Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a statistical method that helps identify the main topics within a large collection of texts. Imagine each document as a mixture of several topics, and each topic as a group of related words. LDA works by uncovering these underlying topics based on the patterns of words in the documents, without prior knowledge of the topics. This process helps organize, categorize, and understand large sets of text data efficiently, revealing the themes that are most prominent across the collection.