LSA represents the meaning of words as a vector, thus calculating word similarity. Iit has been very efficient to that purpose, and is still used. Regarding text for this application, is considered linear. This makes LSA slow due to using a matrix method called Singular Value Decomposition to create the concept space. But it does only address semantic similarity and not ranking, which is the SEO priority.

Scientific SEOs have a similar goal. They try to discover which words and phrases are most semantically linked together for a given keyword phrase, so when Search Engines crawl the web, they find that links to particular pages and content within them is semantically related to other information that is currently in their database. So, in conclusion, LSA calculates a measure of similarity for words based on possible occurrence patterns of words in documents and on how often words appear in the same context or together with the same set of common elements.