So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison

Marcin Chlebus () and Maciej Stefan Świtała
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Maciej Stefan Świtała: Faculty of Economic Sciences, University of Warsaw

No 2020-16, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The paper takes into consideration the broad idea of topic modelling and its application. The aim of the research was to identify mutual tendencies in econometric and machine learning abstracts. Different topic models were compared in terms of their performance and interpretability. The former was measured with a newly introduced approach. Summaries collected from esteemed journals were analysed with LSA, LDA and CTM algorithms. The obtained results enable finding similar trends in both corpora. Probabilistic models – LDA and CTM – outperform the semantic alternative – LSA. It appears that econometrics and machine learning are fields that consider problems being rather homogenous at the level of concept. However, they differ in terms of used tools and dominance in particular areas.

Keywords: abstracts; comparison; interpretability; tendencies; topics (search for similar items in EconPapers)
JEL-codes: A12 C18 C38 C52 C61 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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https://www.wne.uw.edu.pl/index.php/download_file/5660/ First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-16

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