Textual Machine Learning: An Application to Computational Economics Research

Christos Alexakis, Michael Dowling, Konstantinos Eleftheriou and Michael Polemis ()
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Christos Alexakis: ESC [Rennes] - ESC Rennes School of Business
Michael Dowling: ESC [Rennes] - ESC Rennes School of Business

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Abstract: We demonstrate the benefit to economics of machine learning approaches for textual analysis. Our use case is a machine learning based structuring of research on computational economics based on 1160 articles published in the Computational Economics journal from 1993 to 2019. Our Latent Dirichlet Allocation approach, popular in the computer sciences, use a probabilistic approach to identify shared topics across a body of documents. This combines natural language processing of article content with probabilistic learning of the latent (hidden) topics that link groups of articles. We show that this body of research can be well-described by 18 overall topics and provide a structure for computational economists to adopt this approach in other avenues.

Keywords: Topic modeling; Latent Dirichlet allocation; Computational economics (search for similar items in EconPapers)
Date: 2021-01
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Citations: View citations in EconPapers (2)

Published in Computational Economics, 2021, 57 (1), pp.369-385. ⟨10.1007/s10614-020-10077-3⟩

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Journal Article: Textual Machine Learning: An Application to Computational Economics Research (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03182910

DOI: 10.1007/s10614-020-10077-3

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