 Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methodsGuo Chen and Lu Xiao Journal of Informetrics, 2016, vol. 10, issue 1, 212-223 Abstract: Publication keywords have been widely utilized to reveal the knowledge structure of research domains. An important but under-addressed problem is the decision of which keywords should be retained as analysis objects after a great number of keywords are gathered from domain publications. In this paper, we discuss the problems with the traditional term frequency (TF) method and introduce two alternative methods: TF-inverse document frequency (TF-IDF) and TF-Keyword Activity Index (TF-KAI). These two methods take into account keyword discrimination by considering their frequency both in and out of the domain. To test their performance, the keywords they select in China's Digital Library domain are evaluated both qualitatively and quantitatively. The evaluation results show that the TF-KAI method performs the best: it can retain keywords that match expert selection much better and reveal the research specialization of the domain with more details. Keywords: Domain analysis; Keyword analysis; Keyword Activity Index; Digital Library in China (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:10:y:2016:i:1:p:212-223 DOI: 10.1016/j.joi.2016.01.006 Access Statistics for this article Journal of Informetrics is currently edited by Leo Egghe More articles in Journal of Informetrics from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.