Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords
Munan Li ()
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Munan Li: South China University of Technology
Scientometrics, 2018, vol. 116, issue 1, 77-100
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 (search for similar items in EconPapers)
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