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

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 (L...

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Bibliographic Details
Published in:Computational statistics & data analysis 2019-09, Vol.137, p.247-270
Main Authors: Bergé, Laurent R., Bouveyron, Charles, Corneli, Marco, Latouche, Pierre
Format: Article
Language:English
Subjects:
Co-clustering
Latent block model
Machine Learning
Statistics
Text matrices
Topic model
Variational inference
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Summary: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.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2019.03.005