Insights from a text mining survey on Expert Systems research from 2000 to 2016

Paulo Cortez, Sérgio Moro, Paulo Rita, David King, Jon Hall

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

This study presents a literature analysis using a semiautomated text mining and topic modelling approach of the body of knowledge encompassed in 17 years (2000–2016) of literature published in the Wiley's Expert Systems journal, a key reference in Expert Systems (ESs) research, in a total of 488 research articles. The methodological approach included analysing countries from authors' affiliations, with results emphasizing the relevance of both U.S. and U.K. researchers, with Chinese, Turkish, and Spanish holding also a significant relevance. As a result of the sparsity found on the keywords, one of our goals became to devise a taxonomy for future submissions under 2 core dimensions: ESs' methods and ESs' applications. Finally, through topic modelling, data-driven methods were unveiled as the most relevant, pairing with evaluation methods in its application to managerial sciences, arts, and humanities. Findings also show that most of the application domains are well represented, including health, engineering, energy, and social sciences.

Original languageEnglish
Article numbere12280
JournalExpert Systems
Volume35
Issue number3
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • Expert Systems
  • literature analysis
  • research categorization
  • research evolution
  • text mining

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