Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Text Analytics for Classifying Types of Accident Occurrence Using Accident Report Documents

사고보고문서를 이용한 텍스트 기반 사고발생 유형 및 관계 분석

  • Kim, Beom Soo (Department of Safety Engineering, Pukyong National University) ;
  • Chang, Seongrok (Department of Safety Engineering, Pukyong National University) ;
  • Suh, Yongyoon (Department of Safety Engineering, Pukyong National University)
  • Received : 2018.04.03
  • Accepted : 2018.06.19
  • Published : 2018.06.30
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Abstract

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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