Free tool · SEO
TF-IDF tool.
Paste your text and a competitor's. Every term comes back with its weight in each, and the terms only they cover come first. Everything stays in your browser.
- Free
- Instant access
- Runs in your browser
- Unlimited texts
- Competitor terms
- 25
- Missing in yours
- 17
- Weaker in yours
- 3
- Only in yours
- 11
- Coverage
- 3232%
| # | Term | Competitor | Yours | TF-IDF weight | Status |
|---|---|---|---|---|---|
| 1 | snippet | ×3 | - | 0.124 | Missing High |
| 2 | search | ×2 | - | 0.083 | Missing High |
| 3 | intent | ×2 | - | 0.083 | Missing High |
| 4 | pixel | ×2 | - | 0.083 | Missing High |
13 more missing terms, then the shared and the unique ones. Load the example to see the full report.
Paste both texts to compare them. The report needs yours and the competitor's.
- Competitor terms
- Missing in yours
- Weaker in yours
- Only in yours
- Coverage
| # | Term | Competitor | Yours | TF-IDF weight | Status |
|---|---|---|---|---|---|
| | Missing Weaker in yours Covered Only in yours |
How to read the report.
- 01
Missing first
The default view lists the terms in the competitor's text that are absent from yours, heaviest first. Each one is a word the page that ranks found worth using. Read them as subtopics to cover.
- 02
Present, but weaker
A term you use once and they use six times is a gap too. The Weaker filter ranks shared terms by how much more weight they carry on their side. Those are the sections to deepen.
- 03
What only you cover
Terms that appear in your text alone are either your angle or your noise. Keep the ones that serve the query. The rest marks where you drifted off the subject.
What counts is coverage.
TF-IDF weighs a term by how often it appears in a text, discounted by how common it is across the texts compared. With two texts the discount is small and the reading is plain: a term that weighs a lot for the page that ranks and is absent from yours points to a subject left to cover.
Google stopped scoring pages this way years ago. The tool is still useful because the pages that rank for a query tend to agree on the vocabulary the query needs, and the fastest way to see that vocabulary is to compare. Treat the list as a brief: each missing term is a question your page has yet to answer.
Run it against each of the top three results. A term all three use and yours lacks is a near-certain gap; a term only one uses is their angle. Then write the section around the word: explaining that Google truncates on pixels is what moves the page, far more than repeating "pixel" four times.
Questions
- What is TF-IDF in SEO?
- TF-IDF stands for term frequency-inverse document frequency. It weighs a term by how often it appears in your document against how rare it is across a set of documents. A word used everywhere scores low; a word your competitors use repeatedly and that is absent from your page scores high. In SEO it is used to surface the vocabulary a topic requires.
- How do I use a TF-IDF tool for content?
- Paste your draft, add the pages currently ranking for your target query, and read the terms that score high for them and are absent from yours. Those are your gaps. The point is to spot the subtopics your draft has yet to cover, which is what the missing words usually represent, and to write those sections.
- Is TF-IDF still relevant now that Google uses AI?
- As a diagnostic, yes. Google moved away from weighting TF-IDF directly long ago, so its value today lies in analysis: it is a fast, cheap, explainable way to find the concepts a thorough page on your topic covers. Treat the output as a research brief.
- What is the difference between TF-IDF and keyword density?
- Keyword density counts repetition inside one text. TF-IDF compares your text against others and shows what is missing, which tells you about the topic. Density is a guard against overuse; TF-IDF is a guide to coverage.
- How is the weight calculated here?
- Both texts are lowercased, split into words, and stripped of stop words and numbers. Term frequency is the share of a text's remaining words that a term accounts for. With two texts, the inverse document frequency is ln(3 / (1 + number of texts containing the term)) + 1: about 1.41 for a term in one text, 1 for a term in both. The weight is the product. Priority is High above 0.01, Medium above 0.005, Low below, which separates a term used once on a long page from one used all the way through it.
- Is this TF-IDF tool free?
- Yes: free, unlimited and ready to use right on this page. Everything is computed locally in your browser, so everything you paste stays on your device.
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One competitor is a comparison. Ten is a strategy.
Tonaily maps your competitors' keywords and pages before an article is written, and says which gaps are worth filling first. Connect your site and see where you stand.
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