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.

1. 서 론
 2. 선행연구
 3. 연구방법
  3.1 데이터 수집
  3.2 트윗 내용 분류
  3.3 연관규칙분석
 4. 연구결과
  4.1 감성분석 결과
  4.2 연관규칙분석 결과
 5. 결 론
 References

• 이기광(Department of Business Administration, Dankook University) | Ki-Kwang Lee Corresponding Author