Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them. LSA itself is an unsupervised way of uncovering synonyms in a collection of documents. To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way.