IDEAS home Printed from https://ideas.repec.org/a/jns/jbstat/v242y2022i4p433-469n3.html
   My bibliography  Save this article

Cross-Corpora Comparisons of Topics and Topic Trends

Author

Listed:
  • Bystrov Victor

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

  • Naboka Viktoriia
  • Winker Peter

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

  • Staszewska-Bystrova Anna

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

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 & Winker Peter & Staszewska-Bystrova Anna, 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jbnst-2022-0024
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jbnst-2022-0024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jns:jbstat:v:242:y:2022:i:4:p:433-469:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.