Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage

Dejian Yu () and Zhaoping Yan ()
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Dejian Yu: Nanjing Audit University
Zhaoping Yan: Nanjing Audit University

Scientometrics, 2022, vol. 127, issue 7, No 25, 4274 pages

Abstract: Abstract With the development of the era of big data, research data are accumulating, and various research directions emerge endlessly. It is difficult for researchers to grasp the hotspots and development trends of the discipline. Therefore, exploring methods to quickly and accurately identify research fronts is of great significance to scientific and technological innovation. This paper proposes a research front identification method integrating machine learning and main path analysis in conjunction with papers and patents based on the existing research. The innovation of this method is the combination of citation analysis and semantic analysis to identify research front from the perspective of science-technology linkage. This article takes the Internet of Things in supply chain as an example to verify the feasibility and effectiveness of the method. It is of great significance to identify important scientific and technological research fronts in a specific domain by intuitively revealing knowledge diffusion and text mining. The proposed method enriches the application of MPA and helps scholars grasp the latest information, mainstreams and future directions.

Keywords: Main path analysis; Text classification; Machine learning; Science-technology linkage; Internet of Things (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11192-022-04443-1

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