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

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.

• Publication year

  2019

• Venue

  Computational Statistics & Data Analysis

• Publication date

  2019-09-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1016/J.CSDA.2019.03.005
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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