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!
(A part of Seo research series by Joydeep Bhattacharya)
Also See:-
Domain Authority and its impact on Google’s search results- SEO RESEARCH SERIES-Part 2
Anchor Text Variation and Seo
5 Points Every Seo Must Follow
Importance of Backlinks in Seo
Site Wide Links
Are You Making Your Website Vulnerable to Future Google Updates?
Seo Checklist
Seo Strategies For 2013
Why Brand Matters in Seo
Seo Secrets
How to Rank in Google
Anchor Text Variation and Seo
5 Points Every Seo Must Follow
Importance of Backlinks in Seo
Site Wide Links
Are You Making Your Website Vulnerable to Future Google Updates?
Seo Checklist
Seo Strategies For 2013
Why Brand Matters in Seo
Seo Secrets
How to Rank in Google
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