Extract keywords from text with frequency scoring
Yes, completely free. Extract keywords from text of any length — articles, blog posts, product descriptions, or academic papers.
No. All text analysis is performed locally in your browser. Your content stays private on your device.
TF-IDF (Term Frequency × Inverse Document Frequency) identifies words that are both frequent in the given text AND distinctive (not common across all documents). TF measures how often a word appears in your text; IDF down-weights common words ("the", "and", "is") that appear in almost all documents. The result ranks words that are characteristic of your specific content. For best results: (1) Use longer texts — at least 300–500 words for meaningful keyword extraction. (2) Remove boilerplate content (navigation, footers, ads) before extraction — these introduce noise. (3) Compare keyword lists from your content versus competitors' content to find topic gaps. (4) Use extracted keywords for SEO meta tags, content briefs, and topic clustering. The tool also identifies n-grams (2–3 word phrases) which often make better SEO keywords than single words — "project management software" is more valuable than three separate words. For SEO, target keywords with moderate competition and clear user intent.