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Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords

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  • Munan Li

    (South China University of Technology
    Guangdong Key Laboratory of Innovation Methods and Decision Management Systems)

Abstract

In traditional bibliometric analysis, author keywords (AKs) play a critical role in such areas as information query, co-word analysis, and capturing topic terms. In past decades, the most relevant studies have focused on the weighting methods of AKs to find specialty or discriminated terms for a topic; however, very few explorations touched the issue of role differentiation for AKs within a specific topic or the context of topic query. Furthermore, either traditional co-word analysis or the latest semantic modeling methods still face the challenges on accurate classifying and ranking the keywords/terms for a specific research topic. As a complement to prior research, a novel analytical framework based on role differentiation of AKs and Technique for Order of Preference by Similarity to Ideal Solution is proposed in this article. In addition, a case study on additive manufacturing is conducted to verify the proposed framework.

Suggested Citation

  • Munan Li, 2018. "Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 77-100, July.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:1:d:10.1007_s11192-018-2741-7
    DOI: 10.1007/s11192-018-2741-7
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    References listed on IDEAS

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    Cited by:

    1. Steffen Wendzel & Cédric Lévy-Bencheton & Luca Caviglione, 2020. "Not all areas are equal: analysis of citations in information security research," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 267-286, January.
    2. Qahtan, Talal F. & Alade, Ibrahim O. & Rahaman, Md Safiqur & Saleh, Tawfik A., 2023. "Mapping the research landscape of hydrogen production through electrocatalysis: A decade of progress and key trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Tingting Zhang & Baozhen Lee & Qinghua Zhu, 2019. "Semantic measure of plagiarism using a hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 209-239, October.
    4. Lu, Wei & Liu, Zhifeng & Huang, Yong & Bu, Yi & Li, Xin & Cheng, Qikai, 2020. "How do authors select keywords? A preliminary study of author keyword selection behavior," Journal of Informetrics, Elsevier, vol. 14(4).
    5. Gabriele Sampagnaro, 2023. "Keyword occurrences and journal specialization," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5629-5645, October.

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