Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts

Hali Edison () and Hector Carcel

Applied Economics Letters, 2021, vol. 28, issue 1, 38-42

Abstract: This paper applies Latent Dirichlet Allocation (LDA), a machine learning algorithm, to analyse the transcripts of the U.S. Federal Open Market Committee (FOMC) covering the period 2003–2012, including 45,346 passages. The goal is to detect the evolution of the different topics discussed by the members of the FOMC. The results of this exercise show that discussions on economic modelling were dominant during the Global Financial Crisis (GFC), with an increase in discussion of the banking system in the years following the GFC. Discussions on communication gained relevance towards the end of the sample as the Federal Reserve adopted a more transparent approach. The paper suggests that LDA analysis could be further exploited by researchers at central banks and institutions to identify topic priorities in relevant documents such as FOMC transcripts.

Date: 2021
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Related works:
Chapter: Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts (2022)
Working Paper: Text Data Analysis Using Latent Dirichlet Allocation: An Application to FOMC Transcripts (2019)
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DOI: 10.1080/13504851.2020.1730748

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