Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora. In the context of text, each document is modeled as a mixture of topics, and each topic is a distribution over words. LDA is commonly used for topic modeling, document classification, and information retrieval to discover underlying thematic structures within a large collection of documents.
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