Definition
TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical method used in information retrieval and SEO to evaluate the importance of a word in a document relative to a set of documents. TF measures the term frequency in the document, IDF measures its rarity across the corpus. A term frequent in a document but rare in the corpus has a high TF-IDF, indicating significance. In SEO, TF-IDF analysis helps identify important terms to include in content for relevance.
Key Points
- Measures term importance in a document relative to a corpus
- Helps identify essential terms for content relevance
- Tools: Surfer SEO, Clearscope, YourTextGuru use TF-IDF variants
Practical Examples
TF-IDF article analysis
TF-IDF analysis of a link building article reveals that 'domain authority', 'anchor text', and 'link profile' have high scores, indicating they should be included.
Competitor comparison
A TF-IDF tool compares your article with Google's top 10 results and identifies missing terms that could improve semantic relevance.
Frequently Asked Questions
Google uses far more advanced methods (BERT, MUM), but TF-IDF remains useful for SEOs. Content optimization tools use TF-IDF variants to recommend terms.
Surfer SEO, Clearscope, MarketMuse, and YourTextGuru use TF-IDF-type analysis to compare content with top results.
Go Further with LemmiLink
Discover how LemmiLink can help you put these SEO concepts into practice.
Last updated: 2026-02-07