Abstract
Topic modeling techniques are used to discover the topics for (unlabeled) texts. As such, they are considered as unsupervised learning techniques which try to learn the patterns inside the text by considering words as observations. In this context, latent Dirichlet allocation (LDA) is a Bayesian topic modeling approach which has useful properties particularly for practical applications (Blei et al. 2003). In this section, we go through LDA by first reviewing the Dirichlet distribution, which is the basic distribution used in LDA.
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Chinaei, H., Chaib-draa, B. (2016). A Few Words on Topic Modeling. In: Building Dialogue POMDPs from Expert Dialogues. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-26200-0_2
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