Economic history goes digital: Topic modeling the Journal of Economic History

Arbeitspapier

Digitization and computer science have established a whole new set of methods to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, statistical algorithms that automatically infer themes from large collections of texts. In this article, I present an introduction to topic modeling and give a very first review on the research using topic models. I illustrate their capacity by applying them on 2.675 articles published in the Journal of Economic History between 1941 and 2016. This contributes to traditional research on the JEH and to current research on the cliometric revolution.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: BGPE Discussion Paper ; No. 177

Classification and Topics

• Classification: Wirtschaft ; Relation of Economics to Other Disciplines ; Methodological Issues: General ; Development of the Discipline: Historiographical; Sources and Methods
• Subject: Economic History ; Topic Models ; Latent Dirichlet Allocation ; Cliometrics ; Digitization ; Methodology

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Wehrheim, Lino
• Event: Veröffentlichung
• (who): Friedrich-Alexander-Universität Erlangen-Nürnberg, Bavarian Graduate Program in Economics (BGPE)
• (where): Nürnberg
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/180156
• Last update: 10.03.2025, 11:42 AM CET