IDEAS home Printed from https://ideas.repec.org/a/cup/polals/v28y2020i4p532-551_5.html
   My bibliography  Save this article

Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches

Author

Listed:
  • Miller, Blake
  • Linder, Fridolin
  • Mebane, Walter R.

Abstract

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling.

Suggested Citation

  • Miller, Blake & Linder, Fridolin & Mebane, Walter R., 2020. "Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches," Political Analysis, Cambridge University Press, vol. 28(4), pages 532-551, October.
  • Handle: RePEc:cup:polals:v:28:y:2020:i:4:p:532-551_5
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1047198720000042/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    2. Paweł Matuszewski, 2023. "How to prepare data for the automatic classification of politically related beliefs expressed on Twitter? The consequences of researchers’ decisions on the number of coders, the algorithm learning pro," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 301-321, February.

    More about this item

    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:cup:polals:v:28:y:2020:i:4:p:532-551_5. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/pan .

    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.