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

文献类型：期刊论文

作者Jing, Sifeng1,3; Liu, Xiwei1,3; Gong, Xiaoyan1,3; Tang, Ying1,2; Xiong, Gang3; Liu, Sheng3; Xiang, Shuguang4; Bi, Rongshan4
刊名PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
出版日期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
DOI10.1016/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.
WOS关键词EVENTS ; MODEL
资助项目Technology Innovation Project of Hunan Province[2018GK1040] ; Natural Science Foundation of Shandong Province[ZR2020MB124]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000743757000001
出版者ELSEVIER
资助机构Technology Innovation Project of Hunan Province ; Natural Science Foundation of Shandong Province
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 Acad Intelligent Ind, Inst Smart Educ Syst, Qingdao 266044, Peoples R China
2.Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USA
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Qingdao Univ Sci & Technol, Coll Chem Engn, Qingdao 266042, 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.

入库方式： OAI收割

来源：自动化研究所

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