Machine Learning Algorithms for Big Data Applications With Policy Implementation

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,...

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Bibliographic Details
Published inJournal of organizational and end user computing Vol. 34; no. 3; pp. 1 - 13
Main Authors Wu, Jianzu, Zhang, Kunxin
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.05.2022
Subjects
Algorithm
Algorithms
Big Data
Bureaucrats
Data mining
Data warehousing/data mining
Forecasts and trends
Generations
Machine learning
Market trend/market analysis
Public policy
Researchers
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1546-2234
1546-5012
DOI:10.4018/JOEUC.287570