What Is TF-IDF?
TF-IDF (term frequency-inverse document frequency) is a statistical formula that measures how important a specific word is within a document, relative to how common that word is across a broader collection of documents.
How It Works
Term frequency measures how often a word appears in a single document. Inverse document frequency measures how rare that word is across all documents in the collection — common words like “the” or “seo” (if every competing page uses it) score low, while distinctive, topic-specific words score higher. Multiplying the two together highlights words that are both frequently used and distinctively relevant to that particular document.
How It’s Used in SEO
Some content optimization tools use TF-IDF-style analysis to suggest topically relevant terms that top-ranking pages use, as a way of gauging whether a piece of content covers a topic as thoroughly as its competitors. It’s a useful directional signal for topical coverage, though it shouldn’t be treated as a strict scorecard to hit — genuinely thorough, well-researched content tends to naturally include the relevant terms TF-IDF analysis would flag anyway.
