Analyze keyword frequency and density in any text or web page — see your top 1-word, 2-word, and 3-word phrases, with an optional stop-word filter and the top 10 keywords highlighted.
| # | Keyword / Phrase | Count | Density |
|---|---|---|---|
| 1 | seo | 6 | 16.2% |
| 2 | tools | 3 | 8.1% |
| 3 | best | 1 | 2.7% |
| 4 | better | 1 | 2.7% |
| 5 | covers | 1 | 2.7% |
| 6 | engines | 1 | 2.7% |
| 7 | essential | 1 | 2.7% |
| 8 | free | 1 | 2.7% |
| 9 | good | 1 | 2.7% |
| 10 | grow | 1 | 2.7% |
| 11 | guide | 1 | 2.7% |
| 12 | help | 1 | 2.7% |
| 13 | need | 1 | 2.7% |
| 14 | organic | 1 | 2.7% |
| 15 | rank | 1 | 2.7% |
| 16 | search | 1 | 2.7% |
| 17 | success | 1 | 2.7% |
| 18 | tips | 1 | 2.7% |
| 19 | traffic | 1 | 2.7% |
Early search engines relied heavily on simple keyword frequency to judge a page's relevance, which led an entire generation of SEO practitioners to obsess over hitting precise density percentages — and led an entire generation of spammy web pages to repeat the same phrase dozens of times in barely-readable text. Modern search engines have moved far beyond that: they use semantic understanding, entity recognition, and natural language models to judge topical relevance, and they actively penalize the unnatural repetition that old-school keyword stuffing produces. Keyword density analysis today is useful not as an optimization target to hit, but as a diagnostic lens — a quick way to see what your content is actually, measurably about, and whether that matches what you intended it to be about.
The analysis starts by breaking your text into individual words — sequences of letters and digits, with internal apostrophes and hyphens preserved so contractions and compound terms stay intact as single tokens. From there, it builds three separate frequency tables: one counting how often each individual word appears (1-word), one counting every consecutive pair of words as a phrase (2-word), and one counting every consecutive triple (3-word) — a standard technique called n-gram extraction, widely used in natural language processing and search engine indexing alike. Every phrase's density is calculated against the same total word count, so a 2-word phrase appearing 5 times in a 500-word article shows a 1% density, directly comparable to a single keyword also appearing 5 times in that same article.
The three tables tell complementary stories. The 1-word table shows your raw vocabulary emphasis — which individual terms come up most, useful for a quick gut-check on topical focus. The 2-word table starts revealing actual phrase patterns — the two- or three-word combinations that make up real, natural search queries, and often the first place a keyword-stuffing problem becomes visible (an oddly-repeated 2-word phrase is much more noticeable than a repeated single word, which can hide within normal writing). The 3-word table surfaces longer, more specific phrases, which tend to closely mirror long-tail search intent — the kind of specific, lower-competition queries that are often easier to rank for than a single broad keyword.
A practical workflow: paste a finished draft, review the 1-word list with stop words filtered to confirm your primary and secondary keywords appear with reasonable, natural frequency (not once, and not dozens of times), then check the 2-word and 3-word tables for any phrase that repeats suspiciously often — a strong signal to vary your language and let synonyms and related phrasing carry some of the topical weight instead. For competitive research, run the same analysis against a top-ranking competitor's page (via Fetch from URL) and compare which phrases dominate their content versus yours — differences often point directly at content gaps or opportunities your page is currently missing.
Keyword Density Checker reveals what your content is actually about, word by word. These related SEO tools help you act on what you find.