Keyword Density Checker

Runs in your browser

Analyze keyword frequency and density in your content. Real-time stop-word filtering, bigram analysis, and SEO traffic-light scoring.

Your Content

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Options

Density Guide

Optimal
≤ 2%
Caution
2–4%
High
> 4%

Top Keywords

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Runs entirely in your browser — nothing is uploaded
Runs entirely in your browser. No uploads. Your files stay private.

What Is Keyword Density?

Keyword density is the ratio of a single term's occurrences to the total word count, usually expressed as a percentage. It was a primary on-page signal in the early 2000s, when matching algorithms were largely lexical and a higher count of a phrase could meaningfully shift a ranking. That world is gone — Google's BERT, MUM, and the broader Helpful Content System now score topical coverage and entity relationships, not raw frequency.
The analysis here runs entirely in client-side JavaScript. Your text is split on whitespace and Unicode word boundaries, lowercased, and counted with a Map keyed by token. Single-word frequencies, bigrams (two-word phrases), and trigrams are all derived from the same pass, so even very long articles update in a few milliseconds without leaving the tab.
A built-in stop word list filters function words like the, and, is, of, a, to, in before reporting frequencies. Without that filter, articles and prepositions dominate the top of any English text and obscure the topical terms you actually care about. You can disable the filter if you want a raw distribution for stylometric or readability work.
The classic stuffing threshold is roughly 3-4% for a single term in an English document of meaningful length. Above that, prose tends to read awkwardly to humans, and Google's spam systems may flag the page. Below 0.5% on a long article often indicates the topic is not discussed enough for the target keyword to be the page's clear subject.
Bigram and trigram views surface multi-word patterns that the unigram view hides — phrases like content marketing strategy or local business schema are usually what searchers actually type. These are also the phrases LLM-driven retrieval systems weight heavily when extracting passages for AI overviews.
Keep in mind the tool measures one document at a time and has no awareness of synonyms, stems, or related entities. A page that says automobile twelve times and car never will look balanced to a human reader but score zero for the keyword car. Treat density as a sanity check on the writing, not as a target to optimize toward.
Use the output to tune wording: trim runaway repetitions, vary phrasing, and confirm your primary keyword appears enough times to make the page's subject obvious without crossing into mechanical repetition. That is the only role density still plays in 2026 SEO.

Common Use Cases

01

Content optimization

Confirm your primary keyword is mentioned enough to make the topic obvious without crossing into stuffing territory.

02

Stuffing audit

Scan finished drafts for any single term above the 3-4% threshold that could read awkwardly or trigger spam classifiers.

03

Topic coverage check

Verify supporting terms and related entities all show up at sensible frequencies across a long-form article.

04

Vocabulary variety editing

Spot overused words you keep falling back on so you can swap in synonyms and improve readability scores.

Frequently Asked Questions

There is no official target. A practical band is 0.5-2.5% for a primary keyword in an English article of 800+ words. Above 4% looks unnatural to readers and flags spam systems; below 0.5% usually means the topic is not the page's clear focus.
Not as a direct factor. Modern ranking uses neural embeddings and entity recognition that capture meaning, not term counts. Density is useful only as a writing diagnostic — a way to spot accidental repetition or thin topical coverage.
Stop words are high-frequency function words like the, and, is, of, in. They carry no topical meaning, and in any English text they dominate raw word counts. Filtering them lets the report show the content words that actually describe what the page is about.
Bigrams are two-word phrases (search engine, meta tag); trigrams are three-word phrases (open graph image). They reveal multi-word patterns the single-word view misses, and they tend to map closely to the long-tail queries real users type.
No. The analysis runs in your browser tab using plain JavaScript Map and regex tokenization. The text never leaves your device — there is no upload endpoint behind this tool, so you can safely paste unpublished drafts or NDA-covered material.
Different tools tokenize differently. Hyphenated words, contractions, and punctuation handling vary, and stop word lists are not standardized. Two tools can both be correct and report different percentages for the same input.
Tokenization works on any language that uses whitespace word breaks (most European languages). The stop word filter only covers English, so for other languages you will see articles and prepositions in the top results. CJK languages without whitespace breaks need a different tokenizer entirely.
Yes — use the bigram or trigram view, or search for the phrase in the results table. The tool counts every literal occurrence of the phrase as a single hit and reports its share of total bigrams or trigrams.
Density is a ratio. Adding more text expands the denominator, so unless the new content also repeats the keyword, the percentage falls. This is a feature, not a bug: it reflects how concentrated the term is in the document overall.
Only if it reads as repetitive when you skim it. Three percent is fine for a tightly focused product page or definition article. The same number on a 2000-word feature usually means you have unintentionally hammered the same phrase and could vary the wording.

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