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


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.


Twitter;Sentiment Analysis;Naive Bayes;Satisfaction


Supported by : 국립기상과학원


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