Latent Dirichlet Allocation (LDA) is a statistical model used in natural language processing to discover topics within a collection of documents. It assumes that each document is a mixture of topics, and each topic is characterized by a distribution of words. By analyzing the patterns of word co-occurrences, LDA can identify the underlying themes present in the text data. LDA operates by assigning each word in a document to a topic based on probabilities. It iteratively refines these assignments to maximize the likelihood of the observed data. This makes LDA a powerful tool for tasks like document classification, topic modeling, and information retrieval.