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

Yuting Jiang, Mengyao Fu, Jie Fang, Matti Rossi

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

9 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoPACIS 2023 Proceedings
KustantajaAssociation for Information Systems
Sivumäärä16
TilaJulkaistu - heinäk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaPacific Asia Conference on Information Systems - Nanchang, Kiina
Kesto: 8 heinäk. 202312 heinäk. 2023
https://pacis2023.aisconferences.org/

Julkaisusarja

NimiPacific Asia Conference on Information Systems
ISSN (elektroninen)2689-6354

Conference

ConferencePacific Asia Conference on Information Systems
LyhennettäPACIS
Maa/AlueKiina
KaupunkiNanchang
Ajanjakso08/07/202312/07/2023
www-osoite

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