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Comparison of topic extraction approaches and their results

Author

Listed:
  • Theresa Velden

    (University of Michigan School of Information
    Technical University Berlin)

  • Kevin W. Boyack

    (SciTech Strategies, Inc.)

  • Jochen Gläser

    (Technical University Berlin)

  • Rob Koopman

    (OCLC Research)

  • Andrea Scharnhorst

    (DANS-KNAW)

  • Shenghui Wang

    (OCLC Research)

Abstract

This is the last paper in the Synthesis section of this special issue on ‘Same Data, Different Results’. We first provide a framework of how to describe and distinguish approaches to topic extraction from bibliographic data of scientific publications. We then compare solutions delivered by the different topic extraction approaches in this special issue, and explore where they agree and differ. This is achieved without reference to a ground truth, since we have to assume the existence of multiple, equally important, valid perspectives and want to avoid bias through the adoption of an ad-hoc yardstick. Instead, we apply different ways to quantitatively and visually compare solutions to explore their commonalities and differences and develop hypotheses about the origin of these differences. We conclude with a discussion of future work needed to develop methods for comparison and validation of topic extraction results, and express our concern about the lack of access to non-proprietary benchmark data sets to support method development in the field of scientometrics.

Suggested Citation

  • Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2306-1
    DOI: 10.1007/s11192-017-2306-1
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    References listed on IDEAS

    as
    1. Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
    2. Nees Jan Eck & Ludo Waltman, 2017. "Citation-based clustering of publications using CitNetExplorer and VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1053-1070, May.
    3. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    4. Rob Koopman & Shenghui Wang, 2017. "Mutual information based labelling and comparing clusters," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1157-1167, May.
    5. Theresa Velden & Carl Lagoze, 2013. "The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(12), pages 2405-2427, December.
    6. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
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    8. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    9. Diana Hicks & Paul Wouters & Ludo Waltman & Sarah de Rijcke & Ismael Rafols, 2015. "Bibliometrics: The Leiden Manifesto for research metrics," Nature, Nature, vol. 520(7548), pages 429-431, April.
    10. Theresa Velden & Carl Lagoze, 2013. "The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(12), pages 2405-2427, December.
    11. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    12. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    13. Kevin Boyack & Wolfgang Glänzel & Jochen Gläser & Frank Havemann & Andrea Scharnhorst & Bart Thijs & Nees Jan Eck & Theresa Velden & Ludo Waltmann, 2017. "Topic identification challenge," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1223-1224, May.
    14. Wolfgang Glänzel & Bart Thijs, 2017. "Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1071-1087, May.
    15. Shenghui Wang & Rob Koopman, 2017. "Clustering articles based on semantic similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1017-1031, May.
    16. Kevin W. Boyack, 2017. "Thesaurus-based methods for mapping contents of publication sets," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1141-1155, May.
    17. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    18. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
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