DOI QR코드

DOI QR Code

Relationship between Result of Sentiment Analysis and User Satisfaction -The case of Korean Meteorological Administration-

감성분석 결과와 사용자 만족도와의 관계 -기상청 사례를 중심으로-

  • 김인겸 (국립기상과학원 연구기획운영과) ;
  • 김혜민 (국립기상과학원 연구기획운영과) ;
  • 임병환 (국립기상과학원 연구기획운영과) ;
  • 이기광 (단국대학교 경영학과)
  • Received : 2016.07.08
  • Accepted : 2016.08.22
  • Published : 2016.10.28

Abstract

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.

Keywords

Twitter;Sentiment Analysis;Naive Bayes;Satisfaction

Acknowledgement

Supported by : 국립기상과학원

References

  1. A. Silver and C. Conrad, "Public perception of and response to severe weather warning in Nova Scotia, Canada," Meteorological Applications, Vol.17, pp.173-179, 2010. https://doi.org/10.1002/met.198
  2. D. Demeritt, S. Nobert, H. Cloke, and F. Pappenberger, "The European Flood Alert System and the communication, perception, and use of ensemble predictions for operational flood risk management," Hydrological Precesses, Vol.27, pp.147-157, 2013. https://doi.org/10.1002/hyp.9419
  3. H. Stephanine, B. Rachel, K. Kim, Dr. B. Jerry, and E. Somer, "A Preliminary Look at the Social Perspective of Warn-on-Forecast: Preferred Tornado Warning Lead Time and the general Public's Perceptions of Weather Risks," weather, climate, and society, Vol.3, pp.128-140, 2011. https://doi.org/10.1175/2011WCAS1076.1
  4. J. Demuth, J. Lazo, and R. Morss, "Exploring Variations in Perple's Sources, Uses, and Perceptions of Weather Forecasts," Weather, Climate, and Society, Vol.3, pp.177-192, 2011. https://doi.org/10.1175/2011WCAS1061.1
  5. R. Morss, J. Demuth, and J. Lazo, "Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public," Weather and Forecasting, Vol.23, pp.974-991, 2008. https://doi.org/10.1175/2008WAF2007088.1
  6. S. Joslyn and S. Savelli, "Communicating forecast uncertainty: public perception of weather forecast uncertainty," Meteorological Applications, Vol.17, pp.180-195, 2010. https://doi.org/10.1002/met.190
  7. S. Savelli and S. Joslyn, "Boater Safety: Communicating Weather Forecast Information to High-Stakes End Users," Weather, Climate, and Society, Vol.4, pp.7-19, 2012. https://doi.org/10.1175/WCAS-D-11-00025.1
  8. T. Kox, L. Gerhold, and U. Ulbrich, "Perception and use of uncertainty in severe weather warnings by emergency services in Germany," Atmospheric Research, Vol.158-159, pp.292-301, 2015. https://doi.org/10.1016/j.atmosres.2014.02.024
  9. 기상청, 2015년도 기상업무 국민 만족도 조사 결과보고서, 2015.
  10. 김인겸, 정지훈, 김정윤, 신진호, 김백조, 이기광, "기상예보 정보 사용자 그룹의 만족가치 제고 방안: 강수예보를 중심으로," 한국콘텐츠학회논문지, 제13권, 제11호, pp.382-395, 2013.
  11. 기상청, "기상현상과 소셜 데이터 연관성 분석을 위한 기반 연구," 2014.
  12. S. Kiritchenko, X. Zhu, and S. Mohammad, "Sentiment Analysis of Short Informal Texts," Journal of Artificial Intelligence Research, Vol.50, pp.723-762, 2014.
  13. H. Tang, S. Tan, and X. Cheng, "A survey on sentiment detection of reviews," Expert Systems with Applications, Vol.36, pp.10760-10773, 2009. https://doi.org/10.1016/j.eswa.2009.02.063
  14. 장재영, "온라인 쇼핑몰의 상품평 자동분류를 위한 감성분석 알고리즘," 한국전자거래학회지, 제14권, 제4호, pp.19-33, 2009.
  15. 이상훈, 최정, 김종우, "영역별 맞춤형 감성사전 구축을 통한 영화리뷰 감성분석," 지능정보연구, 제22권, 제2호, pp.97-113, 2016.
  16. 김도연, 오영, 박혁로, "감성 강도를 고려한 감성 분석 평가집합 구축," 한국콘텐츠학회논문지, 제12권, 제11호, pp.30-38, 2012.
  17. 김유신, 김남규, 정승렬, "뉴스와 주가: 빅데이터 감성분석을 통한 지능형 투자의사결정모형," 지능정보연구, 제18권, 제2호, pp.143-156, 2012.
  18. 정지선, 김동성, 김종우, "온라인 언급이 기업 성과에 미치는 영향 분석: 뉴스 감성분석을 통한 기업별 주가 예측," 지능정보연구, 제21권, 제4호, pp.37-51, 2015.
  19. A. Go, R. Bhayani, and L. Huang, Twitter sentiment classification using distant supervision, Technical report, Stanford, 2009.
  20. A. Pak and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," Poreceedings of LREC, 2010.
  21. B. O'Connor, R. Balasubramanyan, B. Routledge, and N. Smith, "From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series," in Proceeding of the Fourth international AAAI Conference on Weblogs and Social Media Washington, DC, May23-26, pp.122-129, 2010.
  22. D. Kang and Y. Park, "Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach," Expert Systems with Applications, Vol.41, pp.1041-1050, 2014. https://doi.org/10.1016/j.eswa.2013.07.101
  23. 김장석, 진은미, 이샘, "20대 여성소비자의 화장품 용기디자인 선호도에 관한 연구-기초화장품을 중심으로," Journal of Integrated Design Research, 제14권, 제4호, pp.97-106, 2015.
  24. http://kldp.net/projects/hannanum/
  25. L. L. Dhande and G. K. Patnaik, "Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier," International Journal of Emerging Trends & Technology in Computer Science, Vol.3, pp.313-320, 2014.
  26. H. S. Kang, J. Yoo, and D. Han, "Senti-lexicon and improved Naive-bayes algorithms for sentiment analysis of restaurant reviews," Expert Systems with Applications, Vol.39, pp.6000-6010, 2012. https://doi.org/10.1016/j.eswa.2011.11.107
  27. E. Fersini, E. Messina, and F. A. Pozzi, "Sentiment analysis: Bayesian Ensemble Learning," Decision Support Systems, Vol.68, pp.26-38, 2014. https://doi.org/10.1016/j.dss.2014.10.004
  28. A. Onan, S. Korukoglu, and H. Bulut, "Ensemble of keyword extraction methods and classifiers in text classification," Expert Systems with Applications, Vol.57, pp.232-247, 2016. https://doi.org/10.1016/j.eswa.2016.03.045
  29. 기상청, "2011년도 기상업무 대국민 만족도 조사," 2011.