Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining

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Abstract

Nonnegative matrix factorization (NMF) is a popular approach to model data, however, most models are unable to flexibly take into account multiple matrices across sources and time or apply only to integer-valued data. We introduce a probabilistic, Gaussian Process-based, more inclusive NMF-based model which jointly analyzes nonnegative data such as text data word content from multiple sources in a temporal dynamic manner. The model collectively models observed matrix data, source-wise latent variables, and their dependencies and temporal evolution with a full-fledged hierarchical approach including flexible nonparametric temporal dynamics. Experiments on simulated data and real data show the model out-performs, comparable models. A case study on social media and news demonstrates the model discovers semantically meaningful topical factors and their evolution.
Original languageEnglish
Title of host publicationCIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
PublisherACM
Pages1035-1043
Number of pages9
ISBN (Electronic)978-1-4503-6859-9
DOIs
Publication statusPublished - Oct 2020
Publication typeA4 Article in conference proceedings
EventACM International Conference on Information & Knowledge Management -
Duration: 19 Oct 202023 Oct 2020

Conference

ConferenceACM International Conference on Information & Knowledge Management
Abbreviated titleCIKM
Period19/10/2023/10/20

Publication forum classification

  • Publication forum level 2

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