Latent Dirichlet Allocation (LDA)

A generative statistical model used to discover abstract topics within a collection of documents. It assumes that each document is a mixture of a small number of topics and that each word in the document is attributable to one of the document’s topics. LDA helps in identifying unobserved (latent) topic structures within sets of observations, making it possible to explain similarities in the data based on these hidden groups. By analyzing the distribution of words across documents, LDA can infer the topic distribution that generated the observed words in each document, facilitating the understanding of large text corpora by grouping similar documents and words under common topics. This model is widely applied in natural language processing and information retrieval to enhance the organization, summarization, and exploration of large datasets.