Word Frequency Counter
Content editors need to know whether a blog post actually uses its target keyword more than “the” or “and”, and whether any word is used so often it reads as a tic. This counter processes your text, filters stopwords by language, optionally stems word variants together, and lists every content word with its count and percentage of total words. Copy the top 50 into a spreadsheet and you have a respectable text-analysis report.
How to count word frequency
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Paste text
Anywhere from a tweet to a book chapter. Longer is more informative.
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Configure filters
Stopwords per language, min length, ignore digits, stemming on/off.
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Process
Tool tokenises, normalises case and counts each token.
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Browse ranked results
Table of word, count and percentage, sortable and exportable as CSV.
What the output looks like
For a typical 1500-word blog post, stopwords filtered, stemmed:
| Rank | Word | Count | % of total |
|---|---|---|---|
| 1 | marketing | 47 | 3.1% |
| 2 | campaign | 38 | 2.5% |
| 3 | audience | 29 | 1.9% |
| 4 | 27 | 1.8% | |
| … |
If the word you wanted to rank for is not in the top 5, the post is probably not focused enough on that keyword. If a word you did not intend sits at the top, you have an unconscious verbal tic.
SEO keyword density targets
Current SEO guidance is forgiving compared to 2015:
- 1-2% for the primary keyword is plenty.
- 0.5-1% for secondary keywords or LSI terms.
- Over 3% risks being flagged as keyword stuffing, especially if the term feels unnatural in context.
- The target keyword should appear in the title, H1, first paragraph and at least one subhead, regardless of body density.
Quality beats density by a wide margin in 2026 rankings.
Stemming and lemmatising
- Stemming (Porter algorithm) chops word endings:
running,runs,ran→run. Fast, but produces non-words sometimes (happily→happili). - Lemmatising returns dictionary forms:
better→good. More accurate but requires a language dictionary.
The tool supports Porter stemming for English and basic stemming for Romance languages. Lemmatising is available only for English.
Use cases beyond SEO
- Editing for overuse. Spotting “just”, “really”, “actually” as filler in first drafts.
- Reading level. High-frequency content words indicate vocabulary reuse; a thesaurus pass flattens the distribution.
- Content audits. Running frequency across 100 blog posts shows which topics you cover more than you realise.
- Academic writing. Checking whether a thesis statement’s key concepts actually recur through the chapters.
Frequently Asked Questions
A word counter gives totals: 1,500 words in your piece. A frequency counter gives per-word counts: “marketing” appears 47 times. Different tools for different questions.
For SEO density analysis, stemming usually reflects what search engines do better — Google matches “run”, “running” and “ran” as related. For literary analysis, exact forms preserve stylistic choices.
By default no. Toggle “include stopwords” if you want the raw distribution including “the”, “and”, “of”. That view is useful for readability but distracting for content analysis.
No. All tokenisation and counting happens in your browser.