How does Google use the TF-IDF calculation?
Posted: Sun Feb 16, 2025 10:31 am
In Google's case, the TF-IDF calculation helps the search engine emphasize the terms and phrases in the content of sites and blogs that really matter for indexing and ranking.
It is worth remembering that Google uses a robot to crawl the contents of the web , so it does not have the human capacity to understand the meaning of words and the context of the content. Or rather, today it already knows how to do that, thanks to technology that allows it to increasingly approach human intelligence.
The TF-IDF calculation is an example of robot-based technology for language processing. Google adopts systems that perform these calculations automatically on millions of Internet documents to make sense of what they say.
TF-IDF is used as part of Latent Semantic Indexing ( LSI ). Google uses this indexing approach to understand the relationships between words, phrases and concepts, i.e. the semantics of the texts on a website or blog.
This is essentially important when there are words with similar meanings (synonymy) or with more than one meaning (polysemy).
Do you remember the time when websites repeated the same keyword they wanted to rank for thousands of times?
To avoid this type of black hat practice , called keyword stuffing, which is detrimental to the user experience, Google adopted LSI. This way, the search engine has more intelligence to value quality content for the visitor.
Within this logic, then, the TF-IDF serves to indonesia phone number list process the language used in the content. It does not serve to give meaning to the terms, but to understand their importance and give them different weights.
Before that, Google only considered keyword density, which is a fairly common concept in SEO, but which only analyzes the frequency of the term on the page, without evaluating its relevance.
Thus, the word "that" could be understood as relevant in a post about "Content Marketing", since it usually appears quite often.
TF-IDF then fine-tunes that calculation to understand the importance of the term by comparing its frequency on the page to its frequency in thousands of other documents. That way, Google can refine indexing quality for the right keywords.
In this way, when the user does a search on Google, it will know which pages are most valuable for their query, taking into account other factors for positioning , of course.
It is worth remembering that Google uses a robot to crawl the contents of the web , so it does not have the human capacity to understand the meaning of words and the context of the content. Or rather, today it already knows how to do that, thanks to technology that allows it to increasingly approach human intelligence.
The TF-IDF calculation is an example of robot-based technology for language processing. Google adopts systems that perform these calculations automatically on millions of Internet documents to make sense of what they say.
TF-IDF is used as part of Latent Semantic Indexing ( LSI ). Google uses this indexing approach to understand the relationships between words, phrases and concepts, i.e. the semantics of the texts on a website or blog.
This is essentially important when there are words with similar meanings (synonymy) or with more than one meaning (polysemy).
Do you remember the time when websites repeated the same keyword they wanted to rank for thousands of times?
To avoid this type of black hat practice , called keyword stuffing, which is detrimental to the user experience, Google adopted LSI. This way, the search engine has more intelligence to value quality content for the visitor.
Within this logic, then, the TF-IDF serves to indonesia phone number list process the language used in the content. It does not serve to give meaning to the terms, but to understand their importance and give them different weights.
Before that, Google only considered keyword density, which is a fairly common concept in SEO, but which only analyzes the frequency of the term on the page, without evaluating its relevance.
Thus, the word "that" could be understood as relevant in a post about "Content Marketing", since it usually appears quite often.
TF-IDF then fine-tunes that calculation to understand the importance of the term by comparing its frequency on the page to its frequency in thousands of other documents. That way, Google can refine indexing quality for the right keywords.
In this way, when the user does a search on Google, it will know which pages are most valuable for their query, taking into account other factors for positioning , of course.