Developing and Evaluating Damage Information Classifier of High Impact Weather by Using News Big Data

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 46권 / 3호
ㆍ저자명 : Su-Ji Cho, Ki-Kwang Lee

목차

1. 서 론
2. 이론적 배경
2.1 영향예보와 재해기상 언론기사
2.2 텍스트 마이닝을 통한 문서 분류
3. 분 석
3.1 분류기 설계 및 검증
3.2 데이터 수집 및 분류기 학습
3.3 분류 결과
4. 결 론

영어 초록

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

참고 자료

없음