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Using Supervised Machine Learning to Code Policy Issues

Author

Listed:
  • Bjorn Burscher
  • Rens Vliegenthart
  • Claes H. De Vreese

Abstract

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coders. Furthermore, we investigate the capability of an automatic coding tool, which is based on supervised machine learning, to generalize across contexts. We conclude by highlighting implications for methodological advances and empirical theory testing.

Suggested Citation

  • Bjorn Burscher & Rens Vliegenthart & Claes H. De Vreese, 2015. "Using Supervised Machine Learning to Code Policy Issues," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 122-131, May.
    • Handle: RePEc:sae:anname:v:659:y:2015:i:1:p:122-131
    DOI: 10.1177/0002716215569441
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
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