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Greasing the wheels for comparative communication research: Supervised text classification for multilingual corpora

Author

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  • Lind, Fabienne
  • Heidenreich, Tobias
  • Kralj, Christoph
  • Boomgaarden, Hajo G.

Abstract

Employing supervised machine learning for text classification is already a resource-intensive endeavor in a monolingual setting. However, facing the challenge to classify a multilingual corpus, the cost of producing the required annotated documents quickly exceeds even generous time and financial constraints. We show how tools like automated annotation and machine translation can not only efficiently but also effectively be employed for the classification of a multilingual corpus with supervised machine learning. Our findings demonstrate that good results can already be achieved with the machine translation of about 250 to 350 documents per category class and language and a dictionary in just one language, which we perceive as a realistic scenario for many projects. The methodological strategy is applied to study migration frames in seven languages (news discourse in seven European countries) and discussed and evaluated for its usability in comparative communication research.

Suggested Citation

  • Lind, Fabienne & Heidenreich, Tobias & Kralj, Christoph & Boomgaarden, Hajo G., 2021. "Greasing the wheels for comparative communication research: Supervised text classification for multilingual corpora," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 3(3), pages 1-30.
    • Handle: RePEc:zbw:espost:250905
    DOI: 10.5117/CCR2021.3.001.LIND
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    References listed on IDEAS

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    1. Sebők, Miklós & Kacsuk, Zoltán, 2021. "The Multiclass Classification of Newspaper Articles with Machine Learning: The Hybrid Binary Snowball Approach," Political Analysis, Cambridge University Press, vol. 29(2), pages 236-249, April.
    2. Michael Scharkow, 2013. "Thematic content analysis using supervised machine learning: An empirical evaluation using German online news," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 761-773, February.
    3. Chang, Charles & Masterson, Michael, 2020. "Using Word Order in Political Text Classification with Long Short-term Memory Models," Political Analysis, Cambridge University Press, vol. 28(3), pages 395-411, July.
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    Cited by:

    1. Hauke Licht & Ronja Sczepanski & Moritz Laurer & Ayjeren Bekmuratovna, 2024. "No More Cost in Translation: Validating Open-Source Machine Translation for Quantitative Text Analysis," ECONtribute Discussion Papers Series 276, University of Bonn and University of Cologne, Germany.

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