Related tools
Why use a keyword extractor?
Skim repeated terms, draft tag ideas, or compare two pastes—without shipping your copy to a server.
Benefits
- Ranked list: see what repeats most as unigrams.
- Controls: top N, minimum length, stop-word toggle.
- Phrases: optional 2–5 word n-grams.
- Export: quick .txt of keyword tokens.
- Private: client-side only.
How it works
Naive bag-of-words and sliding windows—good for exploration, not a substitute for SEO suites or linguistics tools.
What the code does
- Normalize: lowercase; non-\w to spaces; split on whitespace.
- Unigrams: count tokens passing min length; optional English stop list.
- Sort & cap: descending count; keep top N (≤ unique available).
- N-grams: same stream, contiguous n-word windows; rank by count.
- Export: keywords only, newline separated.
How to interpret extracted keywords
- Frequency highlights repetition, not necessarily priority for SEO targets.
- Use n-grams to catch repeated phrasing patterns that single-word lists miss.
- Apply stopword and length filters to reduce noise before downstream review.
When to use
Blog outlines, student summaries, light content QA, and quick “what did I overuse?” checks.
Ideal use cases
- Editing: spot overused words.
- Drafting: phrase echoes via n-grams.
- Teaching: demonstrate tokenization limits.
- Privacy: air-gapped pastes.
- Prep: before specialized NLP.
Facts
Interpretation depends on token rules and language.
Key points
- Stop-word list is English and fixed in code.
- N-gram ranking ignores min-length and stop-word settings used for unigrams.
- High frequency is not the same as topical importance or search intent.
- Very large pastes may hit browser memory limits.
- \w includes letters, digits, and underscore in ECMAScript.
Best practices
Cross-check with your editorial or SEO workflow.
Quality tips
- Clean markup to plain text first for fair counts.
- Try several min-length values to reduce noise.
- Pair with readability or corpus tools for serious analysis.
- Do not treat export lists as finalized keyword strategy.
- For code snippets, identifiers may dominate tokens.
When not to rely on it
- Multilingual stop-word lists or lemmatization requirements.
- Legal, medical, or compliance-grade keyword reporting.
- Exact parity with a specific publisher’s keyword specification.
Limitations and compatibility
English-oriented stop words; heuristic tokenization; requires JavaScript.
Recommended keyword workflow
- Extract keywords and n-grams first to identify dominant terms and phrase clusters.
- Cross-check with readability, analyzer, and word frequency before final edits.
- Validate shortlisted terms with dedicated SEO tools for intent and demand.
For complete content optimization, combine this extractor with analyzer, word frequency, readability checker, and word counter.
Keyword extraction runs fully in your browser with no server upload; keyword rankings and phrase lists update instantly as filters change.
Frequently asked questions
Is this free and private?
Yes. Everything runs in your browser; nothing is uploaded for extraction.
What are stop words here?
A fixed small English list of common words you can filter out so unigrams skew toward content words. It is not customizable in the UI.
Do n-grams use stop-word removal?
No. N-grams are built from all non-empty normalized tokens; only the unigram list uses the stop-word and min-length options.
What does export contain?
Only the visible keyword tokens (one per line). Counts and n-grams are not included in the file.
Will this match Google keyword volume?
No. This is a naive frequency view of your pasted text, not a search-volume or ranking tool.
Does it work for non-English text?
Tokenization follows JavaScript \w rules; stop-word filtering is English-oriented. Results may be less meaningful for other languages.
Why can results differ from SEO keyword tools?
This extractor ranks frequency in your pasted text. It does not include search volume, SERP intent, competition, or semantic clustering from SEO suites.
How should I interpret frequency vs intent?
High frequency can reveal repetition or topical terms, but intent-fit and user demand require external research and editorial judgment.