What is TF-IDF and How It Can be used to Optimize SEO Content? by@archana123

# What is TF-IDF and How It Can be used to Optimize SEO Content?

### @archana123archana

I am a Professional writer. I have written many tech articles.

If you’re even remotely concerned with SEO, you must have come across the term “TF-IDF”, which sounds like some really technical jargon, but is apparently of extreme importance to Google.

While, some debate that it is quite old-school and would not help rank your website, it really isn’t the truth.

It is a more effective way than measuring keyword density to analyze whether or not your content is optimized correctly for search engines.

In this post, you will understand what TF-IDF is and how it is important for SEO.

So before investing in SEO service packages to rank your website, first understand…

## What is TF-IDF?

Acronym? Expression? Equation? Alien-world emoji?

TF-IDF stands for Term Frequency multiplied by Inverse Document Frequency.

Why is it needed?

It needs a numeric representation to understand the relevancy of a piece of content.

This is where TF-IDF steps in. It is a formula to calculate the importance of a particular term in a specific document, in a collection of a number of documents. Search engines rely on the variants of this algorithm to deliver relevant and informative search results within milliseconds.

In fact, TF-IDF has been a crucial part of Google’s ranking mechanism for quite some time now. It works by analyzing the frequency of a term appearing in a document (term frequency – TF) and the average number of times that term appears on an average page, out of all the documents (inverse document frequency – IDF).

Now, most SEO packages providers use TF-IDF to optimize your website content such that it gains some good ranking, and thus, traffic.

Let’s discover the formula behind this backbone of search engines.

The Math Behind TF-IDF

In the above explanation of the two terms, the term frequency part is still fairly easy to comprehend, but what about this weird sounding inverse document frequency?

The following is the calculation for TF-IDF.

This really mysterious looking formula does not require you to perform these calculations. You can choose the tools to do this for you. However, you should first understand the real meaning behind the formula.

Note that TF-IDF is not a fancy way to calculate the keyword density. Here’s what it really means.

• Term Frequency (TF)

It’s simple. The number of times a term appears in the document. Easy peasy:

Clearly, term frequency is about the usage of a particular keyword: whether it is used too much or too little.

But if you think about it, it’s not as useful all by itself to measure the importance of the term.

Think about it this way. If the word count is low and the entire content is filled with jargon keywords, which make no sense, the algo won’t do such a great job in listing the most relevant content on top, right?

So, we need…

• Inverse Document Frequency (IDF)

This is what gives value to term frequency, by measuring the importance of a keyword.

The equation is:

Now, let’s say the keyword is an extremely common word like “and”. Naturally, it is likely to appear in a huge number of documents.

Therefore, it can be deduced from the formula above that its IDF value will be rather tinier than that of a term that is rarely found.

It can be safely said now that unlike keyword density, which only shows how stuffed the content is with a keyword, TF-IDF, combined tells us the value of the keyword and how it relates to the topic.

Now let’s see an example.

Example of TF-IDF Calculation

Let’s say that the number of words in a document is 1000 and the keyword appears 5 times.

Now the TF can be calculated as:

Now, notice what happens if the keyword frequency is increased to 10 instead of 5:

Clearly, the TF value doesn’t change much.

For IDF, let’s assume that there are 10 million documents and the keyword appears in 3000 of these documents.

Therefore,

Therefore, together,

You can check the TF-IDF of your content on Seobility.

So let’s understand the connection between the two.

Why Does TF-IDF Matter to SEO?

The role of TF-IDF is to ensure that your content is optimized to its maximum. You can even compare your score to that of other websites to see how your content is performing online.

This way, you can learn how Google grades the content on websites, even on the same topic.

With accurate TF-IDF analysis, you will be able to balance the terms in your content with respect to the ones being credited by Google’s algorithm.

TF-IDF means that while the smoky-teen version of keyword density was abusively kicked out, it is back as a mature, more learned adult to be used in a more literal and natural way.

TF-IDF to Enhance Keyword Research

You can conduct a TF-IDF analysis to figure out the keywords that are performing the best on search engines.

Example: Let’s say you want to optimize a page using keywords like “affordable SEO packages”. If you use a keyword searching tool, it would list keywords like “SEO service packages”, “SEO packages”, etc.

However, if you use TF-IDF tools, it’ll also show you terms that are non-SEO but are being used by pages ranking on the top of Google.

These terms can be like “content”, “tools”, “best practices”, etc.

These keywords will never show up if you run the page through a keyword search tool, given to the fact that the pages aren’t ranking on them. However, they depict the intent of the search and how you can use it to optimize your content in the eyes of the machine.

Bottom Line

The bottom line is that TF-IDF is not some fancy word for keyword density and is a significant part of a content development strategy.

While it won’t magically bring your webpage to the top, it would definitely help improve ranking.

Nevertheless, you can always get in touch with an SEO service package provider. They would wholesomely help you gain ranking, as well as optimize your content as per TF-IDF.

I work at Ranking by SEO as a Senior Writer. You can reach out to me through our website as well!

Cheers!

by archana I am a Professional writer. I have written many tech articles.