Applying topic modeling with prior domain-knowledge in information systems research

Yuting Jiang, Mengyao Fu, Jie Fang, Matti Rossi

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Topic modeling is gaining traction in Information Systems (IS) research as more textual material becomes accessible online and computational tools for analyzing large textual datasets are getting more powerful. This paper advances a new two-step correlation explanation topic modeling (Corex) method with prior domain knowledge to improve the interpretability of topic modeling to meet the needs of current IS research. The proposed method combines the traditional Latent Dirichlet Allocation topic model and the Anchored correlation explanation topic model. In the first step, the approach allows for the rapid and maximum acquisition of topic words related to domain knowledge. These anchor words are then inputted into the second-step CorEx topic model. We further applied and verified the effectiveness of the two-step Corex method to a textual dataset containing 4,290,484 users’ personal profiles, thereby illustrating the utility of applying this innovative topic-modeling method in information systems research.
Original languageEnglish
Title of host publicationPACIS 2023 Proceedings
PublisherAssociation for Information Systems
Number of pages16
Publication statusPublished - Jul 2023
MoE publication typeA4 Conference publication
EventPacific Asia Conference on Information Systems - Nanchang, China
Duration: 8 Jul 202312 Jul 2023

Publication series

NamePacific Asia Conference on Information Systems
ISSN (Electronic)2689-6354


ConferencePacific Asia Conference on Information Systems
Abbreviated titlePACIS
Internet address


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