Latent Semantic Analysis (LSA) is a technique used in natural language processing to understand the relationships between words and the concepts they represent. By analyzing a large collection of text, LSA identifies patterns and similarities in word usage, allowing it to uncover hidden meanings and connections. This helps in tasks like information retrieval and document classification. LSA works by creating a mathematical representation of words and documents in a high-dimensional space. It reduces this complexity through a process called singular value decomposition, which highlights the most important relationships. This way, LSA can improve search engines and enhance the understanding of language in applications like machine learning and artificial intelligence.