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A Unified Formal Framework for Factorial and Probabilistic Topic Modelling

Author

Listed:
  • Karina Gibert

    (Intelligent Data Science and Artificial Intelligence Research Group, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

  • Yaroslav Hernandez-Potiomkin

    (Intelligent Data Science and Artificial Intelligence Research Group, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

Abstract

Topic modelling has become a highly popular technique for extracting knowledge from texts. It encompasses various method families, including Factorial methods, Probabilistic methods, and Natural Language Processing methods. This paper introduces a unified conceptual framework for Factorial and Probabilistic methods by identifying shared elements and representing them using a homogeneous notation. The paper presents 12 different methods within this framework, enabling easy comparative analysis to assess the flexibility and how realistic the assumptions of each approach are. This establishes the initial stage of a broader analysis aimed at relating all method families to this common framework, comprehensively understanding their strengths and weaknesses, and establishing general application guidelines. Also, an experimental setup reinforces the convenience of having harmonized notational schema. The paper concludes with a discussion on the presented methods and outlines future research directions.

Suggested Citation

  • Karina Gibert & Yaroslav Hernandez-Potiomkin, 2023. "A Unified Formal Framework for Factorial and Probabilistic Topic Modelling," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4375-:d:1264494
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    References listed on IDEAS

    as
    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    3. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    4. Michael Greenacre & Oleg Nenadic, 2005. "Computation of multiple correspondence analysis, with code in R," Economics Working Papers 887, Department of Economics and Business, Universitat Pompeu Fabra.
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