What topic modeling could reveal about the evolution of economics*. Issue 4 (2nd October 2018)

• Record Type: Journal Article
• Authors: Ambrosino, Angela; Cedrini, Mario; Davis, John B.; Fiori, Stefano; Guerzoni, Marco; Nuccio, Massimiliano
• Is Part Of: Journal of economic methodology. Volume 25:Issue 4(2018)
• Page Start: 329
• Page End: 348
• Publication Date: 2018-10-02
• Subjects: Topic modeling -- economics as science -- economics literature -- text analysis; B4 -- B1 -- B2 -- A1; Economics -- Methodology -- Periodicals; 330.072
• DOI: 10.1080/1350178X.2018.1529215
• Languages: English
• ISSNs: 1350-178X

Abstract

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.