TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic that holds the power to unravel the significance of words within a document corpus.
TF-IDF is a straightforward concept that carries profound implications. At its core, it is designed to evaluate the importance of a word within a document relative to a collection of documents.
Check out our SEOLeverage app to know how your branded and unbranded keywords are performing.
Let us break down the components of TF-IDF to understand it better:
Term Frequency quantifies the number of times a term t (word) appears in a specific document.
It is calculated using a simple formula:
| TF = (Number of times the term appears in the document d) / (Total number of terms in the document) |
A higher TF indicates that a term is more prevalent within the document.
TF helps writers gauge whether they are using a term too frequently or too sparingly in their content. For SEO practitioners, it is crucial to strike a balance between keyword optimization and avoiding keyword stuffing.
Inverse Document Frequency measures the significance of a term across the entire document corpus.
IDF is computed as follows:
| IDF = log(Total number of documents / Number of documents containing the term) |
Words that are common across many documents, like stop words, have a low IDF value, while words that occur rarely will have a high IDF.
This is the product of TF and IDF, which results in a value that reflects how relevant a word or term is within a specific document and across the entire corpus.
| TF-IDF = TF * IDF |
TF-IDF values are one of the earliest methods employed in information retrieval. It serves as a cornerstone for developing more sophisticated modern processing approaches.
Moreover, TF-IDF maintains its widespread utilization across digital libraries, databases, and archives, proving invaluable in finding pertinent documents.
While TF-IDF can be a powerful tool, it is not without its limitations. For instance, it does not capture word semantics or context. Additionally, it treats all words independently, ignoring word order and syntactic relationships.
TF-IDF was a powerful tool used by Google and other search engines to evaluate how relevant a web page is for a certain search query. It helps ensure that common words are given less weight in the ranking process. By optimizing their content for TF-IDF, site owners can improve site visibility in search engine results pages, driving more site traffic.
But recently, the use of the TF-IDF algorithm for SEO has significantly declined. Newer and more advanced techniques have emerged for improving both the accuracy and relevance of search results. Search engines have placed more emphasis on user behavior and engagement.
Our SEOLeverage app's KPI overview feature offers you an easy way to assess how well your SEO efforts meet your goals and objectives.
TF-IDF analysis can be useful for keyword research and content optimization. But TF-IDF is “not a magic bullet" for SEO success. Instead, it is only a piece of the complex SEO puzzle.
Google now uses various techniques for information retrieval. This underscores that TFIDF is just one of many metrics employed by Google to understand how important a word is on web pages. TF-IDF value is not a standalone ranking factor.
So do not rely solely on TF-IDF analysis. Focus more on content quality, relevance, and user experience. Our team of SEO experts here at SEOLeverage can help you, from content creation to brand reputation management.
