Probabilistic Topic Modeling for Comparative Analysis of Document Collections

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Probabilistic topic models, which can discover hidden patterns in documents, have been extensively studied. However, rather than learning from a single document collection, numerous real-world applications demand a comprehensive understanding of the relationships among various document sets. To address such needs, this article proposes a new model that can identify the common and discriminative aspects of multiple datasets. Specifically, our proposed method is a Bayesian approach that represents each document as a combination of common topics (shared across all document sets) and distinctive topics (distributions over words that are exclusive to a particular dataset). Through extensive experiments, we demonstrate the effectiveness of our method compared with state-of-the-art models. The proposedmodel can be useful for "comparative thinking" analysis in real-world document collections.
Publisher
Special Interest Group on Computer Graphics, Association for Computing Machinery
Issue Date
2020-03
Language
English
Article Type
Article
Citation

ACM Transactions on Knowledge Discovery from Data, v.14, no.2, pp.24

ISSN
1556-4681
DOI
10.1145/3369873
URI
http://hdl.handle.net/10203/279465
Appears in Collection
RIMS Journal Papers
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