A New Method for Identifying Key and Common Themes Based on Text Mining: An Example in the Field of Urban Expansion. (14th October 2021)

• Record Type: Journal Article
• Authors: Zhang, Yanwei; Lu, Xinhai; Lin, Chaoran; Wu, Feng; Li, Jinqiu
• Other Names: Macias-Diaz Jorge E. Academic Editor.
• Is Part Of: Discrete dynamics in nature and society. Volume 2021(2021)
• Publication Date: 2021-10-14
• Subjects: System analysis -- Periodicals; Dynamics -- Periodicals; Chaotic behavior in systems -- Periodicals; Differentiable dynamical systems -- Periodicals; 003.05
• DOI: 10.1155/2021/8166376
• Languages: English
• ISSNs: 1026-0226

Abstract

Abstract : Urban land use is a core area of multidisciplinary research that involves geography, land science, and urban planning. With the rapid progress of global urbanization, urban expansion has become a research focus in recent years. Therefore, how to scientifically and accurately identify key and common themes in the urban expansion literature has become crucial for scientific research institutions in various countries. This paper proposes a new framework for identifying such themes based on an analysis of scientific literature and by using text mining and thematic evolutionary analysis. First, the latent Dirichlet allocation algorithm is used to capture the thematic clustering of scientific literature. Second, the key degree of the thematic node in the thematic evolution transfer network is used to represent the key feature of a theme, and the PageRank algorithm is employed to measure the critical score of this theme. When recognizing common themes, the common features of various themes are digitized and mapped to a specially selected quadratic function to measure the degree of commonness. Finally, the hidden Markov model is used to build a thematic prediction model. This method can efficiently identify key and common themes from the literature and provide theoretical and technical support for future research in related fields.