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Keyword density is the percentage of times a keyword or phrase appears on a web page compared to the total number of words on the page. In the context of search engine optimization, keyword density can be used to determine whether a web page is relevant to a specified keyword or keyword phrase.

In the late 1990s, the early days of search engines, keyword density was an important factor in page ranking. However, as webmasters discovered how to implement optimum keyword density, search engines began giving priority to other factors beyond the direct control of webmasters. Today, the overuse of keywords, a practice called keyword stuffing, will cause a web page to be penalized.

Many SEO experts consider the optimum keyword density to be 1 to 3 percent; more could be considered search spam. The formula to calculate your keyword density on a web page for SEO purposes is ( N k r / T k n ) ∗ 100 {\displaystyle (Nkr/Tkn)*100} , where Nkr is how many times you repeated a specific keyword, and Tkn the total words in the analyzed text. The result is a keyword density value. When calculating keyword density, ignore html tags and other embedded tags which will not appear in the text of the page once published.

When calculating the density of a keyword phrase, the formula would be ( N k r ∗ N w p / T k n ) ∗ 100 {\displaystyle (Nkr*Nwp/Tkn)*100} , where Nwp is the number of words in the phrase. So, for example, for a four-hundred word page about search engine optimization where "search engine optimization" is used four times, the keyword phrase density is (4*3/400)*100 or 3 percent.

From a mathematical viewpoint, the original concept of keyword density refers to the frequency (Nkr) of appearance of a keyword in a dissertation. A "keyword" consisting of multiple terms, e.g. "blue suede shoes," is an entity in itself. T frequency of the phrase "blue suede shoes" within a dissertation drives the key(phrase) density. It is "more" mathematically correct for a "keyphrase" to be calculated just like the original calculation, but considering the word group, "blue suede shoes," as a single appearance, not three:

Density = ( Nkr / Tkn ) * 100.

'Keywords' (kr) that consist of several words artificially inflate the total word count of the dissertation. The purest mathematical representation should adjust the total word count (Tkn) lower by removing the excess key(phrase) word counts from the total:

Density = ( Nkr / ( Tkn -( Nkr * ( Nwp-1 ) ) ) ) * 100.where Nwp = the number of terms in the keyphrase.

This general formula allows that the total word count will be unaffected if the key(phrase) is indeed a single term, so it acts as the original formula.

Beyond the formulas, keyword density can be measured in a push of a button online with tools that count the number of instances a keyphrase has been mentioned.

However, with the release of the Hummingbird update changed how Google evaluates content. Instead of that looking for exact-match keywords, Google now attempts to understand the intent behind a user’s query, and finds pages that match that intent. For example, rather than looking for instances of “ice cream parlor” on pages online, Google looks for pages that demonstrate qualities that an ice cream parlor would have, speaking contextually about ice cream parlors using natural, conversational language.

This implies that keyword inclusion isn’t nearly as important as simply writing about the right subjects—and relying on natural language to take care of the rest.

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End-of-Transmission-Block (ETB) is a communications control character used to indicate the end of a block of data for communications purposes. ETB is used for segmenting data into blocks when the block structure is not necessarily related to the processing function.

In ASCII, ETB is code point 23 (0x17, or ^W in caret notation) in the C0 control code set. In EBCDIC, ETB is code point 0x26.

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Optical mark recognition (also called optical mark reading and OMR) is the process of capturing human-marked data from document forms such as surveys and tests. They are used to read questionnaires, multiple choice examination paper in the form of shaded areas.

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