What is Latent Semantic Analysis (LSI Indexing)?
 

What is Latent Semantic Analysis (LSI Indexing)?

LSI keywords (Latent Semantic Indexing) are basically keywords that are semantically related to your primary keyword. Contrary to popular belief, they are NOT just synonym or keywords that are similar in meaning.

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.

Latent Semantic Indexing came as a direct reaction to people trying to cheat search engines by cramming Meta keyword tags full of hundreds of keywords, Meta description full of more keywords, and page content full of nothing more than random keywords and no subject-related material or worthwhile content.

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.

Latent Semantic Indexing (LSI) is a mathematical method used to determine the relationship between terms and concepts in content.

it's a relation between the set of keywords and the content by which those keywords are defined

The Latent semantic examination is a method in regular dialect handling, specifically distributional semantics, of breaking down connections between an arrangement of reports and the terms they contain by creating an arrangement of ideas identified with the archives and terms.

LSI keywords (Latent Semantic Indexing) are basically keywords that are semantically related to your primary keyword.