What Is TF-IDF and How Does It Apply to Search Engine Optimization (SEO)?
Search engine optimization (SEO) consists of more than just creating content and building backlinks. It includes technical processes as well, such as keyword analysis. You’ll need to analyze the keywords on your website to ensure they match those for which you want to rank. Rather than focusing entirely on keyword usage, though, you should look at your website’s term frequency, inverse document frequency (TF-IDF).
What Is TF-IDF?
TF-IDF is a keyword analysis system used by search engines to determine the importance and relevancy of a keyword for a given web page. It takes into account two factors: term frequency and inverse document frequency. By evaluating these factors, search engines can assign keyword scores to pages. The more important and relevant a keyword is to a page, the higher its TF-IDF score will be.
Term Frequency vs Inverse Document Frequency
Term frequency is essentially keyword ratio. It’s calculated by dividing the number of times the keyword appears on the page by the page’s total number of words. If the keyword appears 20 times on a page with 500 words, its term frequency would be 4 percent. A high term frequency indicates the keyword is important and relevant to the page, resulting in a higher TF-IDF score for the keyword.
Inverse document frequency is a bit more complex. Search engines calculate inverse document frequency by evaluating the number of times the keyword appears on all pages, including other websites’ pages. But unlike with term frequency, a high inverse frequency doesn’t indicate the keyword is important or relevant.
Keywords that are used many times and on many different pages are often unimportant and irrelevant. Article words like “the” and “a,” for instance, are found on most pages. Because they are so common, search engines consider them to be unimportant and irrelevant. With inverse document frequency, search engines can identify overly common keywords. If the page contains a common keyword, it will receive a lower TF-IDF score for that keyword.
How Search Engines Use TF-IDF
Search engines use TF-IDF as part of their ranking algorithms. All rankings revolve around keywords. A page may rank high for a given keyword, and it may rank low or not all for a different keyword. While they evaluate hundreds of factors when ranking pages for a keyword, search engines will consider TF-IDF scores.
A high TF-IDF score indicates the keyword is important and relevant to the page. More specifically, it means the keyword appears on the page many times, and the keyword is not a common word found on millions of other sites. Therefore, pages with a high TF-IDF score usually rank higher in the organic search results than those with a low TF-IDF score.
Search engines use TF-IDF for natural language processing as well. With natural language processing, search engines will evaluate the context in which words are used on a page. TF-IDF allows search engines to distinguish between important and unimportant words. Search engines may come across a keyword used dozens of times on a page, but if the keyword has a high inverse document frequency, they’ll deem it unimportant. Search engines can then process the page’s words more accurately to determine what the page as a whole is about.
Tips on How to Optimize Your Website for TF-IDF
You can optimize your website for TF-IDF in several ways. When choosing keywords for which to optimize your website, focus on low-competition keywords. Low-competition keywords, by definition, aren’t found on as many pages as high-competition keywords. With fewer pages featuring them, they tend to have a low inverse document frequency, which leads to higher TF-IDF scores.
To find low-competition keywords, try using Google Ads Keyword Planner. Available in the pay-per-click (PPC) network of its namesake, it will reveal the average competition a keyword has among the network’s advertisers. Google Ads Keyword Planner won’t reveal exactly how many times the keyword appears on all pages. Rather, it will reveal whether the keyword has low, medium or high competition.
Assuming you have an advertiser account at Google Ads, you can access the network’s Keyword Planner under the “Tools and Settings” menu. Just enter a keyword that you are thinking about targeting to see how much competition, as well as search volume, the keyword has.
Creating concise website content will typically result in higher TF-IDF scores. Whether it’s an article, a blog post or a product description, pages with concise content often have higher TF-IDF scores than their counterparts. Concise content is characterized by straightforward text in which each word serves a purpose. If you can remove a word from a sentence without changing the sentence’s meaning, there’s no point in keeping it. By taking out unnecessary words such as this, you’ll create more concise content with a higher term frequency for your target keywords.
Limiting the use of common words on your website can improve your site’s TF-IDF scores. Common words are essential for legible, easy-to-read content. For some certain places on pages, though, you can remove them. For titles and subheadings, you can remove many common words. Removing common words from places such as these will increase your website’s term frequencies for its target keywords.
Don’t make the mistake of using keywords too many times for the sake of improving your website’s TF-IDF scores. Since term frequency affects TF-IDF scores, you may assume that the more times you use a keyword on a page, the higher the page will rank for it. A high term frequency may lead to a higher TF-IDF score, but search engines have safeguards to prevent manipulation such as this.
There’s currently no way to accurately calculate TF-IDF scores. You can calculate a page’s term frequency for a keyword simply by analyzing its keyword ratio, but you can’t calculate its inverse document frequency. Inverse document frequency takes into account how many times a keyword appears on all pages across the entire internet. Nonetheless, you can still optimize your website for TF-IDF by targeting low-competition keywords, limiting the use of common words and creating concise content.
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