## Latent Dirichlet Allocation (LDA)¶

Latent Dirichlet Allocation (LDA) is a type of probabilistic topic model commonly used in natural language processing to extract topics from large collections of documents in an unsupervised manner. LDA assumes that each document in a corpus (collection of documents) is associated with a mixture of topics and the proportions of the topics varies per document. Each topic is represented as a probability distribution over a vocabulary (the set of all allowable words).