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논문 기본 정보

자료유형
학술저널
저자정보
김범수 (부경대학교 안전공학과) 장성록 (부경대학교 안전공학과) 서용윤 (부경대학교 안전공학과)
저널정보
한국안전학회 한국안전학회지 한국안전학회지 제33권 제3호
발행연도
2018.1
수록면
58 - 64 (7page)

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Recently, a lot of accident report documents have accumulated in almost all of industries, including critical information of accidents. Accordingly, text data contained in accident report documents are considered useful information for understanding accident processes. However, there has been a lack of systematic approaches to analyzing accident report documents. In this respect, this paper aims at proposing text analytics approach to extracting critical information on accident processes. To be specific, major causes of the accident occurrence are classified based on text information contained in accident report documents by using both textmining and latent Dirichlet allocation (LDA) algorithms. The textmining algorithm is used to structure the document-term matrix and the LDA algorithm is applied to extract latent topics included in a lot of accident report documents. We extract ten topics of accidents as accident types and related keywords of accidents with respect to each accident type. The cause-and-effect diagram is then depicted as a tool for navigating processes of the accident occurrence by structuring causes extracted from LDA. Further, the trends of accidents are identified to explore patterns of accident occurrence in each of types. Three patterns of increasing to decreasing, decreasing to increasing, or only increasing are presented in the case of a chemical plant. The proposed approach helps safety managers systematically supervise the causes and processes of accidents through analysis of text information contained in accident report documents.

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