Latent Semantic Analysis

Wikipedia defines "latent semantic analysis" as a technique in natural language processing, in particular in vectorial 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 Analysis can use a "term-document matrix" to describe the occurrences of terms in a document. This is a sparse matrix whose rows correspond to keywords and whose columns correspond to documents.

In Theme Zoom applications we create a different term-document matrix for each seed term, using those documents most highly ranked by the search engine for that seed term. Our Latent Semantic Index and Bayesian Index for each keyword are then derived using this term-document matrix. This is the foundation for our proprietary Theme Relevance Index (TRI) in Krakken and Local Aggregate Relevance Index (LARI) in The Last Keyword Tool.

Krakken takes this one step further by pre-filtering the keywords returned to insure a high degree of relevance, allowing us to call the collection of keywords brought back a "theme".

This facilitates market analysis in an unprecedented manner which has still not been duplicated in other tools available today.