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Machine Learning Algorithms for Big Data Applications With Policy Implementation

Machine Learning Algorithms for Big Data Applications With Policy Implementation

Jianzu Wu, Kunxin Zhang
Copyright: © 2022 |Volume: 34 |Issue: 3 |Pages: 13
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893264|DOI: 10.4018/JOEUC.287570
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MLA

Wu, Jianzu, and Kunxin Zhang. "Machine Learning Algorithms for Big Data Applications With Policy Implementation." JOEUC vol.34, no.3 2022: pp.1-13. http://doi.org/10.4018/JOEUC.287570

APA

Wu, J. & Zhang, K. (2022). Machine Learning Algorithms for Big Data Applications With Policy Implementation. Journal of Organizational and End User Computing (JOEUC), 34(3), 1-13. http://doi.org/10.4018/JOEUC.287570

Chicago

Wu, Jianzu, and Kunxin Zhang. "Machine Learning Algorithms for Big Data Applications With Policy Implementation," Journal of Organizational and End User Computing (JOEUC) 34, no.3: 1-13. http://doi.org/10.4018/JOEUC.287570

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Abstract

This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.