Latent Semantic Analysis LSA And Search Engines SEO

Latent Semantic Analysis LSA And Search Engines SEO



By Jose Nuñez

Latent Semantic Analysis (LSA) is​ applied by taking millions of​ web pages,​ where the​ search engines can learn which words are related and which noun concepts relate to​ one another. Searh Engines are considering related terms and recognizing which terms that frequently occur together,​ maybe on​ the​ same page,​ or​ in​ close enough proximity. So it​ is​ mainly used for language modeling or​ most other applications.

Part of​ this process involves looking at​ the​ copy content of​ a​ page,​ or​ included on​ the​ links,​ and looking through the​ ways on​ how they are related. Latent Semantic Analysis (LSA) is​ based on​ the​ well known Singular Value Decomposition Theorem from Matrix Algebra but applied to​ text. That is​ why some of​ the​ semantic analysis that is​ done at​ the​ page content level it​ may also be done on​ the​ linkage data.

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.




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