Selecting the Number and Labels of Topics in Topic Modeling: A Tutorial

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dc.contributor.authorWeston, Sara J.
dc.contributor.authorShryock, Ian
dc.contributor.authorLight, Ryan
dc.contributor.authorFisher, Phillip A.
dc.date.accessioned2023-10-24T01:50:33Z
dc.date.available2023-10-24T01:50:33Z
dc.date.issued2023-05-25
dc.description13 pagesen_US
dc.description.abstractTopic modeling is a type of text analysis that identifies clusters of co-occurring words, or latent topics. A challenging step of topic modeling is determining the number of topics to extract. This tutorial describes tools researchers can use to identify the number and labels of topics in topic modeling. First, we outline the procedure for narrowing down a large range of models to a select number of candidate models. This procedure involves comparing the large set on fit metrics, including exclusivity, residuals, variational lower bound, and semantic coherence. Next, we describe the comparison of a small number of models using project goals as a guide and information about topic representative and solution congruence. Finally, we describe tools for labeling topics, including frequent and exclusive words, key examples, and correlations among topics.en_US
dc.identifier.citationWeston SJ, Shryock I, Light R, Fisher PA. Selecting the Number and Labels of Topics in Topic Modeling: A Tutorial. Advances in Methods and Practices in Psychological Science. 2023;6(2). doi:10.1177/25152459231160105en_US
dc.identifier.urihttps://doi.org/10.1177/25152459231160105
dc.identifier.urihttps://hdl.handle.net/1794/29017
dc.language.isoenen_US
dc.publisherSage Journalsen_US
dc.rightsCreative Commons BY-NC-ND 4.0-USen_US
dc.subjectChilden_US
dc.subjectDevelopmenten_US
dc.subjectHealthen_US
dc.subjectInfanten_US
dc.subjectNatural language processingen_US
dc.subjectTopic modelingen_US
dc.subjectStructural topic modelingen_US
dc.titleSelecting the Number and Labels of Topics in Topic Modeling: A Tutorialen_US
dc.typeArticleen_US

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