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Textual Machine Learning: An Application to Computational Economics Research

Author

Listed:
  • Christos Alexakis

    (Rennes SB - Rennes School of Business)

  • Michael Dowling

    (Rennes SB - Rennes School of Business)

  • Konstantinos Eleftheriou

    (University of Piraeus)

  • Michael Polemis

    (University of Piraeus)

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.

Suggested Citation

  • Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Post-Print hal-03182910, HAL.
    • Handle: RePEc:hal:journl:hal-03182910
    DOI: 10.1007/s10614-020-10077-3
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    Cited by:

    1. David Ardia & Keven Bluteau & Mohammad‐Abbas Meghani, 2024. "Thirty years of academic finance," Journal of Economic Surveys, Wiley Blackwell, vol. 38(3), pages 1008-1042, July.
    2. Rizki Praba Nugraha & Akhmad Fauzi & Ernan Rustiadi & Sambas Basuni, 2025. "Quantifying the Geopark Contribution to the Village Development Index Using Machine Learning—A Deep Learning Approach: A Case Study in Gunung Sewu UNESCO Global Geopark, Indonesia," Sustainability, MDPI, vol. 17(15), pages 1-31, July.

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