Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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On the Development of Risk Factor Map for Accident Analysis using Textmining and Self-Organizing Map(SOM) Algorithms

재해분석을 위한 텍스트마이닝과 SOM 기반 위험요인지도 개발

  • Kang, Sungsik (Department of Safety Engineering, Pukyong National University) ;
  • Suh, Yongyoon (Department of Safety Engineering, Pukyong National University)
  • Received : 2018.09.03
  • Accepted : 2018.11.20
  • Published : 2018.12.31
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Abstract

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

Keywords

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Fig. 1. Conceptual scheme of self-organizing map.

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Fig. 2. Flowchart for the methodology.

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Fig. 3. Number of accident documents.

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Fig. 4. Word cloud of accident keywords.

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Fig. 5. Result of Document-Term matrix.

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Fig. 6. SOM-based risk factor map.

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Fig. 8. Dynamic analysis for keyword change in each cluster.

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Fig. 7. Major clusters of accidents.

Table 1. Analysis of large cells

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Table 2. Analysis of major clusters

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