Abstract
In this chapter, the reader is introduced to an unsupervised, probabilistic analysis model known as topic models. In topic models, the full TDM (or DTM) is broken down into two major components: the topic distribution over terms and the document distribution over topics. The topic models introduced in this chapter include latent Dirichlet allocation, dynamic topic models, correlated topic models, supervised latent Dirichlet allocation, and structural topic models. Finally, decision-making and topic model validation are presented.
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References
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Further Reading
To learn more about topic models, see Blei (2012), Blei and Lafferty (2009), and Griffiths et al. (2007).
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Anandarajan, M., Hill, C., Nolan, T. (2019). Probabilistic Topic Models. In: Practical Text Analytics. Advances in Analytics and Data Science, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-95663-3_8
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DOI: https://doi.org/10.1007/978-3-319-95663-3_8
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