What is TF IDF in SEO ?
How does TF-IDF impact your SEO performance ? Over the years, search engines like Google evolved to ensure that they understand the relevance of a content piece just like the human mind does.
Consider a simple example :
When your friend tells you, “I want to book a room in a hotel” – you understand what your friend means. You understand that, “book” is a verb and it means “reserve“. You are not confused by the fact that “book” could be a noun . It could mean “a written or printed work consisting of pages glued or sewn together along one side and bound in covers.”
The human mind is advanced and amazing. Machines are trying hard to get there.
The field of machine learning that deals with understanding languages is called Natural Language Processing (NLP). Over the years, the field of NLP has advanced in leaps & bounds. The fact that Alexa understands what’s going on in the room and Google throws back an answer in a box – is testimony to the same.
Back in the days “Googling” a topic wasn’t always quick & easy. Before you hit upon the content piece that was useful, you had to skim through a whole bunch of junk and sub-optimal pages. With every passing day, Google seems to be getting better at guessing what you want. Among many other factors this has been possible due to Natural Language Processing (NLP). There are several NLP algorithms that search engines use today to determine how relevant a page on the internet is with respect to the search term. One such algorithm is Term Frequency Inverse Document Frequency (TF-IDF).
TF IDF Formula
TF or Term-Frequency , denoted by “tf” in the above formula denotes the number of times a word “i” appears in a document “j” . For example there are 10 mentions of the term “apple” in a document. The TF for “apple” would thus be 10.
IDF or Inverse Document Frequency is denoted by the logarithmic function present in the formula. Let’s assume that we search for the word “apple” in 100 documents and find it in 10. The IDF value would be log(10).
The TF IDF score is calculated by multiplying the TF value with the IDF value.
As a SEO practitioner, you aren’t expected to be machine learning savvy. However, knowing how some of these algorithms work (even in a very non technical way) can help you better understand what you need to do to stand out in the search engine.
In the following section , I will try my best to explain how TF-IDF works in a simple non-technical way.
How does TF-IDF Work
As per TF IDF, the more frequently occurring terms in an article are more important than the less frequently occurring terms.
Let’s imagine a hypothetical article where the top 10 most used terms (barring commonly used terms in English such as prepositions, articles etc.) are : apple, office, city, country, macbook, stock , iPad, business, steve & talk.
Does this tell entirely what this article is about?
Well just to be sure we will look for these terms in a predefined list of documents (known as corpora). In this case the corpora could be a set of business articles (since most of these terms sound of business). If a term in the above list is present across documents – then probably the term, though important isn’t unique to the article in question.
Going back to the above example, let’s assume that the following terms are present in many of the documents: office, city, country, stock, business, talk.
Let’s filter these out. What are we left with ? A handful of terms that are not just important but unique to the article : apple, macbook, ipad, steve !
We now know that the article is on “Apple the company” .
In a different scenario, if we were left with : apple, red, garden & juicy, we would know that the article is on ” The fruit called Apple“.
That’s how TF-IDF helps search engines understand what an article is about. And whether it’s useful in a particular context.
Why is TF IDF useful for SEO content?
Well even in 2020, a large number of experienced SEO practitioners believe that they can bluff a search engine by increasing keyword density. Algorithms like TF IDF, have made search engines evolved enough to call that bluff! Remember that TF IDF, despite being one of the simpler algorithms out there, can tell the search engine to look beyond keyword density & look for a word cluster to determine relevance.
So in short, when you are writing SEO content to rank for a particular keyword – you need to ensure that you are considering not just the keyword but also words & terms that reinforce the fact that your content is topically relevant.
How to use TF-IDF to improve SEO content?
It’s actually very simple. Remember , human mind is the most advanced and amazing algorithm out there. Use your’s.
The simplest process for boosting up your SEO content with TF-IDF is as follows:
- Check the top-10 results for the target keyword(s)
- Identify the frequently occurring terms in these articles
- You don’t need to validate these against a corpora (your mind does the cleansing)
- Think how you could add all these identified key terms to your article (with adequate frequency)
- Build the content structure accordingly
Follow the above outlined process and you will notice how your article suddenly becomes alive & useful.
There are of course a whole bunch of TF IDF analysis tools out there. Some of them are listed in the next section.
TOP TF IDF analysis tools for SEO
SEOBILITY’s TF IDF analyzer
My personal favorite happens to be Seobility’s TF IDF analysis tool for SEO. It analyzes a particular URL for a particular keyword against the top-10 ranking pages. In the process the tool identifies the key terms and words that needs to be included in the article & also compares the relative presence of such words in top ranking articles in terms of TF IDF scores.
TF IDF analyzer for SEO by tfidftool.com [Paid tool]
Another available TD IDF analyzer for SEO happens to be the paid tool by tfidftool.com. The tools compares your target page with top-ranking pages and helps you identify missed opportunities in the for of single word or multi-word phrases.
FAQs: TF IDF SEO
TF-IDF (Term Frequency * Inverse Document Frequency) weight is used in text mining and information analysis. TF IDF is also a parameter used by search engines in scoring and ranking a document’s relevance for a given search query.
TF IDF ranks documents by identifying key words and phrases in the documents that are key to explaining the result. For arriving at the same, it considers the frequency of the terms and also the uniqueness of the terms (relative to a mixed corpora).