Journal
JOURNAL OF ECONOMIC METHODOLOGY
Volume 25, Issue 4, Pages 329-348Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/1350178X.2018.1529215
Keywords
Topic modeling; economics as science; economics literature; text analysis
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Funding
- European Society for the History of Economic Thought (ESHET)
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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.
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