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The latent topic block model for the co-clustering of textual interaction data

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  • Bergé, Laurent R.
  • Bouveyron, Charles
  • Corneli, Marco
  • Latouche, Pierre

Abstract

Textual interaction data involving two disjoint sets of individuals/objects are considered. An example of such data is given by the reviews on web platforms (e.g. Amazon, TripAdvisor, etc.) where buyers comment on products/services they bought. A new generative model, the latent topic block model (LTBM), is developed along with an inference algorithm to simultaneously partition the elements of each set, accounting for the textual information. The estimation of the model parameters is performed via a variational version of the expectation maximization (EM) algorithm. A model selection criterion is formally obtained to estimate the number of partitions. Numerical experiments on simulated data are carried out to highlight the main features of the estimation procedure. Two real-world datasets are finally employed to show the usefulness of the proposed approach.

Suggested Citation

  • Bergé, Laurent R. & Bouveyron, Charles & Corneli, Marco & Latouche, Pierre, 2019. "The latent topic block model for the co-clustering of textual interaction data," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 247-270.
    • Handle: RePEc:eee:csdana:v:137:y:2019:i:c:p:247-270
    DOI: 10.1016/j.csda.2019.03.005
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    References listed on IDEAS

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    4. Hathaway, Richard J., 1986. "Another interpretation of the EM algorithm for mixture distributions," Statistics & Probability Letters, Elsevier, vol. 4(2), pages 53-56, March.
    5. 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.
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    1. Nicolas Jouvin & Pierre Latouche & Charles Bouveyron & Guillaume Bataillon & Alain Livartowski, 2021. "Greedy clustering of count data through a mixture of multinomial PCA," Computational Statistics, Springer, vol. 36(1), pages 1-33, March.

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