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Julia Silge, David Robinson (2016)
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declare that there were no conflicts of interest with respect to the authorship or the publication of this article
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Although not the focus of the current tutorial, data preprocessing is a crucial step in text analyses. We refer readers that wish to learn more to the following books: Supervised Machine Learning for
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Topic 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.
Advances in Methods and Practices in Psychological Science – SAGE
Published: May 1, 2023
Keywords: child; development; development; health; infant; natural language processing; structural topic modeling; topic modeling
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