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TF-IDF Content Optimizer

Analyze content using Term Frequency-Inverse Document Frequency (TF-IDF). Compare to competitor content, identify important terms, find missing terms, flag over-optimization, compute content score, CSV export, history, shareable URL. 100% client-side.

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About TF-IDF Content Optimizer

Analyze content using Term Frequency-Inverse Document Frequency (TF-IDF). Compare to competitor content, identify important terms, find missing terms, flag over-optimization, compute content score, CSV export, history, shareable URL. 100% client-side. Everything runs locally in your browser — your data never leaves your device.

How to use

  1. Enter your input in the tool above.
  2. Adjust any options to your preference.
  3. Use the Copy or Download buttons to save the result.
  4. Everything happens locally — your data never leaves your browser.

FAQ

What is TF-IDF?

TF-IDF (Term Frequency-Inverse Document Frequency) scores how important a term is to a document in a collection. Terms that appear frequently in your content but rarely across competitors score high — those are your 'signature' terms. Terms that appear in competitors but not in yours are content gaps to fill.

How is TF-IDF calculated?

TF = (count of term in document) / (total terms in document). IDF = log( (total documents) / (documents containing term) ). TF-IDF = TF × IDF. Stop words are filtered out before scoring.

What does the content score mean?

The content score (0-100) reflects how well your content covers the important terms shared with competitors. A higher score means your content includes more of the terms that competitors collectively use — but watch for over-optimization warnings if your TF-IDF is much higher than competitors.

What extra features does this tool have compared to others?

(1) TF-IDF calculation (TF, IDF, final score). (2) Term importance ranking. (3) Competitor comparison. (4) Missing-term suggestions (terms in competitors but not yours). (5) Over-optimization warnings (terms where your TF is 2x+ the competitor average). (6) Word-cloud data export. (7) CSV export. (8) Content score 0-100. (9) History (localStorage, last 20). (10) Shareable URL. (11) Stop-word filtering (English).

Is my data sent anywhere?

No. All TF-IDF computation runs locally. History is stored in localStorage on this device only.

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