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.
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
What topic modeling could reveal about the evolution of economics.pdf
Accesso riservato
Descrizione: Articolo principale
Tipo di file:
PDF EDITORIALE
Dimensione
2.88 MB
Formato
Adobe PDF
|
2.88 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
Guerzoni_What_topic_modeling_could_reveal_about_the_evolution_of_economics (2).pdf
Accesso riservato
Tipo di file:
PDF EDITORIALE
Dimensione
2.88 MB
Formato
Adobe PDF
|
2.88 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
JEM article final-1.pdf
Accesso aperto
Tipo di file:
POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione
1.52 MB
Formato
Adobe PDF
|
1.52 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
