Correlation analysis and text classification of chemical accident cases based on word embedding
Jing, Sifeng1,3; Liu, Xiwei1,3; Gong, Xiaoyan1,3; Tang, Ying3,4; Xiong, Gang1; Liu, Sheng1; Xiang, Shuguang2; Bi, Rongshan2
Source PublicationPROCESS SAFETY AND ENVIRONMENTAL PROTECTION
ISSN0957-5820
2022-02-01
Volume158Pages:698-710
Corresponding AuthorBi, Rongshan(brs@qust.edu.cn)
AbstractAccident 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.
KeywordText mining Correlation analysis Text classification Word embedding Deep learning Chemical accident cases
DOI10.1016/j.psep.2021.12.038
WOS KeywordEVENTS ; MODEL
Indexed BySCI
Language英语
Funding ProjectTechnology Innovation Project of Hunan Province[2018GK1040] ; Natural Science Foundation of Shandong Province[ZR2020MB124]
Funding OrganizationTechnology Innovation Project of Hunan Province ; Natural Science Foundation of Shandong Province
WOS Research AreaEngineering
WOS SubjectEngineering, Environmental ; Engineering, Chemical
WOS IDWOS:000743757000001
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47245
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorBi, Rongshan
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Qingdao Univ Sci & Technol, Coll Chem Engn, Qingdao 266042, Peoples R China
3.Qingdao Acad Intelligent Ind, Inst Smart Educ Syst, Qingdao 266044, Peoples R China
4.Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USA
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
1-s2.0-S095758202100(7149KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jing, Sifeng]'s Articles
[Liu, Xiwei]'s Articles
[Gong, Xiaoyan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jing, Sifeng]'s Articles
[Liu, Xiwei]'s Articles
[Gong, Xiaoyan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jing, Sifeng]'s Articles
[Liu, Xiwei]'s Articles
[Gong, Xiaoyan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 1-s2.0-S0957582021007138-main.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.