Evaluating the latest trends of Industry 4.0 based on LDA topic model

被引：2

作者：

Ozyurt, Ozcan ^{[1
]}

Ozkose, Hakan ^{[2
]}

Ayaz, Ahmet ^{[3
]}

机构：

[1] Karadeniz Tech Univ, Fac Technol, Dept Software Engn, TR-61830 Trabzon, Turkiye

[2] Bartin Univ, Dept Management Informat Syst, Fac Econ & Adm Sci, TR-74110 Bartin, Turkiye

[3] Karadeniz Tech Univ, Digital Transformat Off, TR-61080 Trabzon, Turkiye

来源：

JOURNAL OF SUPERCOMPUTING | 2024年 / 80卷 / 13期

关键词：

Industry; 4.0; Topic modeling; Latent Dirichlet allocation; Trend analysis; Text mining; BIBLIOMETRIC ANALYSIS; DIGITAL TRANSFORMATION; BIG DATA; INTERNET; THINGS; LOGISTICS; FUTURE; FIELD;

D O I：

10.1007/s11227-024-06247-x

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study employs the Latent Dirichlet allocation method, a topic modeling technique, to reveal hidden patterns in Industry 4.0 research. The dataset comprises 8584 articles published in the Scopus database from 2011 to the end of 2022. The analysis categorized the articles into 12 distinct topics. The three most prominent topics identified are "Smart Cyber-Physical Systems," "Digital Transformation and Knowledge Management" and "Data Science in Energy," respectively. The findings from this topic modeling provide a comprehensive overview for researchers in the field of Industry 4.0, offering valuable insights into current trends and potential future research directions.

引用

页码：19003 / 19030

页数：28

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