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Cross-Corpora Comparisons of Topics and Topic Trends

Author

Listed:
  • Bystrov Victor

    (University of Lodz, Rewolucji 1905r. 41, 90-214 Lodz, Poland)

  • Naboka Viktoriia

    (Justus Liebig University Giessen, Licher Strasse 64, 35394 Giessen, Germany)

  • Staszewska-Bystrova Anna

    (University of Lodz, Rewolucji 1905r. 37/39, 90-214 Lodz, Poland)

  • Winker Peter

    (Justus Liebig University Giessen, Licher Strasse 64, 35394 Giessen, Germany)

Abstract

Textual data gained relevance as a novel source of information for applied economic research. When considering longer periods or international comparisons, often different text corpora have to be used and combined for the analysis. A methods pipeline is presented for identifying topics in different corpora, matching these topics across corpora and comparing the resulting time series of topic importance. The relative importance of topics over time in a text corpus is used as an additional indicator in econometric models and for forecasting as well as for identifying changing foci of economic studies. The methods pipeline is illustrated using scientific publications from Poland and Germany in English and German for the period 1984–2020. As methodological contributions, a novel tool for data based model selection, sBIC, is impelemented, and approaches for mapping of topics of different corpora (including different languages) are presented.

Suggested Citation

  • Bystrov Victor & Naboka Viktoriia & Staszewska-Bystrova Anna & Winker Peter, 2022. "Cross-Corpora Comparisons of Topics and Topic Trends," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 242(4), pages 433-469, August.
    • Handle: RePEc:jns:jbstat:v:242:y:2022:i:4:p:433-469:n:3
    DOI: 10.1515/jbnst-2022-0024
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    Citations

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    Cited by:

    1. Winker, Peter, 2023. "Visualizing Topic Uncertainty in Topic Modelling," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277584, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    topic models; text analysis; latent Dirichlet allocation; singular Bayesian information criterion; topic matching;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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