Trends Recognition in Journal Papers by Text Mining
Masahiro Terachi, Ryosuke Saga, Hiroshi Tsuji · 2006
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 KIM 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.