Keyword Density Checker

A keyword density checker counts how often each word, and each run of consecutive words, appears in a text and divides that count by the total number of words. The percentage shows which terms a page actually emphasizes, which is not always the same as the terms the writer meant to emphasize.

Text is lowercased and split into words at spaces and punctuation. Letters and digits in any alphabet count as word characters, so accented words such as café stay whole. Apostrophes and hyphens inside a word are kept, so a contraction or a hyphenated term such as long-tail counts as one word, while a dash standing on its own or quotation marks around a word do not add a word. Languages written without spaces between words, such as Chinese and Japanese, are not segmented, so each unbroken run counts as one word. The total is the number of words after this split, and the unique count is the number of distinct words. A .txt or .csv file can be loaded in place of pasted text; a CSV is read as plain text, so its commas simply separate words.

Phrases are n-grams: every run of two, three, or four consecutive words, read with a sliding window, so a 100-word text contains 99 two-word windows and 98 three-word windows. Punctuation is removed before the window slides, which means a phrase can span the end of one sentence and the start of the next. Density for any term is its count divided by the total word count, times 100. For a phrase this counts each occurrence once, so a three-word phrase found 5 times in 100 words reads 5 percent, not the 15 percent of the words it occupies.

Stop words are the 139 English function words (the, and, of, with, and so on) that dominate any text. Filtering removes them from the single-word table only and leaves the phrase tables intact, so a phrase such as cost of living is still found. Additional stop words, separated by commas, extend the list with brand names or boilerplate. A minimum occurrence count, 2 by default, hides terms that appear once, and a per-table limit of 20 rows keeps long documents readable; neither changes the total or the densities. The single keyword lookup counts one word or phrase with punctuation ignored and reports its density the same way, whatever the minimum count.

Density has no fixed target. Older SEO advice quoted 1 to 3 percent for a primary keyword, but search engines rank pages on relevance and usefulness rather than a ratio, and a term repeated far above its natural rate reads as keyword stuffing to people and to spam filters alike. The figure is most useful comparatively: against an earlier draft, against another page on the same topic, or to catch one phrase repeated far more often than intended.

The 10-word text "seo tips and seo tools make seo work for you" contains seo 3 times, so its density is 3 / 10 x 100 = 30.00 percent. The two-word phrase seo tips appears once in the 9 two-word windows, and its density is still measured against all 10 words: 1 / 10 x 100 = 10.00 percent.

Keyword density data informs content decisions across many stages of writing, editing, and auditing. The following scenarios illustrate how different workflows benefit from frequency and density analysis.

• On-page SEO audit: A published post's target term can be compared with the rate it reaches in competing articles for the same query. A primary term far below its rivals may be under-covered, while one repeated far above them reads as keyword stuffing.
• Competitor content analysis: Running a competitor's article through the same analysis shows which terms it emphasizes. Comparing two-word and three-word phrase density between your content and theirs reveals gaps in topical coverage or opportunities to strengthen related-term usage.
• Academic writing review: A research paper or thesis chapter can be scanned for overused terminology. High density on a single term may indicate repetitive phrasing that weakens readability, and the phrase tables reveal habitual multi-word constructions that could be varied for clarity.
• Product listing optimization: Analyze e-commerce product descriptions to confirm that target search terms appear at meaningful density. CSV exports from many listings, combined in a spreadsheet, track keyword distribution across an entire product catalog.
• Content brief compliance: Writers receiving SEO content briefs can paste their draft and verify that each required keyword meets the specified count or density range before submission, reducing revision cycles with editors.
• Translation and localization quality assurance: Comparing keyword frequency in translated text against the source-language version shows where key terminology was paraphrased or omitted. Accented letters and non-Latin alphabets count as word characters, although the stop-word list is English only.
• Social media and ad copy testing: Analyze short-form copy to confirm that brand terms and calls to action appear at intended frequency. Even in 280-character posts, density analysis highlights whether the core message term dominates or gets diluted by filler words.