Keyword Extractor — Find Top Keywords & Phrases in Text
Extract top single-word and multi-word keywords from text using TF-IDF / RAKE. Free, in-browser, no upload.
About Keyword Extractor
A keyword extractor analyses a body of text and surfaces the most informative words and phrases — the ones that distinguish the document from generic English — using algorithms like TF-IDF (term frequency–inverse document frequency) or RAKE (Rapid Automatic Keyword Extraction) to weigh significance. The ZTools Keyword Extractor runs entirely in the browser, supports single-word and multi-word phrase extraction, configurable stopword filtering (English / Spanish / French / German), n-gram range (1-3 words), and outputs a ranked list with relevance scores.
Use cases
- SEO content audit. Paste a draft blog post; extractor surfaces the dominant keywords. Compare to your target keyword — is the post actually about what you intended?
- Document summarisation. A long report or article. Top keywords tell you the gist in 30 seconds without reading the document.
- Tag suggestion for a CMS. Extract candidate tags for blog posts or knowledge-base articles. Beats human-only tagging consistency for large content libraries.
- Theme analysis across documents. Paste each document separately; compare top keywords to see which themes recur and which are unique.
How it works
- Paste text. Single document. For multi-document analysis, run each separately and compare.
- Pick algorithm. TF-IDF: best with a reference corpus. RAKE: works on a single document; favours phrases over single words.
- Configure. Stopword language, n-gram range (1 = single words, 2-3 = phrases), max results.
- Tokenise + score. Lowercase, drop stopwords, tokenise into words / n-grams, score by chosen algorithm.
- Export. Ranked list with score + frequency. Plain text or CSV.
Examples
Input: 500-word article on machine learning
Output: Top keywords: "machine learning", "neural network", "training data", "model accuracy". Ranking by score.
Input: Product description
Output: Top: "ergonomic", "adjustable height", "lumbar support" — surfaces the standout features.
Input: Multi-paragraph travel review
Output: Top: "boutique hotel", "rooftop bar", "old town", "walking distance". Captures the experience.
Frequently asked questions
TF-IDF vs RAKE — which to use?
TF-IDF needs a corpus to compute IDF (e.g. all documents on your blog) — surfaces what makes this doc unique. RAKE works on one doc — surfaces dense phrases. RAKE for one-off; TF-IDF for cross-document analysis.
Should I use single words or phrases?
Phrases (2-3 words) are usually more informative ("machine learning" beats "machine" alone). Single words help for very short texts.
Stopwords — what are they?
Common words (the, is, of, and) that appear everywhere and provide little discrimination. Removed before scoring. Custom stopword lists let you remove domain-specific noise.
How accurate is keyword extraction?
Algorithms are fast and language-agnostic but miss semantic synonyms (AI ↔ artificial intelligence). For deep semantic analysis, switch to embedding-based tools.
Is the input uploaded?
No — client-side only.
Why do some "obvious" keywords miss?
If they appear too uniformly across a corpus (TF-IDF) or lack co-occurring related words (RAKE), score is low. Tune algorithm parameters or supply a different corpus.
Pro tips
- Always check the top 10 — they tell you what your document is "really" about, which often differs from your intent.
- Use 2-3 word phrase mode for blog and product content; single-word mode for short texts.
- Remove brand names from stopwords if you want them as keywords; add them as stopwords if you want underlying-topic analysis.
- For SEO, compare extracted keywords to your target search keyword. If they don't match, the article needs editing.
- Run on chunks (per section) for long documents — global keywords miss local themes.
Reviewed by Ahsan Mahmood · Last updated 2026-05-05 · Part of ZTools.
For the full,
formatted version of this page, please enable JavaScript and reload
https://ztools.zaions.com/keyword-extractor.