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What topic modeling could reveal about the evolution of economics

Author

Listed:
  • Angela Ambrosino
  • Mario Cedrini
  • John B. Davis
  • Stefano Fiori
  • Marco Guerzoni
  • Massimiliano Nuccio

Abstract

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.

Suggested Citation

  • Angela Ambrosino & Mario Cedrini & John B. Davis & Stefano Fiori & Marco Guerzoni & Massimiliano Nuccio, 2018. "What topic modeling could reveal about the evolution of economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 25(4), pages 329-348, October.
    • Handle: RePEc:taf:jecmet:v:25:y:2018:i:4:p:329-348
    DOI: 10.1080/1350178X.2018.1529215
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