Sunday, March 24, 2013

Utilizing TF-IDF Score to Increase Google Rankings of Your Site- SEO RESEARCH SERIES PART 4

One of the basic factors which Google considers when ranking a web page is tf-idf score. This post will explain the basics of tf-idf score and how to utilize it effectively in order to increase your website’s Google rankings.

Tf stands for "Term Frequency" and Idf stands for "Inverse Document Frequency". These two metrics are used for filtering entities for proper refinement of queries. This helps to return more relevant web documents with respect to the search query.

TF

Term Frequency measures the number of times a specific word or a phrase appears in a document. The higher the count, the higher will be the term frequency.

How to Calculate TF Value?

TF Value = No. of times the common word appears in the document
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Total No. of words present in the document

Example of Calculating TF Value

Suppose a web page is having the word "calligraphy" 5 times in a document consisting of 1000 words, then the TF would be calculated as given below:-

TF = 5/1000 = 0.005

IDF

Inverse Document Frequency measures the importance of a term within a document. In true terms, a word or a phrase that occurs rarely among a collection of other similar documents having high common term frequency has a high idf value.

How to Calculate IDF Value?

IDF = log ( Total No. of Documents/ No. of Documents Containing the unique term)

Example of Calculating IDF Value

Suppose 10 web pages are having the unique term "sanskrit" from among a set of 200 web pages then the IDF would be calculated as given below:-

IDF = log (200/10) = 1.30

TF*IDF

The formula for computing the relevancy of a web document as per this factor is given below:-

Importance of a Keyword = TF*IDF

Example of Finding Out The Importance of Keyword

Suppose you are interested in writing a post related to calligraphy then knowing the words of higher importance to your web page would help your page’s content to become more important in the eyes of Google related to the keyword “calligraphy”. As for example, a 1000 word post related to calligraphy would be having the words “writing” 15 times and the word “lettering” 5 times in it. Now we will find out the tf-idf score of the individual words in order to predict the importance of keywords assuming  25 out of 100 web pages are having the keyword “writing” in it and 5 out of 30 web pages are having the keyword “lettering” in it.

Keyword 1 – Writing

Tf = 15/1000 = 0.015
Idf = log (100/25) = 0.60
Tf-idf score = 0.015*0.60 = 0.009

Keyword 2 – Lettering

Tf = 5/1000 = 0.005
Idf = log (30/5) = 6
Tf-idf score = 0.005*6 = 0.03

Hence, we can clearly find out that the word “lettering” has a high tf-idf score when compared to the word “writing”. By finding out the keywords of relative importance, you may start calculating the individual tf-idf score of the important keywords and change the content of your web pages in ordering to rank highly for your targeted keywords.

Please Note:- The tf-idf score is not only the only ranking factor which works but it’s one out of more than 200 factors powering the Google ranking algorithm. Having this metric work for you would make your web page work best as per this metric but make sure to consider other ranking factors as well before you can start imagining number one search results for yourself!

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