Correlation analysis and text classification of chemical accident cases based on word embedding

doi:10.1016/j.psep.2021.12.038

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Correlation analysis and text classification of chemical accident cases based on word embedding
	Jing, Sifeng 3,4; Liu, Xiwei 3,4; Gong, Xiaoyan 3,4; Tang, Ying 2,3; Xiong, Gang 4; Liu, Sheng 4; Xiang, Shuguang 1; Bi, Rongshan 1
刊名	PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
	2022-02-01
卷号	158 页码:698-710
关键词	Text mining Correlation analysis Text classification Word embedding Deep learning Chemical accident cases
ISSN号	0957-5820
DOI	10.1016/j.psep.2021.12.038
通讯作者	Bi, Rongshan(brs@qust.edu.cn)
英文摘要	Accident precursors can provide valuable clues for risk assessment and risk warning. Trends such as the main characteristics, common causes, and high-frequency types of chemical accidents can provide references for formulating safety-management strategies. However, such information is usually documented in unstructured or semistructured free text related to chemical accident cases, and it can be costly to manually extract the information. Recently, text-mining methods based on deep learning have been shown to be very effective. This study, therefore, developed a text-mining method for chemical accident cases based on word embedding and deep learning. First, the word2vec model was used to obtain word vectors from a text corpus of chemical accident cases. Then, a bidirectional long short-term memory (LSTM) model with an attention mechanism was constructed to classify the types and causes of Chinese chemical accident cases. The case studies revealed the following results: 1) Common trends in chemical accidents (e.g., characteristics, causes, high-frequency types) could be obtained through correlation analysis based on word embedding; 2) The developed text-classification model could classify different types of accidents as fires, explosions, poisoning, and others, and the average p (73.1%) and r (72.5%) of the model achieved ideal performance for Chinese text classification; 3) The developed text-classification model could classify the causes of accidents as personal unsafe act, personal habitual behavior, unsafe conditions of equipment or materials and vulnerabilities management strategy; p and r were 63.6% for the causes of vulnerabilities management strategy, and the average p and r are both 60.7%; 4) the accident precursors of explosion, fire, and poisoning were obtained through correlation analyses of each high-frequency type of chemical accident case based on text classification; 5) the text-mining method can provide site managers with an efficient tool for extracting useful insights from chemical accident cases based on word embedding and deep learning. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
资助项目	Technology Innovation Project of Hunan Province[2018GK1040] ; Natural Science Foundation of Shandong Province[ZR2020MB124]
WOS关键词	EVENTS ; MODEL
WOS研究方向	Engineering
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000743757000001
资助机构	Technology Innovation Project of Hunan Province ; Natural Science Foundation of Shandong Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47244]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Bi, Rongshan
作者单位	1.Qingdao Univ Sci & Technol, Coll Chem Engn, Qingdao 266042, Peoples R China 2.Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USA 3.Qingdao Acad Intelligent Ind, Inst Smart Educ Syst, Qingdao 266044, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Jing, Sifeng,Liu, Xiwei,Gong, Xiaoyan,et al. Correlation analysis and text classification of chemical accident cases based on word embedding[J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,2022,158:698-710.
APA	Jing, Sifeng.,Liu, Xiwei.,Gong, Xiaoyan.,Tang, Ying.,Xiong, Gang.,...&Bi, Rongshan.(2022).Correlation analysis and text classification of chemical accident cases based on word embedding.PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,158,698-710.
MLA	Jing, Sifeng,et al."Correlation analysis and text classification of chemical accident cases based on word embedding".PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 158(2022):698-710.