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A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information

Author

Listed:
  • Najmeh Masoumi

    (Ferdowsi University of Mashhad)

  • Reza Khajavi

    (Ferdowsi University of Mashhad)

Abstract

Assessment of new research topics and emerging technologies in any branch of knowledge is important for researchers, universities and research institutes, research investors, industry sectors, and scientific policymakers for a variety of reasons. The basic premise of this research is that the topics of interest for academic research are those that are yet underdeveloped, but are relatively well sponsored by investors. This paper proposes a method to identify and evaluate topics for their research, industrial and commercial potential based on development, investment and investment-to-development ratio (investment appeal). Since the target audience of this paper is researchers in all fields of knowledge who are mostly unfamiliar with scientometric schemes, the proposed method is aimed to be simple, based on meta-databases with easy access, without any need to clustering on keywords. The development index is defined as the keyword link strength obtained from the keyword co-occurrence network, and investment is introduced as the number of sponsors associated with each keyword. From the qualitative analysis of the development-investment diagram, six sets of keywords, entitled as: for Research, for Industry, for Commerce, Matured, Academic and Chaotic, are identified. Due to uncertain membership of research topics to these sets and their relative overlapping, they are defined as fuzzy sets. A fuzzy model, called as Fuzzy Research Ranking System (FRRS), is designed to characterize the fuzzy behavior of research topics and their potential assessment, the output of which is the membership of keywords to any of the six predefined fuzzy sets. The proposed method has been implemented for a sample knowledge domain, Geo-Engineering, which is an interdisciplinary field with significant technological capacity. Expert review of the results shows that the method is relatively well qualified for its ability to identify research topics with technological and industrial perspectives from purely scientific keywords, and may efficiently introduce a ranked list of research topics to the researchers.

Suggested Citation

  • Najmeh Masoumi & Reza Khajavi, 2023. "A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1485-1512, March.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:3:d:10.1007_s11192-022-04618-w
    DOI: 10.1007/s11192-022-04618-w
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    References listed on IDEAS

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