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Clustering scientific documents with topic modeling

Author

Listed:
  • Chyi-Kwei Yau

    (Georgia Tech)

  • Alan Porter

    (Georgia Tech
    Search Technology, Inc.)

  • Nils Newman

    (IISC
    University of Maastricht)

  • Arho Suominen

    (VTT Technical Research Centre of Finland, Innovations, Economy, and Policy)

Abstract

Topic modeling is a type of statistical model for discovering the latent “topics” that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities.

Suggested Citation

  • Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    • Handle: RePEc:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1321-8
    DOI: 10.1007/s11192-014-1321-8
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    References listed on IDEAS

    as
    1. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    2. Erjia Yan & Ying Ding & Elin K. Jacob, 2012. "Overlaying communities and topics: an analysis on publication networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 499-513, February.
    3. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    4. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    5. Patrick Glenisson & Wolfgang Glänzel & Olle Persson, 2005. "Combining full-text analysis and bibliometric indicators. A pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(1), pages 163-180, March.
    Full references (including those not matched with items on IDEAS)

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