Proximity
By storing where the terms occur, search engines can understand how close one
term is to another. Generally, the closer the terms are together, the more likely the
page with matching terms will satisfy your query.
If you only use an important group of words on the page once, try to make sure
they are close together or right next to each other. If words also occur naturally,
sprinkled throughout the copy many times, you do not need to try to rewrite the
content to always have the words next to one another. Natural sounding content is
best.
Stop Words
Words that are common do not help search engines understand documents.
Exceptionally common terms, such as the, are called stop words. While search
engines index stop words, they are not typically used or weighted heavily to
determine relevancy in search algorithms. If I search for the Cat in the Hat, search
engines may insert wildcards for the words the and in, so my search will look like
* cat * * hat.
Index Normalization
Each page is standardized to a size. This prevents longer pages from having an
unfair advantage by using a term many more times throughout long page copy.
This also prevents short pages for scoring arbitrarily high by having a high percentage of their page copy composed of a few keyword phrases. Thus, there is
no magical page copy length that is best for all search engines.
The uniqueness of page content is far more important than the length. Page copy
has three purposes above all others:
• To be unique enough to get indexed and ranked in the search result
• To create content that people find interesting enough to want to link
to
• To convert site visitors into subscribers, buyers, or people who click
on ads
Not every page is going to make sales or be compelling enough to link to, but if, in
aggregate, many of your pages are of high-quality over time, it will help boost the
rankings of nearly every page on your site.
Keyword Density, Term Frequency & Term Weight
Term Frequency (TF) is a weighted measure of how often a term appears in a
document. Terms that occur frequently within a document are thought to be some
of the more important terms of that document.
If a word appears in every (or almost every) document, then it tells you little about
how to discern value between documents. Words that appear frequently will have
little to no discrimination value, which is why many search engines ignore common
stop words (like the, and, and or).
Rare terms, which only appear in a few or limited number of documents, have a
much higher signal-to-noise ratio. They are much more likely to tell you what a
document is about.
Inverse Document Frequency (IDF) can be used to further discriminate the value
of term frequency to account for how common terms are across a corpus of
documents. Terms that are in a limited number of documents will likely tell you
more about those documents than terms that are scattered throughout many
documents.
When people measure keyword density, they are generally missing some other
important factors in information retrieval such as IDF, index normalization, word
proximity, and how search engines account for the various element types. (Is the
term bolded, in a header, or in a link?)
Search engines may also use technologies like latent semantic indexing to
mathematically model the concepts of related pages. Google is scanning millions
of books from university libraries. As much as that process is about helping people
find information, it is also used to help Google understand linguistic patterns.
If you artificially write a page stuffed with one keyword or keyword phrase without
adding many of the phrases that occur in similar natural documents you may not