Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords
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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.
KeywordsTopic terms classification Role differentiation Author keywords Variable scale isolating outliers (VSIO) TOPSIS Additive manufacturing
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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