Abstract
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-artmodels. The proposedmodel can be useful for "comparative thinking" analysis in real-world document collections.
Original language | English |
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Article number | A24 |
Journal | ACM Transactions on Knowledge Discovery from Data |
Volume | 14 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2020 Mar 4 |
Keywords
- Probabilistic topic modeling
- text mining
ASJC Scopus subject areas
- Computer Science(all)