 The latent topic block model for the co-clustering of textual interaction dataLaurent Bergé, Charles Bouveyron, Marco Corneli and Pierre Latouche Computational Statistics & Data Analysis, 2019, vol. 137, issue C, 247-270 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. Keywords: Co-clustering; Latent block model; Text matrices; Topic model; Variational inference (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:137:y:2019:i:c:p:247-270 DOI: 10.1016/j.csda.2019.03.005 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.