Free Word Frequency Counter Online
Paste your text below to instantly see your top 20 most frequent words. Stop-word filter on by default. Export to CSV with one click.
A word frequency counter shows which words appear most often in a piece of text. Notepad AI's counter ranks the top 20 words, filters common stop words (the, a, is) by default, and lets you export results as CSV. Everything runs in your browser — no uploads.
Common Use Cases
SEO keyword analysis
Spot over-used or missing keywords in your blog post or page copy.
Text characterization
Identify the central themes of any document by its top terms.
Writing style review
Detect overused crutch words you may want to vary.
Academic / research
Quick frequency analysis for corpus studies or content audits.
Related Tools
Word Frequency FAQ
How does the word frequency counter work?
It tokenizes your text into words, normalizes them to lowercase, and counts occurrences. By default it filters common English stop words (the, a, is, etc.) so meaningful terms surface to the top. Toggle the filter off if you want raw counts including stop words.
What are stop words?
Stop words are extremely common words (the, a, an, is, are, of, in, etc.) that appear in almost every sentence but carry little semantic weight. Filtering them out shows you the words that actually characterize your text.
Is the word frequency counter free?
Yes — completely free, no signup, no login, no limits on text length.
Is my text private?
Yes. All analysis happens in your browser. Nothing is uploaded, logged, or tracked.
Can I export the results?
Yes. Click 'Copy as CSV' to get a comma-separated word-count list you can paste into Excel, Google Sheets, or any spreadsheet tool.
What's keyword density and how does this help SEO?
Keyword density is the percentage of times a target keyword appears relative to total word count. Most SEO guides suggest 1-2% for primary keywords. This tool shows raw counts — divide by total word count to compute density.
Why are some inflected words counted separately?
The counter doesn't stem words (so 'run', 'runs', and 'running' count separately). This is intentional — stemming requires language-specific rules and can confuse counts in technical or multilingual text.
How accurate is it for non-English text?
It works for any language that uses whitespace and Latin-character word separation. The stop-word filter is English-only — toggle it off for non-English text.
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