This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents.LSAas a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors.LSAas a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general.WIREs Cogn Sci2013, 4:683–692. doi: 10.1002/wcs.1254This article is categorized under:Linguistics > Computational Models of LanguagePsychology > Language