Latent Dirichlet Allocation (LDA) is a generative statistical model used to discover abstract topics from a collection of documents. It assumes that each document is a mixture of topics, and each topic is characterized by a distribution over words. LDA helps in organizing and summarizing large volumes of text by identifying underlying themes without needing prior labeling of the data.
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