Latent Dirichlet Allocation (LDA) is a generative probabilistic model used for topic modeling, which is a technique used to extract the underlying topics from a collection of text documents.
In LDA, each document is represented as a mixture of topics, and each topic is represented as a distribution over words. The model assumes that the words in a document are generated by first choosing a topic from the document's topic mixture, and then choosing a word from the chosen topic's word distribution.
LDA can be used to discover the topics present in a collection of documents, as well as to assign topics to new documents. It is widely used in natural language processing, text mining, and information retrieval.
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