What is the effect of TF-IDF and can it really help your SEO strategy?
You’d be forgiven for thinking, “Those crazy SEO guys…what are they going to think next?”
But this is not a case of this thought leader or trying to coin a new phrase.
In this chapter, you’ll learn what TF-IDF is, how it works, why it’s part of the SEO lexicon, and most importantly – whether Google uses it as a ranking factor.
Disclaimer: TF-IDF is a ranking factor
If you want to learn more about the subject, you’ll see some crazy headlines that will make you feel like you’re missing out on not allocating a budget for TF-IDF this year:
- TF-IDF for SEO: What works and what doesn’t.
- TF-IDF: The best content optimization tool SEO doesn’t use.
- TF IDF SEO: How to Smash Your Competitors with TF-IDF.
Is TF-IDF the SEO strategy you’ve been missing?
Evidence for TF-IDF as a ranking factor
Let’s start with this: what is TF-IDF?
Term Frequency – Inverse Document Frequency is a term in the field of information retrieval.
This is a number representing the statistical importance of any given word to the entire collection of documents.
In simple language, the more frequently a word appears in a collection of documents, the more important it is and the more weight the word has.
What’s with the search?
Well, Google is a huge information retrieval system.
Suppose you have a collection of 500 documents and you want to sort them in order related to terms [rocking and rolling].
The first part of the equation, term frequency (TF), will:
- ignore document Does not contain all three words.
- counting the number of occurrences of each term in each remaining document.
- factor in length document.
What the system finally gets is a TF graph for each document.
But that number alone can be problematic.
Depending on the terminology, you may still end up with a bunch of documents and no real clue as to which is most relevant to your query.
The next step, Inverse Document Frequency (IDF), provides more context to your TF.
Document Frequency = Count the terms in the document collection.
Inverse = Invert the importance of the most frequently occurring terms.
Here, the system removes the term [and] From the equation, as we can see it occurs so frequently in all 500 documents that it is irrelevant for this particular query.
We don’t want the document with the most instances [and] Highest ranking.
file with the highest weight [rocking] and [rolling] And normalization of text length is more likely to be relevant to people looking for information [rocking and rolling].
Evidence against TF-IDF as a ranking factor
The utility of this metric shrinks as the size and variety of document collections increase.
Google’s John Mueller talks about this, and explain
“It’s a fairly old indicator, and things have changed a lot over the years. There are a lot of other indicators.”
I don’t think that means it’s not a factor. I think he made it very clear that it doesn’t matter that much anymore.
As much as one likes to believe that Mueller is trying to pull a guy over, it’s impossible for him to lie on the issue.
Identifying which documents contain the word the searcher is querying is a necessary first step in returning a response.
But having said that, this is an old metric that is not useful by itself.
In a Google-sized index, the best TF-IDF can do is bring back millions or billions of results.
Can you optimize it?
Do not.
Trying to optimize for TF-IDF means trying to achieve a certain keyword density, which is called keyword stuffing.
Don’t do that.
Still, that doesn’t mean the concept is irrelevant to SEO professionals.
TF-IDF as a ranking factor: our verdict
Does Google use TF-IDF in its search ranking algorithm – maybe even as a foundational part of its algorithm?
We say absolutely not.
Why? Because it is an old (in the age of technology) information retrieval concept.
Today, Google has many better ways to evaluate web pages (e.g. word vectors, cosine similarity and others natural language processing method).
Knowing if and how often the word the user is searching for appears in the document is just the first step.
TF-IDF doesn’t say much without a myriad of other layers of analysis to determine things, like Expertise, authority and trustfor beginners.
This means that TF-IDF is not a tool or strategy you can use to optimize your website.
You can’t use TF-IDF for any useful analysis, or use it to improve your SEO, because it requires the entire corpus of search results to run calculations.
Plus, we’ve graduated, not just wondering what keywords are used for how They are used and what related topics have emerged to ensure Context and intent match our own.
TF-IDF is misunderstood by SEO professionals who use the terms TF-IDF and semantic search interchangeably.
It just measures how often a word appears in a collection of documents.
Bottom line: Knowing how to evaluate content is important, but that knowledge doesn’t always lead to another item on your SEO checklist.
Unless you’re building your own information retrieval system, you can chalk up TF-IDF as a fun fact of the past and move on.
Featured image: Robin Biong/Search Engine Magazine
