Latent Dirichlet Allocation (LDA) is an algorithm in the field of topic modeling.

If observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA represents documents as mixtures of topics that spit out words with certain probabilities.

Popular software packages to perform LDA include

It should not be confused with Linear Discriminant Analysis, a supervised learning procedure for classifying observations into a set of categories.