A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors

Zhiyong Zhou, Jianhui Huang, Yao Lu, Hongcai Ma, Wenwen Li and Jianhong Chen ()
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Zhiyong Zhou: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Jianhui Huang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Yao Lu: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Hongcai Ma: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Wenwen Li: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Jianhong Chen: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Mathematics, 2022, vol. 10, issue 24, 1-25

Abstract: The frequent occurrence of accidents in the chemical industry has caused serious economic loss and negative social impact. The chemical accident investigation report is of great value for analyzing the risk factors involved. However, traditional manual analysis is time-consuming and labor-intensive, while existing keyword extraction methods still need to be improved. This study aims to propose an improved text-mining method to analyze a large number of chemical accident reports. A workflow was designed for building and updating lexicons of word segmentation. An improved keyword extraction algorithm was proposed to extract the top 100 keywords from 330 incident reports. A total of 51 safety risk factors was obtained by standardizing these keywords. In all, 294 strong association rules were obtained by Apriori. Based on these rules, a Bayesian network was built to analyze safety risk factors. The mean accuracy and mean recall of the BM25 model in the comparison experiments were 10.5% and 14.38% higher than those of TF-IDF, respectively. The results of association-rule mining and Bayesian network analysis can clearly demonstrate the interrelationship between the safety risk factors. The methodology of this study can quickly and efficiently extract key information from incident reports which can provide managers with new insights and suggestions.

Keywords: chemical safety; risk factor identification; text mining; association-rule mining; Bayesian network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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