TF-IDF is a statistic that is used to determine how important a word is to a document. TF-IDF serves as an analyst for finding the importance of tabular information as compared to all. Balancing term frequency (TF) with inverse document frequency (IDF), helps determine which keyword will be used to fetch the most relevant results. It helps in making sure common words won’t overwhelm the search.
Formula TF-IDF
TF-IDF(t,d)=TF(t,d)×IDF(t)\text{TF-IDF}(t, d) = \text{TF}(t, d) \times \text{IDF}(t)TF-IDF(t,d)=TF(t,d)×IDF(t).
Where.
TF (Term Frequency) – Measures how often a term appears in a document.
IDF (Inverse Document Frequency) – Reduces the weight of common terms by calculating: IDF(t)=log(NDF(t))\text{IDF}(t) = \log \left(\frac{N}{DF(t)}\right)IDF(t)=log(DF(t)N) (Where N = total documents, DF(t) = number of documents containing the term.).
Importance of TF-IDF in SEO & Search
- Search algorithms over the years have used these to obtain and process documents.
- Helps in researching and optimizing content with the use of keywords and score relevancy.
Nowadays, algorithms are also found in chatbots, recommend systems, text classification, and more. Well, they also have other uses as mentioned below.
TF-IDF & Google Rankings
- Google Does Not Use it as a Direct Ranking Factor – More Advanced Systems Used.
- You cannot overuse keywords for SEO. It will be keyword-stuffed so it will get penalized.
- It’s best to give up manipulating term frequency and focus on a natural and high-quality article that satisfies user intent.
Although TF-IDF is still an important concept, it is no longer optimal as a ranking algorithm for search engines.
