In Search of Optimization

One common belief among webmasters is that good search engine ranking can be achieved by having lots of relevant pages on your website.  While this is true, many webmasters also take advantage of this fact by scraping content from other websites in an effort to fill their own websites with ‘good’ content.  Because of this, we see more and more websites that may rank well but makes no sense to the reader.  In other words, lots of webmasters feed the search engines but not the human reader.

It is worth noting however that this method is against Google’s webmaster guidelines as it tarnishes the motto of providing good and relevant search results to the internet user.  Because of this, Google is therefore “willing to take action against domains that try to rank more highly by just showing scraped or other autogenerated pages that don’t add any value to users.”

So all ye scrapers out there.. beware!

The search engines are getting smarter than ever and they have become so complex that not even their own engineers can predict the outcome of the search results. Recently, the major search engines especially Google adopted what is called Latent Semantic Indexing (LSI) / Latent Semantic Analysis (LSA) to determine the overall theme of a website. LSI is defined as follows:

Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, patented in 1988 [1] by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI) - http://www.cs.utk.edu/~lsi/

LSA uses a term-document matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to documents and whose columns correspond to terms, typically stemmed words that appear in the documents. A typical example of the weighting of the elements of the matrix is tf-idf (term frequency–inverse document frequency): the element of the matrix is proportional to the number of times the terms appear in each document, where rare terms are up weighted to reflect their relative importance.

This matrix is common to standard semantic models as well (though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrix are not always used).

What does all of this mean in English? Well, it’s pretty simple for the human brain to comprehend, but complicated to approximate mathematically. Essentially, what all of this means for the average site builder is that instead of thinking exclusively in terms of niches and whole pages based on single keywords, you should think more about organizing whole thematic relationships in contextual hierarchies sometimes called inverted pyramids, Silos or Matrices? Pages and sites should contain thematically related words and subdivisions rather than being completely keyword centric and the linking in general should flow from general to specific.

Confused? Well, you don’t have to…

Click here to get the LSI report today and have it all clarified.

And please do yourself a favor, get the report now or you risk getting left behind.

Ever wondered on what niche market should you focus on?  If you’re a search engine marketer like me then you’re most likely thinking of new possibilities everyday and God knows how tough is it to come up with another good niche - or should it be?

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More NicheBOT Links:

• WordTrend Video
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