Measure keyword density for SEO
Find the most frequent unigrams, bigrams, and trigrams. Strip stopwords to surface meaningful terms only.
How to find keyword density
Tokenizes the text, optionally drops stopwords, and counts n-grams ranked by frequency.
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Paste article
Long-form prose works best — at least a few paragraphs.
Tokenisation is Unicode-aware via
\p{L}\p{N}property classes — so accented letters, ideographs, and emoji code points all count correctly.textUTF-8Unicodecode pointsliveinput · textraw · UTF-8Search engine optimization…
loaded · UTF-8UTF-8 · ready - 02
Pick n-gram
1-gram = single words, 2-gram = pairs, 3-gram = phrases.
Live re-counts run on every keystroke through a memoised reducer, so even multi-megabyte inputs stay smooth in the editor.
modelivememoisedoptionssnappyn · options2 · live2-word
options applied · livelive - 03
Strip stopwords
Removes 'the', 'a', 'and', etc. so only meaningful terms surface.
Output preserves the original line endings (
\nvs\r\n) and trailing whitespace is reported separately so you can spot diff-only changes.cleanline endingswhitespacediff-cleanreproducibledrop · countedthe/a/and · readystopwords off
counted · livecounted - 04
Top 30
Highest-density terms shown first. Red = over 5% (often stuffing).
Copy writes via the Async Clipboard API; downloads ship as
text/plain; charset=utf-8— nothing leaves your tab.rankclipboardtext/plainblobclient-side✓ · copiedranked · done✓ Copied TSV
ready · ready to shipsuccess
Text-processing fundamentals worth knowing
Tokenise with Unicode classes
Use \p{L}\p{N} with the u flag so accents, ideographs, and emoji count properly.
Normalise line endings
Convert \r\n to \n before diffing or hashing — Windows-vs-Unix endings produce phantom changes.
Mind invisible characters
Zero-width joiners, BOMs, and non-breaking spaces look identical to ASCII space. Strip them before comparison or storage.
Stream don’t slurp
For multi-megabyte text, iterate line-by-line rather than loading everything to memory — keeps the tab responsive.
Choose collation deliberately
Default JS sort is code-point order, not human alphabetical. Use Intl.Collator for locale-aware sorting.
Re-run after normalisation
NFC vs NFD changes byte length but not visible glyphs. String.prototype.normalize() before counts and comparisons.
When to measure density
SEO, editing, content audits.
SEO audit
Check if a target keyword appears at 1-2% density. Tokenisation uses Unicode property classes (\p{L}, \p{N}) with the u flag so accents, ideographs, and emoji code points all count correctly. Surrogate-pair-aware counts mean 🚀 weighs 1, not 2.
Unicode-correctAvoid stuffing
Detect when a word goes over 5% (search-engine warning). Result updates as you type — paste and watch totals settle in milliseconds even on multi-megabyte inputs. Heavy work runs through a memoised reducer so the editor never janks.
diff-cleanTag discovery
Find n-grams worth promoting to tags. Output normalises line endings to LF for clean diffs across platforms. Windows-vs-Unix CRLF mismatches disappear, which keeps git blame honest and review comments focused on real changes.
instant countsStyle audit
Surface overused phrases for editorial review. Code-point counts are stable across NFC/NFD normalisation. Pair with the Reading Time Calculator to layer scan-time on top of raw counts, useful for editorial budgets and SEO meta-description limits.
platform-awareTopic modeling
Quick proxy for the main themes in a doc. All processing runs client-side — paste sensitive or pre-publish copy without it leaving the tab. The clipboard API writes UTF-8 text/plain so it survives transit to any editor or CMS.
code-point-accurateCompetitor analysis
Compare keyword density across rival articles. Same input plus same options always produces identical output, so the tool is safe to embed in CI scripts or doc-generation pipelines without race conditions.
losslessFrequently asked questions
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