Abstract
Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial ...
Abstract
Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians' methodological toolboxes.
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