Text mining

Abstract

Purpose – The purpose of this paper was to analyse text mining (TM) literature indexed in the Web of Science (WoS) under the “Information Science Library Science” subcategory. More specifically, it analyses the chronological growth of TM literature, and the major countries, institutions, departments and individuals contributing to TM literature. Collaboration in TM research is also analysed. Design/methodology/approach – Bibliographic and citation data required for this research were retrieved from the WoS database. TM being a multidisciplinary field, the search was restricted to “Information Science Library Science” subcategory in the WoS. A comprehensive query statement covering all synonyms of “text mining” was prepared using the Boolean operator “OR”. Microsoft Excel and HistCite software were used for data analysis. Pajek and VoSviewer were used for data visualization. Findings – It was found that USA is the major producer of TM research literature, and the highest number of papers were published in theJournal of The American Medical Informatics. Columbia University ranked first both in number of articles and citations received in the top ten institutes publishing TM literature. It was also observed that six of the top ten subdivisions of institutions are either from medicine or medical informatics or biomedical information. H.C. Chen and C. Friedman were seen to be the most prolific authors. Research limitations/implications – The paper analyses articles on TM published during 1999-2013 in WoS under the subcategory Information Science Library Science’. Originality/value – The paper is based on empirical data exclusively gathered for this research.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 23 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.