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You move from labeling documents to discovering themes without labels. The chapter demystifies latent Dirichlet allocation (LDA) (Dirichlet priors, document-topic \(\theta \) and topic-word \(\phi \) , Gibbs sampling), then gets practical: plain LDA in seededlda, seeded LDA (with a dictionary + optional residual topics), and sentence-based LDA (sequential, topic continuity via gamma). You visualize with LDAvis and then level up to STM, which bakes in metadata: prevalence (which docs talk about a topic) and content (how vocabulary within a topic shifts by covariate like Brand). You estimate effects (estimateEffect), plot brand differences, time trends, interactions, and topic correlations. Finally, you cover choosing K with stm::searchK and ldatuning, and tie everything to marketing use cases (feature pain points, seasonality, brand comparisons, content strategy).

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Topic Modeling for Marketing Insights, Latent Dirichlet Allocation, and Structural Topic Models

  • 章节

  • 发表日期：

    2026年

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  • Daniel Dan,
  • Thomas Reutterer

摘要

You move from labeling documents to discovering themes without labels. The chapter demystifies latent Dirichlet allocation (LDA) (Dirichlet priors, document-topic \(\theta \) and topic-word \(\phi \) , Gibbs sampling), then gets practical: plain LDA in seededlda, seeded LDA (with a dictionary + optional residual topics), and sentence-based LDA (sequential, topic continuity via gamma). You visualize with LDAvis and then level up to STM, which bakes in metadata: prevalence (which docs talk about a topic) and content (how vocabulary within a topic shifts by covariate like Brand). You estimate effects (estimateEffect), plot brand differences, time trends, interactions, and topic correlations. Finally, you cover choosing K with stm::searchK and ldatuning, and tie everything to marketing use cases (feature pain points, seasonality, brand comparisons, content strategy).