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.
Bibliographical noteFunding Information:
This work was supported in part by the U.S. National Science Foundation grants IIS-1619028, IIS-1707498, and IIS-1838730, and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF2019R1A2C4070420).
© 2020 Association for Computing Machinery.
- Probabilistic topic modeling
- text mining
ASJC Scopus subject areas
- General Computer Science