Unless you’re some kind of SEO guru, you probably haven’t heard of TF-IDF until recently. Otherwise, you wouldn’t be reading this. The term itself is more than enough to intimidate any beginner the first time they encounter it. And it certainly doesn’t help to know that it has something to do with statistical math.
But you don’t need to feel intimidated by this complex term or the math behind it. We’re here to give you an honest-to-goodness beginner’s guide to TF-IDF.
We’ll teach you:
- What TF-IDF is
- The difference between TF-IDF vs keyword density
- How to cheat your way into calculating TF-IDF using a free tool
Our guide is useful for SEOs, content writers, and website owners alike.
- SEOs can use it to find high-quality keywords with high search volume and low competition.
- Content writers can use it to write valuable content, both for readers and search engines.
- Website owners can gain a deeper understanding of SEO and use it to their advantage.
What is TF-IDF?
TF-IDF is a shorthand for Term Frequency – Inverse Document Frequency. It can mean one of many things, depending on who you’re asking.
Traditionally, it is a numerical statistic that helps identify the importance of a word in relation to a body of text, or a collection of texts (a.k.a. corpus). To illustrate, let’s say you have an article on SEO. You can use TF-IDF to find out how much value is placed in the word “SEO” based on the number of times it is mentioned in the article (the text body), and across the internet (the corpus).
For Google, TF-IDF is more than just a number. It is also a ranking factor which they use to understand and rank content. You’re probably wondering if TF-IDF is the same as keyword density, which is more commonly used term in SEO.
Technically, it’s a yes and a no.
TD-IDF vs Keyword Density
Keyword density is used to refer to the number of times a keyword is used in a web page or content. How it’s calculated is easier too. And it produces a percentage of the total word count.
TF-IDF does the same, and more. Apart from counting the number of keywords, it also weighs their importance in relation to the page’s content and the internet at large. This is why when you search for general terms like coke, Coca-Cola appears in the top searches even if you actually meant to search for Coke County. Based on their algorithm of the word coke, Coca-Cola has higher TF-IDF and thus, places more importance in it than it does for Coke County, which has a lower TF-IDF.
Google’s algorithm focuses on TF-IDF more than it does on keyword density. It allows the search engine to rank content, and by extension, web pages when displaying results to their users.
How to Calculate TF-IDF (Without Breaking a Sweat)
As promised, we’ll teach you an easy way to calculate TD-IDF without doing any math. But first, you need to sign up for Ryte. Don’t worry, the online tool has a freemium version. Once you finish registration and confirm your email address, you can proceed with the next steps.
- Step 1. Log in to your free Ryte account and click on ‘Content Success’. You should find it on the left side of your screen.
- Step 2. Under Content Success, click ‘Go to Analyze’ and input your keyword, the country, and the language you are targeting.
- Step 3. Click on ‘Start Content Analysis’ and wait for a few seconds until the page displays keyword recommendations and competition.
- Step 4. Next, click on the ‘Competition’ tab. You should see a list of your keyword recommendations alongside competition.
- Step 5. To view the TD-IDF, refer to the circles under each competition. Their size and color indicate thea following:
- Small-sized, pale blue – Low TF-IDF (Low Relevance)
- Medium-sized, aqua blue – Medium TF-IDF (Medium Relevance)
- Large-sized, dark blue – High TF-IDF (High Relevance)
The higher the relevance, the tougher it is to beat your competition.
When you hover over the circles, you’ll see the relevancy score of a keyword (that is, its TD-IDF) and number of times it is mentioned in a page.