What does TF mean in text? Over time, Google’s algorithm has dramatically changed, and Google is now able to provide the most relevant and important results by interpreting the searchers’ intent rather than relying on specific keywords. Therefore, as a content creator, you should work towards uncovering more relevant terms, keywords, and topics based on what Google deems as relevant content.
According to Google’s algorithm, the more relevant terms and keywords are determined by how frequently a term appears in a document, otherwise known as Term Frequency (TF). However, relevance doesn’t increase proportionally with term frequency. Therefore, the frequency weight of the term, Term Frequency- Inverse Document Frequency (TF-IDF) is often used to determine its relevance.
In this article, we’ll learn about term frequency and its role in on-page search engine optimization.
What Is Term Frequency- Inverse Document Frequency TF-IDF?
Term frequency-inverse document frequency (TF-IDF) is a statistical method used to measure the importance of a term or word in a document or collection of documents. It is widely used in information retrieval and natural language processing to evaluate the relevance of a document to a search query.
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The basic idea behind TF-IDF is to evaluate the importance of each term in a document depending on how frequently the term appears in a document (term frequency-TF) and how rare it is across all documents in the corpus of documents (Inverse Document Frequency-IDF).
TF-IDF algorithm purports that terms or words that are more common in a document are generally less important or less informative, while terms that are less common in the document but more common in the corpus of documents are considered more important or more informative. This is because terms that are more common in the document are likely to be less specific to the document’s content, while those that are less common in the document but more common in the collection of documents are likely to be more specific to the document’s content.
When used for SEO purposes, TF-IDF helps you look past keywords and into relevant content that should reach your audience.
For example, if the word “curtain” appears frequently in a document about curtains, it is likely to have a low TF-IDF score since it is common in the document but may not be very specific to the content of the document. On the other hand, if the word “window treatment” appears less frequently in the document but is more common in a collection of documents about curtains, it’s likely to have a higher TF-IDF score since it’s less common in the document but more specific to the content of the document.
Term Frequency (TF)
Term frequency is how frequently a term appears within a document. According to SEO best practices, a keyword should appear in the content at least four times. But frequency alone doesn’t improve your SEO. Instead, successful SEOs divide the frequency by the total word count to understand the keyword density.
Inverse Document Frequency (IDF)
The inverse document frequency increases the value of unique words and lesser-used terms while lowering the value of highly-common words. For instance, if you assemble a word cloud of the top words on any page, the top results would be common meaningless words (e.g., at, you, for, is, and) that wouldn’t provide actual insight into the page.
Therefore, IDF devalues these common terms and assigns more value to unique terms so you can get an idea of which terms carry the most weight.
How to Calculate the TF-IDF Score of a Term
To calculate the TF-IDF score of a term in a document, multiply the term frequency (TF) and the inverse document frequency (IDF) values for the term.
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The TF of a term in a document is calculated by dividing the occurrences of the term in the document by the total number of words in that document. For instance, if the word “curtain” appears 4 times in a document with 100 total words, the term frequency for the word “curtain” in the document would be 0.04.
On the other hand, The IDF (inverse document frequency) of a term is calculated by finding the logarithm of the number of documents in the collection divided by the total number of documents containing the term. For example, if there are 900 documents in a collection and 40 of them contain the word “curtain”, the IDF for the term “curtain” in the collection would be log(900/40) = 1.35.
The TF-IDF score for a term in a document is then calculated by multiplying the IDF and TF values. From our example, the TF for the word “curtain” in the document is 0.04, and the IDF for the word “curtain” in the collection of documents is 1.35. Therefore, the TF-IDF score for the word “curtain” in the document would be 0.054
Roles of TF-IDF in On-Page Optimization for SEO
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When talking about TF-IDF in SEO, the aim is to use the tools of this analysis to make specific website texts as unique as possible. Because of their uniqueness, search engines can then place these texts with their specific keywords at the top of the SERPs (Search Engine Result Pages).
Content creators can use TF-IDF to identify gaps in their content based on the content currently ranking at the top in the search results. Also, they can use it when creating new content so that content ranks higher faster.
1. Keywords Research
The TF-IDF enables you to enrich your keyword research by adding keywords people search for and keywords that Google found to appear quite often in search results.
The most widely used terms from your competitors’ content may also spur new ideas into your content planning and keyword research, especially when you want out-of-the-box thinking and inspiration.
2. Content Ranking in Search Engine
In search engines, you can use TF-IDF to determine a web page’s relevance to a specific search query. TF-IDF helps determine the importance of a particular word by considering its frequencies within a document and the entire collection of documents. This ensures that common, less important words are not given much weight in the ranking process, and one can easily identify the most important words and phrases.
When you want to use a TF-IDF tool for SEO, it will check pages that rank on Google for your keyword, analyze their content, and compare your content statistics to those of your competitors. TF-IDF analysis will then give you suggestions of terms related to your keyword. You can then use these words and phrases to refine your content and expand on angles your content might have missed earlier to improve its visibility in the search engine.
The information from a TF-IDF analysis can also give you a hint at the topics you haven’t covered yet, but Google deems them important. This way, you can have more clarity to create high-quality content and rank higher on the search engine result pages.
However, the connection between the new TF-IDF suggestions and your topics should be a natural addition to your content, not just keyword stuffing. After all, stuffing keywords would sometimes make the content lose its meaning and be out of context.
Disadvantages of TF-IDF
Like any statistical measure, the TF-IDF has some limitations you should consider when deciding whether to use it for a specific task.
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First, TF-IDF does not take into account the meaning or context of terms beyond their frequency in a document or collection of documents. This can lead to words being given high TF-IDF scores even if they aren’t particularly important or relevant to the content of the document or collection of documents.
Additionally, the TF-IDF is sensitive to the size of the document or collection of documents being analyzed. The TF-IDF scores of words are lower in larger documents or collections and higher in smaller documents or collections. This makes it difficult to compare the TF-IDF scores of terms across different documents and collections of different sizes.
Finally, TF-IDF can be less effective when the documents or collection being analyzed are not representative of the content or language of the task at hand. For instance, if all the documents in a collection are about a different topic or written in a different language from the task being performed, the TF-IDF may not accurately identify the most relevant or important words or terms.
Conclusion on TF-IDF
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As a digital marketer, content is critical to everything you do. One of the most important factors in SEO ranking is providing high-quality, relevant content that’s valuable and useful to users. Moreover, the TF-IDF analysis aims to provide information during content creation on which other terms a document should contain to be as unique as possible. It focuses on creating and promoting relevant content that will always be key to achieving your SEO success.
Jacky Chou is an electrical engineer turned marketer. He is the founder of Indexsy, Far & Away, Laurel & Wolf, a couple FBA businesses , and about 40 affiliate sites. He is a proud native of Vancouver, BC, who has been featured on Entrepreneur.com, Forbes, Oberlo and GoDaddy.
