How to use Keyword Density Checker
Paste any article, blog post, or web-page content to see exactly how often each word and phrase appears — as a raw count and as a density percentage of the total word count. Keyword density analysis helps you spot over-optimisation (keyword stuffing that triggers Google penalties), under-optimisation (topics you're not mentioning enough), and missed long-tail opportunities visible only in 2- and 3-word n-gram breakdowns. Toggle a stopword filter to skip filler words and focus on meaningful terms.
- Paste your full article, landing page copy, or any text block into the input area.
- Toggle "Ignore stopwords" on (default) to filter out common words like "the", "and", "is" — this surfaces your actual topic keywords.
- Click "Analyze" to compute 1-word, 2-word, and 3-word phrase frequencies.
- Switch between the "1-word", "2-word", and "3-word" tabs to explore unigrams, bigrams, and trigrams.
- Review density percentages: anything above 3–5% for a single keyword may indicate over-optimisation; below 0.5% may mean the topic is underrepresented.
- Copy the table data for use in a spreadsheet or SEO reporting tool.
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What keyword density means for SEO
Keyword density is the ratio of how often a target keyword or phrase appears in your content to the total word count, expressed as a percentage. A density of 1% means the word appears once per 100 words. In the early 2000s, SEOs aimed for 2–8% density as a ranking signal; Google's Penguin and Panda updates (2011–2012) penalised pages where unnatural repetition degraded readability. Today, Google's NLP systems (BERT, MUM) evaluate semantic relevance rather than raw repetition — a page that naturally uses synonyms, co-occurring terms, and related entities outperforms one that mechanically repeats a target phrase. Keyword density analysis is now useful as a diagnostic (too low = topic underrepresented; too high = potential stuffing) rather than a target to optimise for directly.
| Density range | Interpretation | Recommended action |
|---|---|---|
| < 0.5% | Keyword underrepresented | Consider adding more natural references to the topic |
| 0.5–2% | Natural, well-integrated | No action needed — healthy range |
| 2–5% | Noticeable repetition | Review whether the repetition reads naturally |
| > 5% | Potential keyword stuffing | Revise content to reduce unnatural repetition |
N-gram analysis for long-tail keyword research
While single-keyword density gives a surface view, bigram (2-word) and trigram (3-word) phrase analysis reveals which long-tail combinations appear in your content — and whether they align with your target search queries. For example, a page about photography might have high frequency for "camera" (unigram) but the bigram report might show "camera settings" and "portrait photography" are also prominent, helping you understand the semantic scope of your content. Comparing your trigrams against Google Search Console query data shows whether your content naturally uses the same 3-word phrases users search for. If searchers look for "best running shoes for flat feet" but your content only contains "running shoes", adding the full phrase context improves topical relevance.
Worked examples
Single-keyword density
Inputs: "coffee" appears 8× in 400 words
Result: 8 ÷ 400 × 100 = 2.0% — natural, well-integrated
Stuffing flag
Inputs: a term at 6% density
Result: flagged as potential keyword stuffing (above 5%)
Glossary
- Keyword density
- The percentage of times a keyword appears in a text relative to the total word count (occurrences ÷ total words × 100).
- Stopwords
- Common words (the, and, is, of, etc.) that carry little meaning and are typically excluded from keyword frequency analysis.
- N-gram
- A contiguous sequence of N words in text — unigrams (1 word), bigrams (2 words), trigrams (3 words).
- Keyword stuffing
- The practice of overloading a page with a keyword to manipulate rankings — a quality violation that can trigger Google penalties.
- TF-IDF
- Term Frequency–Inverse Document Frequency — a statistical measure of how important a word is to a document relative to a corpus; more nuanced than raw density.
Related reading
Frequently Asked Questions
Why use Keyword Density Checker?
- Spot potential keyword stuffing before publishing by flagging any single term that exceeds a 3% density threshold
- Analyse bigram and trigram frequencies to surface long-tail phrases you are naturally using but may not have targeted
- Toggle stopword filtering to switch instantly between all-word frequency and meaningful topic-keyword-only view
- Export the frequency table to a spreadsheet for side-by-side comparison with Google Search Console query data
Common use cases
- Checking a product page for unintentional keyword stuffing before submitting it for a Google Ads quality score review
- Comparing a draft article's trigram frequencies against target search queries pulled from Google Search Console
- Reviewing a translated landing page to confirm the target keyword appears at the same natural density as the original
- Auditing a competitor's blog post to discover co-occurring terms they use that you could add to strengthen your own article
- Identifying which 2-word phrases dominate a landing page to verify the semantic coverage matches the ad group's intent
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