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

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.

Automated summary

This paper applies the LDA algorithm to analyze FOMC transcripts, finding that discussions on economic modeling were dominant during the Global Financial Crisis, discussions on the banking system increased post-crisis, and discussions on communication gained relevance recently. The paper suggests that researchers could further utilize LDA analysis to identify topic priorities in relevant documents.