Textual Machine Learning: An Application to Computational Economics Research
Christos Alexakis (),
Michael Dowling (),
Konstantinos Eleftheriou () and
Michael Polemis ()
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Christos Alexakis: Rennes School of Business
Michael Dowling: Rennes School of Business
Computational Economics, 2021, vol. 57, issue 1, No 16, 369-385
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)
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Working Paper: Textual Machine Learning: An Application to Computational Economics Research (2021)
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