Abstract
One topic that is gaining importance in central bank communication is central bank digital currency (CBDC). To better understand central banks’ stance towards CBDCs, we used different natural language processing techniques on a set of central bank speeches. We found that the sentiment calculated by Large Language Models, and in particular by ChatGPT, is the one that most resembles the sentiment identified by human experts in those same speeches. Our study suggests that LLMs are an effective tool for improving sentiment measurements on specific policy texts, although they are not infallible and may be subject to new risks.
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