Uncovering Hidden Meaning: A Beginner's Guide to Latent Semantic Analysis

If you have ever worked with text data, you have likely encountered the challenge of dealing with high-dimensional and sparse data. One popular solution to this problem is latent semantic analysis (LSA), also known as latent semantic indexing (LSI). LSA is a technique for extracting latent (hidden) semantics from a collection of documents or text data. It does this by mapping the documents into a lower-dimensional space, where the relationships between the documents and the underlying concepts they represent can be more easily understood. One of the key benefits of LSA is that it can handle large amounts of data efficiently and is robust to noise and sparse data.