Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods

14年底投Journal of Informetrics的一篇文章，刊在JOI 2016年第1期上。在刊于《情报学报》的《领域知识分析中的关键词选择方法研究》基础上增加了TF-IDF方法，变成TF、TF-IDF、TF-KAI三种方法比较（初稿中还引入了LDA方法，但LDA结果接近TF，因此在修改中删掉了）。总体思路见共词分析方法优化的一个思路体系。

Highlights
•Selecting keywords as analysis object is important but lesser-noticed in bibliometrics.

•Keywords should be investigated based on their status both inside and outside the domain.

•The Keyword Activity Index is utilized to identify topical emphasis of a domain.

•The keywords selected by TF, TF-IDF, and TF-KAI are evaluated both qualitatively and quantitatively.

•TF-KAI performs better in selecting publication keywords for domain analysis.

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.

地址：Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods