Trends recognition in journal papers by text mining

被引：14

作者：

Terachi, Masahiro ^{[1
]}

Saga, Ryosuke ^{[1
]}

Tsuji, Hiroshi ^{[1
]}

机构：

[1] Osaka Prefecture Univ, Naka Ku, 1-1 Gakuen Cho, Osaka, Japan

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS | 2006年

关键词：

D O I：

10.1109/ICSMC.2006.385062

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To recognize the trends in journal papers, this paper discusses a text mining method and its application. The method is based on combination of the conventional TF-IDF algorithm for document indexing and RFM analysis in marketing research. While TF (Term Frequency) can be clue for strength of topics and IDF (Inverted Document Frequency) can be clue for bias of topics, recency in RFM analysis can be clue of vicissitude of topics. Applying the proposed method to trend analysis for the quality control journals in the Japanese society, this paper describes how the cross-tabulation of TF, DF and LA (Last Appearance) recognizes the research trends.

引用

页码：4784 / +

页数：2