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Using Text Mining Algorithms for Patent Documents and Publications

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Springer Handbook of Science and Technology Indicators

Part of the book series: Springer Handbooks ((SHB))

Abstract

In this chapter we present an overview of text mining approaches that can be used to conduct science and technology studies that rely on assessing the (content) similarity between patent documents and/or scientific publications. We highlight the rationale behind vector space models, latent semantic analysis, and probabilistic topic models. In addition, several validation studies pertaining to patent documents and publications are presented. These studies reveal that choices in terms of algorithms, pre-processing, and calculation options have non-trivial consequences in terms of outcomes and their validity. As such, scholars should pay attention to the technicalities implied when engaging in text mining efforts in order for outcomes to become relevant and informative.

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Authors and Affiliations

  1. Faculty of Economics and Business, KU Leuven, Leuven, Belgium

    Bart Van Looy & Tom Magerman

Authors
  1. Bart Van Looy
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  2. Tom Magerman
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Correspondence to Bart Van Looy .

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Editors and Affiliations

  1. ECOOM and Faculty of Economics and Business, KU Leuven, Leuven, Belgium

    Wolfgang Glänzel

  2. Amsterdam, The Netherlands

    Henk F. Moed

  3. Competence Center Policy – Industry – Innovation, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

    Ulrich Schmoch

  4. Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK

    Mike Thelwall

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Van Looy, B., Magerman, T. (2019). Using Text Mining Algorithms for Patent Documents and Publications. In: Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M. (eds) Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-02511-3_38

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