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The calculation serves as a term weighting factor , that is, to understand the importance of a specific term or phrase for a certain document. But, since you read the title of this article, you must be wondering: TF- what? So, let's understand what this acronym means. TF-IDF stands for Term Frequency - Inverse Document Frequency . This expression can be translated into Spanish as "Term Frequency - Inverse Frequency of Documents". It's still not very clear, is it? So, let's go in parts. TF refers to "term frequency".


That part of the calculation answers the question: How often does Gansu Mobile Number List the term appear in this document? The greater the frequency of the term in the document, the greater its importance . On the other hand, IDF stands for "inverse document frequency." In this part, the tool answers: How often does the term appear in all the documents in the collection? The higher the frequency in the documents, the lower the importance of the term . The IDF calculation considers which terms are frequently repeated in the texts, such as articles and conjunctions (the, the, the, and, but, that, etc.), and are not relevant to the documents.





Thus, in the case of Google, neither for indexing nor for positioning. Therefore, when the IDF factor is incorporated, the calculation decreases the weight of terms that occur very frequently in the document set and increases the weight of terms that occur more rarely. This diagram will help you understand it better. semrush Source: SEMrush We are not going to go into the details of the statistical calculations ( here you can understand the formulas ). But we can summarize it like this: the importance of the term (TF-IDF value) increases according to the number of times the word appears in the document (TF) . But it is compensated by the number of repetitions in the document collection (IDF), which serves to adjust for the fact that some words appear more frequently overall.