149~162 (2010)[DOI]
- Ramos, M.-H., "Communicating Uncertainty in Hydro-meteorological Forecasts : Mission Impossible?", Meteorological Applications, 17(2), pp.223~235 (2010)[DOI]
- Schacter, D.L., "Memory distortion", pp.1~46 (1995), Harvard University Press
- Sherman-Morris, K., "Who's Googling What?", Bulletin of American Meteorological Society, 92(8), pp.975~985 (2011)[DOI]
- Silver, A., "Public Perception of And Response to Severe Weather Warnings in Nova Scotia, Canada", Meteorological Applications, 17(2), pp.173~179 (2010)[DOI]
- Smith, A.B., "US Billion-dollar Weather And Climate Disasters : Data Sources, Trends, Accuracy And Biases", Nature Hazards, 67(2), pp.387~410 (2013)[DOI]
- Walle, S.V.D., "The Order of Questions in a Survey on Citizen Satisfaction with Public Services : Lessons from a Split-ballot Experiment", Public Administrations, 89(4), pp.1436~1450 (2011)[DOI]
- Zabini, F., "Communication And Interpretation of Regional Weather Forecasts : a Survey of the Italian Public", Meteorological Applications, 22(3), pp.495~504 (2015)[DOI]
- Zaltman, G., "Rethinking Market Research : Putting People Back in", Journal of Marketing Research, 34(4), pp.424~437 (1997)[DOI]
- Zhao, Y., "R and data mining-examples and case studies", pp.89~92 (2013), Elsevier