In Google’s I/O Future of Search talk, Ben Gomes said one of the biggest initiatives for the future of search is to improve machine learning to achieve natural language processing (NLP) and reduce language friction.
While there are many methods for achieving semantic understanding, TF-IDF is one of the more common confronting (and legacy) methods that search engines use to better understand the intent and multiple meanings behind a query.
What is TF-IDF?
The term Frequency-Inverse Document Frequency is a sophisticated algorithm that calculates the weight (or importance) of a word across a set of documents relevant to a given word.
As it relates to searches, the engine will print terms based on how often they appear versus how often other words appear with that word. It will then rank it against other terms that appear frequently on those pages. The higher the TF-IDF score, the rarer the word.
- Term Frequency: How often does the word appear in the document
- Inverse Document Frequency: Weigh the words that occur more frequently (such as and, the, or those that are stop words are usually ignored) and prioritize the unique words that appear commonly throughout the document.
The term frequency inverse document frequency score is one of many topic modeling methods that search engines can use to determine which words and phrases are important for a given topic.
Google’s John Mueller has noted that the TF-IDF corpus draws from across the web (thus making term frequency less specific) and that it is the method used in ALL information retrieval, not just searches. He recommends that businesses should focus on users and create useful content (what is still tbd).
Why TF-IDF Analysis Is Still Relevant
Even with the conversation that TF-IDF is one of many topic modeling methods (and not a very important one) these creators still think there is value in understanding and knowing common words that appear on the web for any topic.
In content optimization, basic keyword insertion doesn’t cut it. Content needs to go beyond keywords to the topical universe in which the words reside. Term Frequency is just one of many tools at our disposal to help SEOs get a clear view of that universe. Check out Mike King’s SearchLove presentation to learn more about search engines and natural language processing.
Content creators can use TF-IDF to understand which pages are relevant to the topic they are trying to create or optimize. TF-IDF also allows writers to examine commonly used words and language to describe a concept or service. It’s not about simple keyword insertion or trying game search, it’s bigger than that. TF-IDF and other topic modeling allow us to see the universe of a topic and build a vocabulary to fully describe that topic.
How to Use TF-IDF
So how can you use TF-IDF as a content optimization and keyword expansion tool? Easy. Fortunately, there are many tools on the market that do the job of parsing pages and extracting TF-IDF scores from common words. We review the pros and cons of the tools below, so be sure to scroll down.
For the most part, each tool allows users to enter a term, select a search engine (Google is the default) and get results from common words and phrases (and their score), based on the document set that includes that term. For the purpose of this search, we use SearchMetrics Content Experiencecontent quality measurement tool .
1. Set Brief & Target Keywords
To get started, create a brief under your project and identify the topic. We created a brief on the topic of TF-IDF to analyze this blog post for TF-IDF target phrases. (Why not kill two birds with one stone?)
Once your brief is created and available, (SearchMetrics will let you know), you can start analyzing your content.
Your Content Editor is your dashboard for each individual brief. You post your content/text and analyze a number of different factors including:
- Use of Keywords
- Content Element
- Legibility
- Competitor URL
Content Experience is a powerful tool that enables multiple projects, dashboards, and briefings for a team of content creators.
We added this blog post to our brief and already have suggestions based on TF-IDF scores on how to improve.
2. Review Keywords
Based on the topic of your brief and the content in the editor, Content Experience parses documents from URL rankings to see which terms they are using on their pages that are not yours. This is a must-have keyword. This list shows how often you use the term vs how often you should.
It also gives you alternative terms to combine to meet the weights. As with the “frequency of terms” our posts use 3 out of the 7 recommended times. We may include suggestions such as “TF” stands for “Term Frequency” or “Term Frequency and Weighting”.
It also shows you which terms you are over-indexing. For example, we overuse “IDF” and “TF” and want to reduce the terms in favor of some that are underutilized. However, we seem to use the “corpus” “search engine” and “stop words” at the right frequency.
Suggested and additional keywords are glossary of terms that your competitors use but are not as important as must-have keywords. Include a Recommended term where it makes sense and an Additional term where it is the only word that makes sense.
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3. Optimize Your Brief
Based on the recommendations, we updated some mentions of TF-IDF and other related provisions to increase the weight of the post.
Changes made include:
- Converting all TF*IDF instances to TF-IDF (hyphens make a big difference)
- Added “search” wherever “engine” is mentioned by itself
- Using full phrases (reverse document frequency terms) where TF-IDF is overused
As a result, Content Score increased from 79% to 89% and Keyword Coverage increased from 49% to 61%. We did this while maintaining sentence structure and lost only 1 point in Readability. Based on the recommendations in the Content Experience summary, a score of 75% is enough to compete so we landed that post highly optimized at 89%. Also, since this is a technical topic, losing readability points isn’t a huge loss as other metrics like sentence structure, word count and keyword coverage remain unchanged.
It is important to note that these are all recommendations and when optimizing content it is important to write naturally and to be easy to read.
4. Review & Compare Competitor Pages
The competitive advantage that TF-IDF provides cannot be overstated. By looking at the frequency with which a term is ranked against a page, SEOs can see which competitors are optimizing for certain keywords and where they can gain a competitive advantage.
In Content Experience, you can view competitor pages based on your short topic. For TF-IDF, there are 18 pages, many reputable and authoritative sites. So, how does a salt-worthy SEO take advantage of this information?
They click and do a manual analysis of the ranking page! They review this page structurally and thematically to see if anything is missing. Some things to look for include:
- Content length (provided by Content Experience)
- How detailed is that part?
- What information can your company provide that your competitors can’t?
For terms your page doesn’t use often, collect how the page ranks using them.
- Do they have whole, special sections?
- Is there a nomenclature they follow?
We looked at the What Is TF-IDF article on Onely and noted that it provides up-front definitions and explanations (as we did) as well as a Ryte Content Success tool guide.
Ryte’s tool also allows you to compare unpublished content to the targets and competitors. It looks at the text you add to their Content Editor and compares it against the terms related to your target keyword. The yellow dot shows how your content fares against the TF-IDF target for each phrase. The targets are based on the corpus of other pages.
We can also see that Onely mostly distributes frequency evenly across terms, where some other pages over-index on more relevant phrases.
Keep in mind that this information is directional and no number of levers can ensure that your page will rank. Check all relevant pages and read a copy. Find out how comprehensive, well-structured and well-linked their content is and devise a plan to optimize your pages to be on a fair ground with top rankings.
While keyword utilization and TF-IDF are factors in organic ranking, other factors including inbound links, age and authority also play an important role.
5. Find Your Topic Universe
Another great feature of Content Experience is their Topic Explorer. It displays related topics visually in an interactive relationship graph. Click on a term to expand and add it to your current brief.
If you add additional topics, be sure to go back through the process to optimize your content for the right frequency of terms. You can view this topic by:
- Semantic association
- Rating
- Seasonal
- Competitiveness
- Search intent
As you edit your content, using tools to enhance TF-IDF can help you build pages that go beyond just keywords. Your content will consider all the related terms in your topic universe and become stronger and better equipped to compete in the page universe.
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TF-IDF TOOLS
Here are some great tools that help bridge that gap:
SearchMetrics
SearchMetrics Content Experiences is our preferred enterprise tool. This allows for agile content development, optimization, streamlined workflows, and approval processes. TF-IDF is incorporated into the framework and takes estimates from content optimization,
Ryte
OnPage offers a range of optimization tools including their Content Success tool. Their unique TF-IDF offering allows users to see keyword recommendations at a granular level, compare pages at every step as well as copy content into their Optimize tool and see which terms your content lacks.
Link Assistant Website Auditor
A download of the program, Website Auditor comes with a suite of programs including Link Assistant, Rank Tracker and SEO SpyGlass. Website Auditor has a 7 day trial and provides keyword and target page input to provide users with a complete TF-IDF analysis with ratings, page rankings and content recommendations.
SEObility
A free tool for 3 cases, SEObility provides its analysis in graphic format and provides SERP details. It also allows users to view ratings from desktop and mobile.
How do you use TF-IDF in your optimization/content creation efforts?
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