Latent Dirichlet Allocation (LDA) is a generative probabilistic model used to classify documents in a corpus based on their underlying topics. It assumes that each document is a mixture of topics and that each topic is characterized by a distribution of words. LDA is particularly useful in text mining and natural language processing, allowing for the discovery of hidden thematic structures in large datasets.
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