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. The model operates by assigning each word in a document to a topic, iteratively refining these assignments to maximize the likelihood of the observed data. This process helps researchers and analysts categorize large volumes of text, making it easier to understand and summarize the content.