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Scientometrics

, Volume 116, Issue 1, pp 77–100 | Cite as

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

  • Munan Li
Article
  • 120 Downloads

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.

Keywords

Topic terms classification Role differentiation Author keywords Variable scale isolating outliers (VSIO) TOPSIS Additive manufacturing 

Notes

Acknowledgements

The authors acknowledge and appreciate all of the experts who were involved in the email survey. This material is based on work supported by the National Natural Science Foundation of China (No. 71673088), the Foundation of Guangdong Soft Science (No. 2017A070706003), the Foundation of China Scholarship Council (No. 201606155066).

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.School of Business AdministrationSouth China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.Guangdong Key Laboratory of Innovation Methods and Decision Management SystemsGuangzhouPeople’s Republic of China

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