Citation

Suh Y. Safety Sci. 2021; 142: e105363.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ssci.2021.105363

PMID

unavailable

Abstract

The narrative text analytics has recently focused on identifying an accident process in the various fields of safety such as manufacturing, construction, chemicals, and service. In particular, narrative texts allow finding multiple accident factors and types of accident process including industry, hazard, work activity, and accident result. To present similarity and difference of accident process by categorizing those multiple accident factors shared across industries, identifying sectoral patterns of accidents are useful. In this respect, this study aims to identify the sectoral patterns of accident process using narrative texts information contained in accident reports. For this, the textmining and latent Dirichlet allocation (LDA) algorithms are used to extract topics of accidents and their main factors, matched with class of industries. As a result of the case study for the Occupational Safety and Health Administration (OSHA) in the United States, the five sectoral patterns of accident process are identified: scale-intensive, facility-intensive, supplier-dominated, market-dominated, and service-dominated patterns. According to these sectoral patterns, managers and policy makers in the fields of safety take a look at the management issues related to the industry, source, activity, and accident result, considering respective characteristics of industrial sites.

Language: en

Keywords

Accident process; Latent Dirichlet allocation (LDA); Narrative texts; Occupational Safety and Health Administration (OSHA); Sectoral pattern; Textmining