Question 1

What is TF-IDF?

Accepted Answer

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.

Question 2

How is TF-IDF calculated?

Accepted Answer

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.

Question 3

What does the content score mean?

Accepted Answer

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.

Question 4

What extra features does this tool have compared to others?

Accepted Answer

(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).

Question 5

Is my data sent anywhere?

Accepted Answer

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

TF-IDF Content Optimizer

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