Research on Enterprise Hidden Danger Association Rules Based on Text Analysis

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Ge, Shengxin^1 ; Zhuang, Yufeng^1 ; Hu, Yanzhu^1 ; Ai, Xinbo^1

School of Automation, Beijing University of Post and Telecommunications, Beijing

100876, China^1

关键词: Apriori algorithms;    Chinese word segmentation;    Common sense;    Latent dirichlet allocations;    Structural data;    Text analysis;    Text information;    Topic Modeling;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/3/032170/pdf
DOI  :  10.1088/1755-1315/252/3/032170

来源: IOP

【 摘 要 】

Descriptive fields are used in the daily operation of an enterprise as a dimension for recording hidden danger letters. However, it is quite difficult to extract corresponding effective data from such text information to guide the operation of the enterprise. In view of this problem, this paper first grouped the hidden danger data of different enterprise types, then used Chinese word segmentation technology to transform the corresponding descriptive fields into structural data, extracted the topic model using Latent Dirichlet Allocation (LDA) algorithm, and finally used Apriori algorithm to find the association rules of hidden dangers under different enterprise types. After the analysis of the experimental results, the association rules validated accord with common sense and can provide data support for hidden danger supervision.