Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques

Li, Shuang; You, Mengjie; Li, Dingwei; Liu, Jiao

doi:10.1016/j.psep.2022.04.054

Cited by 83 publications

(26 citation statements)

References 27 publications

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“…Text mining is based on machine learning and statistical data theory to analyze and mine the implied knowledge or data collection from the text [20]. Text mining can be divided by object into data mining based on the whole text collection and data mining based on individual text.…”

Section: Text Mining Techniquesmentioning

confidence: 99%

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

Pei

2022

Journal of Environmental and Public Health

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The company is an essential organization in modern society, and the company has transformed from a purely economic organization to a corporate citizen that realizes economic responsibility and practices social responsibility at the same time. It is only by constructing a legal system of corporate social responsibility that companies can take social responsibility on the track of the legal system, realize the company’s mission of the times, and achieve a win-win situation for both the company and society. This paper used the LDA and text clustering methods to analyze existing legal texts. It obtained the theme and text clustering results, thus proposing five aspects of the legal system construction framework to guide the corporate social responsibility legal system, which has pioneering significance.

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Section: Text Mining Techniquesmentioning

confidence: 99%

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

Pei

2022

Journal of Environmental and Public Health

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“…In order to deal with new data trends, more effective text classification methods are needed. The Bayesian principle for text classification was proposed first [19]. Researchers realized that deep learning methods could significantly improve classification results [20].…”

Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach

et al. 2023

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Travel online reviews is important experience related information for understanding an inherent personality trait, novelty seeking (NS), which influences tourism motivation and the choice of tourism destinations. Manual classification of these reviews is challenging due to their high volume and unstructured nature. This paper aims to develop a classification framework and deep learning model to overcome these limitations. A multi-dimensional classification framework was created for NS personality trait that includes four dimensions synthesized from prior literature: relaxation seeking, experience seeking, arousal seeking and boredom alleviation. Based on 30 000 reviews from TripAdvisor we propose a deep learning model using Bidirectional Encoder Representations from Transformers (BERT)-Bidirectional Gated Recurrent Unit (BiGRU) to recognize NS automatically from the reviews. The classifier based on BERT-BiGRU and NS multi-dimensional scales achieved precision and F1 scores of 93.4% and 93.3% respectively, showing that NS personality trait can be relatively accurately recognized. This study also demonstrates that the classifier based on multi-dimensional NS scales can produce satisfactory results using the deep learning model. The findings also indicate that the BERT-BiGRU model achieves the best effect compared to the same kind of deep learning models. Moreover, it proves that personality traits can be automatically identified from travel reviews based on computational techniques. For practical purposes, this study provides a comprehensive classification framework for NS, which can be used in marketing and recommendation systems operating in the tourism industry.

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“…AHP, Entropy method, and multi-granularity non-equilibrium semantic treatment method are used to calculate the index's weight, and the suggestion of coal mine safety management is put forward ( 25 ). To sum up, scholars' studies on influencing factors of coal mine safety mostly construct index systems from individual factors ( 26 ), organizational factors ( 11 , 22 ), management factors ( 27 , 28 ), machinery and equipment ( 29 , 30 ), and environmental factors.…”

Section: Analysis Of Coal Mine Safety Systemmentioning

confidence: 99%

Using the ISM-ANP-SD combination model to explore the mechanism and intervention strategies of influencing factors of coal mine safety system

Yang

Xing

Tian

et al. 2022

Front. Public Health

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BackgroundWith the intelligent construction of coal mines, the number of coal mine accidents is gradually decreasing, but the complexity of accidents is increasing. Understanding the interaction mechanism among the influencing factors of the coal mine safety system is an essential part of improving and enhancing the safety of the coal mine system.MethodsThe descriptive, structural model-network hierarchical analysis (ISM-ANP) was used to explore the interaction between the factors influencing the coal mine safety system and determine each factor's importance. A system dynamics simulation model was constructed to clarify the mechanism of each factor's effect on the safety system.ResultsThe results show that Individual miners' factors directly influence coal mine system safety, organizational management factors, and group factors indirectly influence system safety and play the role of macro regulation. The intelligent system is the most profound factor influencing system safety. There are apparent differences in the influence of different subsystems on system safety, with organizational management having the most significant influence on system safety, followed by individual miners and group factors, and intelligent system factors and external environmental factors having a more negligible influence on system safety.ConclusionThere is a complex interaction between the factors affecting the safety of the coal mine system, and there are apparent differences in the influence of different subsystems on the safety level of the coal mine system. This study puts forward the intervention strategy to improve the safety of the coal mine system, which provides theoretical support and method guidance for preventing coal mine accidents and improving the safety level of the coal mine system.

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Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques

Cited by 83 publications

References 27 publications

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

[Retracted] Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach

Using the ISM-ANP-SD combination model to explore the mechanism and intervention strategies of influencing factors of coal mine safety system

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