What topic modeling could reveal about the evolution of economics

Angela Ambrosino, Mario Cedrini, John Davis, Stefano Fiori, Marco Guerzoni, Massimiliano Nuccio

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


The paper presents the topic modeling technique known as Latent Dirichlet Allocation (LDA), a form of text-mining aiming at discovering the hidden (latent) thematic structure in large archives of documents. By applying LDA to the full text of the economics articles stored in the JSTOR database, we show how to construct a map of the discipline over time, and illustrate the potentialities of the technique for the study of the shifting structure of economics in a time of (possible) fragmentation.
Original languageEnglish
Pages (from-to)329-348
JournalJournal of Economic Methodology
Issue number4
Early online date18 Oct 2018
Publication statusE-pub ahead of print - 18 Oct 2018


  • topic modeling
  • economics as science
  • economics literature
  • text analysis


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