Latent Semantic Analysis (LSA) is a technique in natural language processing that involves examining the relationships between a set of documents and the terms they contain. It uses a mathematical approach, often based on singular value decomposition, to identify patterns and meanings in data. By transforming large text datasets into a more manageable form, LSA helps in understanding context and semantic relationships beyond simple keyword matching. This allows for improved information retrieval and text classification by capturing hidden meanings.