TF-IDF They usually

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The TF-IDF then adjusts 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 the quality of indexing for the right keywords. In this way, when the user does a search on Google, they will know how to indicate the most valuable pages for their query, considering other factors for positioning , of course. How can this on-page optimization approach contribute to your blog? After knowing what TF-IDF is, you might be wondering: ok, but how can that help my  strategies.


Firstly, knowing the logic of the TF-IDF is important to understand how Google Gansu Mobile Number List works and how it is evolving over the years. This is the first step in establishing your  strategies, according to the latest algorithm updates. Unfortunately, we do not have access to the exact calculations that Google makes on your blog. That is a secret very well kept by the search engine algorithm. The good news is that there are tools that do the TF-IDF calculation for the term and  you define , compared to other well-ranked websites in search engines. In this way, the TF-IDF approach can be used in practice, in your on-page optimizations, which you can now do using the same Google logic.





With these tools you can: search for keywords (identify which terms and topics are vital to a topic) analyze the competition (identify which terms weigh more so that your competition is ahead in the ranking); Semantically optimize new content or old publications (identify vital keywords for the topic and introduce them naturally into the content). Ryte , bility and Link Assistant are some of the tools that work with TF-IDF. They usually work in the following way. First you enter a  and the keywords (or just one) that you want to rank for. The tool checks the top-ranked pages on Google for those keywords, analyzes the content and does the TF-IDF calculation on all terms to identify the most relevant ones. That way, you get a list of related keywords , also known as co-occurrences.