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.

What topic modeling could reveal about the evolution of economics

Angela Ambrosino;Mario Cedrini;Stefano Fiori;Marco Guerzoni;NUCCIO, Massimiliano
2018-01-01

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.
2018
25
4
329
348
Topic modeling; economics as science; economics literature; text analysis
Angela Ambrosino, Mario Cedrini, John B. Davis, Stefano Fiori, Marco Guerzoni, Massimiliano Nuccio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1682858
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