TF-IDF explained Follow Apr 02, 2021 · 3 mins read
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The ever-changing landscape of search engine optimization (SEO) is a minefield of algorithms and protocols that are complex, and yet vitally important in today’s digital marketing sector. Most of us, even those who are only marginally acquainted with the rules, know about keywords and back linking. But a little thing called Term Frequency-Inverse Document Frequency, or TF-IDF as it is better known, is deserving of attention for a number of reasons. This quiet little SEO function can help you take the power of words on the web and refine it in a way that can accomplish precise marketing targets and goals.

What is it and what does it do?

Term Frequency, or TF, measures the frequency of a term or word within a document.

Inverse Document Frequency (IDF) evaluates the relevance of the word in the document, or how well it fits the context of the entire document.

Working together, these two components create a powerful analytic tool for assessing the quality of a website’s text elements.

How is it used?

The formula behind TF-IDF is a simple mathematical equation.

To calculate Term Frequency (TF), look at the keyword count in relation to the total word count of the text in question. For a 1000-word blurb, if the keyword appears 10 times, the TF score is 10/1000.

To calculate the Inverse Document Frequency (IDF), look at the number of keywords in relation to the total number of documents. If you are looking at 1000 documents and the keyword appears in 30 of them, the IDF score is 1000/30.

The combined score is a result of multiplying both values.

As might be expected, webmasters and digital content creators can use TF-IDF scores and data to evaluate and plan their web content; making sure it is optimized to achieve semantic search engine authority. Basically the more unique the single words in the content are, the better it will resonate in terms of TF-IDF. And the better it does in terms of TF-IDF, the more likely it is to resonate with search engines, stakeholders, the public and potential customers.

But much more than this, digital marketers can also glean a lot of data about competitors in their sector by analyzing information about word frequency and quality collected by search engines such as Google using the TF-IDF formula.

The formula described above is simple, but there are more sophisticated ways of collecting the data and calculating the TF-IDF score as well. However, programs such as Python can the values calculate automatically and it is more important for digital marketing teams to understand the concept and the power of the tool, than the nitty gritty of how scores are arrived at. The Power of TF-IDF Tools

The following tools are just two of the best of the many out there. Both will allow your digital marketing team to step up their game in the content relevance game.

Website Auditor

Developed by SEO PowerSuite, this tool allows you to examine top ranking content to improve your own. Easy to use, the program identifies words that are closest to your chosen keywords, as well as what is working best for your competition. It’s like having a well-trained and particularly focused corporate spy on your team.

Text Tools

Similarly, this Chrome analytics tool also analyzes that top-ranked competition and provides data to improve the content/keyword relationship within your web content. The report generated by the data collected is your blueprint on how to improve your business, simple as that.

Want to learn more about how to use the powerful tools of TF-IDF to analyze, optimize and model the text content on your website? For the complete lowdown on what is TF-idf? Get in touch with the professional technicians at Atastic SEO.

Written by Follow