One of the important things that we must learn and know is understanding TF-IDF and its role in overcoming competitors. We must all admit that we have done it at least once with a search engine, we type random words that have nothing to do with each other, then we expect Google to bring us exactly what we are looking for, and surprisingly, it really does!
Maybe it is random song lyrics, maybe lyrics from a scene in a movie, maybe parts of a recipe that we do not remember, and maybe other things that are less important or perhaps have no value at all, and despite that, Google succeeds every time in finding the correct result for our search! How?! Have you ever wondered about that?
What is understanding TF-IDF? And how does it work?
How did the search engine reach this kind of intelligence? How was it able to infer random words to related topics?!
- The answer, dear reader, lies in an important statistical factor called TF-IDF.
- The answer is not clear yet?! Let us help you understand what it means and why it is important for SEO workers ?
TF-IDF is a statistical parameter used by Google algorithms; it stands for Term Frequency-Inverse Document Frequency, which is the frequency of occurrence of a single word in a single document, versus the frequency of appearance of documents containing the same word.
TF-IDF is a text analysis model that Google algorithms use to learn more about what lies beyond a keyword. When these algorithms analyze a text or an article, they don’t just focus on the main topic, but rather try to find out which words are semantically relevant to the topic… how often they appear in relation to other topics…
How were these different topics linked together? So that you can understand their importance to these topics, and exploit this importance in improving search engine results and reaching results that are more relevant and specific to the user’s desires.
But how does this work in detail?! Let me simplify it for you by discussing the two parts of the coefficient in detail:
- TF : It is an abbreviation for Term Frequency, and it refers to the process in which the frequency of a word is calculated in a single text or document. For example, if we say that there is a text that Google wants to analyze, and it includes this sentence:
Ziyadi is used in facial and hair recipes.
Here Google will divide this sentence into individual words, ignoring all prepositions and extra letters, so that the sentence is analyzed as follows:
Yogurt uses for face and hair recipes
Here we will deal with the text as consisting of only one sentence, and when analyzing it we will find that each of the previous words is repeated once. However, if this sentence is part of a large article consisting of many sentences and words, then the word frequency rate will be calculated using the following equation:
Term Frequency = Frequency of the word in the text / Total number of words in the text.
If we say that the word “yogurt”, for example, is repeated in the text 50 times, and the total number of words in the text is 500, then the FT will be the result of dividing 50 by 500, so that the result is 0.1.
- IDF : is an abbreviation for Inverse Document Frequency, which refers to the rate at which a word appears in relation to a set of different documents or texts. This term differs from the previous one, in that here we do not focus on the rate at which the word is repeated in the original text only, but the algorithm analyzes its appearance in other articles and texts that are not necessarily similar.
- Then it comes out with a result about the semantic relationships of this word and the topics related to it. For example, if we apply the same previous example to a group of different topics such as: beauty, health, hair, food recipes, diet, engineering, architecture, design, we will find, for example, that the word yogurt was repeated in 5 topics out of 7. In order to calculate the IDF rate correctly, the algorithm applies the following equation:
IDF = Total number of texts / Number of texts in which the word appeared
So the result is the result of dividing 7 (the number of texts or topics to which the application is being applied) / 5 (the number of texts in which the desired word appeared), which is 1.4.
By extracting both the frequency of the word in the text, as well as the frequency of the word’s appearance relative to other texts, the algorithm can determine its importance and relevance to topics without applying the following equation:
TF-IDF= TF * IDF
any
The product of the frequency of a word in a single text and the frequency of its appearance in different texts.
If we apply it in the previous example, the value of the word Ziadi will be = 0.1 x 1.4 = 0.14. By comparing it with the rest of the words in the text, the algorithm will create a smart matrix in which the words are arranged according to their extracted value and their significance for the topic, then come up with smart, more significant and specific suggestions for the search results.
In the previous example, we applied a simplified explanation of how TF-IDF works in search algorithms, but this process is very complex and huge as it deals with millions of results per second and a large number of repetitions and appearance rates, but this process in its entirety does not go beyond 5 basic stages that words go through as follows:
- Text preparation stage : This involves revising the text from all extra words and letters.
- Text separation stage : This involves dividing sentences and phrases into individual words and dealing with each word separately.
- Repetition rate calculation stage : by applying the TF rate.
- Stage of calculating the appearance rate : by applying the IDF equation
- The stage of calculating the TF-IDF coefficient is to multiply the frequency of a single word in the text by the frequency of its appearance on different pages.
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How important is TF-IDF for SEO professionals?
Google algorithms are evolving day by day, and this development is subject to many changes in the way the results are issued. Your site, which is currently at the top, may be completely removed from the first page due to a new update. Therefore, a professional SEO worker should not rely on one technique or a specific tactic in his work.
Rather, he must diversify his weapons, so that he is ready to keep up with all developments and keep up with them as well. Relying on focusing on keywords and giving them the greatest attention in work is not the right thing. Rather, you must work in parallel on side strategies such as TF-IDF. Why?! Let us clarify the matter.
When Google analyzes a document and discovers that topic (X) is often related to a set of semantic words, these words are treated as a primary reference to link all other results to the same topic; for example, if we say that topics that deal with SEO are often accompanied by words that appear and repeat a lot, such as: website, search engines, configuration, website, Google, content.
The algorithms will link any page that contains these semantic words to the topic and help it appear in the search results. However, if the page neglects to have this type of words within it, this will represent an obstacle for the search algorithms to link it to the main topic.
In addition to the above, the TF-IDF coefficient acts as a spotlight on the audience behind the scenes; while you focus on your keyword, its primary target audience, and related keywords, the TF-IDF will help you understand your secondary audience, which keywords they use to reach you, and which sub-keywords are generating high traffic for your site without giving them enough attention.
The TF-IDF helps to optimize Google search results to make them more relevant to the topic being searched for, which in turn reflects on the services you provide on your website. The more specialized you are in the services you provide and the more you feed them with the appropriate semantic keywords, the more this will help you to top the search results and appear as a result directly related to the topic.
The TF-IDF also opens up the possibility of more new ideas in writing SEO-friendly content. While the factor shows you a set of words that are not related to the main topic, the SEO expert can analyze these words and find out their relationship to the main topic keyword and extract new ideas for content from them, which he would not have discovered through normal methods.
To do this with a reward, we resort to a group of free and paid tools as follows:
Seobility tool :
- It is a tool that gives you the ability to know the most important semantic words related to a topic with 3 free attempts for the first time without registration, as well as a free subscription for a month upon registration. It in turn helps you know the TF-IDF factor easily, by writing the keyword and specifying the targeted geographic area. The tool supports many SEO services and supports the search process in many languages, including Arabic.
tfidftool tool
- Another tool that helps in calculating the TF-IDF rate easily, as it provides its users with an abundance of search results for related words, and it also gives the user the ability to specialize in the search, so that it extracts results for single, double or triple related words.
In addition to the previous tools that are directly related to extracting TF-IDF easily, semantic keywords can also be extracted using professional SEO tools such as: MOZ, Semrush, and Ahrefsk, as these tools provide the service through similar terms such as Keyword relevancy.
Ultimately, understanding TF-IDF and analyzing its relationship with the primary keyword is the core of a professional SEO strategy, a strategy through which you can not only source the targeted keyword, but also increase the number of visits to your site.
Now that you understand how Google finds your favorite song easily, let us know what other questions you have, and we may answer them in future articles!
