What "Large Text" Means for a Density Check
Keyword density for a long document is exactly count divided by total word tokens multiplied by 100, and a keyword density checker for large text applies the same denominator rule whether the input is a 600-word blog post or a 200,000-character whitepaper. The Keyword Density Checker accepts up to 500,000 characters of pasted text in a single browser tab, which is well beyond the length of a typical 5,000-word article (roughly 30,000 characters) and comfortably covers most long-form drafts that real editorial teams produce. The bound is on characters rather than words, the limit is enforced before tokenization rather than silently truncating the input, and empty or token-free content is rejected so the report never reflects a misleading zero. That ceiling is the single most important number to remember when treating the tool as a keyword density checker for large text: the same rules apply at 1,000 characters and at 400,000 characters, and there is no hidden downsampling between the two extremes.
The tool counts Unicode letters and numbers, treats an internal straight or typographic apostrophe as part of a single token, lowercases every term through Unicode conversion, and uses punctuation, dashes, symbols, and whitespace as token separators. Matching is case-insensitive, which means RED, Red, and red all map to the same row. The phrase field is tokenized by the same rules and matched as a contiguous sequence, with overlapping matches counted individually. None of these rules change when the input grows from a paragraph to a chapter, which is what makes the report stable across scales and useful for comparing revisions of a long draft.
How to Run a Keyword Density Checker on Large Text
- Open the Keyword Density Checker in the browser tab where you keep your draft.
- Copy the visible body text you want to inspect, including headings and subheadings, and paste it into the input area.
- Optionally enter one exact keyword or phrase whose repetition you want to track across the document.
- Choose a minimum word length for the displayed single-word rows; the value affects only which rows appear in the table, never the total token count used as the density denominator.
- Select Check keyword density and wait for the local analysis to complete in the same browser tab.
- Read the total word count, the unique word count, the top-frequency table, and (if you entered a phrase) the phrase density row.
- Compare the report to your draft and revise the sentences that surround high-percentage terms, not the percentages themselves.
Because every step runs locally, the draft never leaves your device unless you choose to copy it elsewhere. That matters for internal documentation, draft chapters, and pre-publication reviews where the text should not be transmitted to a third-party service. The tool does not fetch a URL, render JavaScript, or call out to a search-engine API; everything happens in the open tab, and nothing is sent to an AI model.
Strip Boilerplate Before You Paste
The denominator in the density report is every recognized token in your pasted text. If you include repeated navigation labels, cookie-banner copy, footer links, sidebar widgets, and shared CTA buttons, those tokens count toward the denominator and quietly lower the percentage on every term you actually care about. The checker has no HTML boilerplate remover, no language detector, and no built-in stop-word list, so it cannot tell the difference between body prose and template chrome; that distinction is yours to make before pasting. Pasting only the visible body copy also keeps the table focused on the words that reflect your editorial choices rather than the words that reflect your site framework, and it keeps the unique-word count from being dominated by repeated chrome.
| Paste | Leave out |
|---|---|
| Body paragraphs and subheadings | Header navigation labels |
| Author byline and caption text | Footer links and copyright line |
| Inline callouts you actually wrote | Cookie banner and consent prompts |
| Pull quotes and figure captions | Sidebar widgets and related-article lists |
| Final published copy as readers see it | Markup tags, scripts, and CSS |
For a draft that lives across multiple editor windows or documents, paste from each section separately and treat each segment as its own report. Comparing unrelated reports on the same settings is fine, but comparing two different drafts without using the same input boundary is not meaningful, because the denominator changes whenever the input boundary changes. Pick one boundary and stick with it across every revision you plan to compare.
Read the Report as Descriptive Evidence
The single-word density row is the term's count divided by total tokens times 100. The phrase density row is the contiguous phrase's count divided by total tokens times 100, with overlapping matches allowed. The phrase denominator is not the number of possible starting positions, and it is not multiplied by phrase length; it is the same all-tokens denominator used for single words. That consistency is what makes the report readable as evidence rather than as a black-box score, and it is the reason a comparison of two revisions is meaningful only when both reports used the same input and the same minimum-length control.
Consider the test sentence "Red blue red. RED green blue red-blue." The tokenizer finds eight total tokens: Red appears four times for 4 ÷ 8 × 100 = 50%, blue appears three times for 3 ÷ 8 × 100 = 37.5%, and green appears once for 1 ÷ 8 × 100 = 12.5%. The exact phrase "red blue" appears twice as a contiguous overlapping sequence, giving 2 ÷ 8 × 100 = 25%. The hyphenated form "red-blue" splits into two tokens ("red" and "blue") because dashes separate tokens, which is why its presence contributes to both the red and blue counts. This same arithmetic is what your report will display on the long draft you paste in, scaled up to the actual word count of your text.
Sorting is deterministic: rows sort by descending count and then alphabetically for ties. The table shows at most 50 eligible rows, while the unique-word count still reflects the complete input. That 50-row cap is a readability choice, not a tokenization choice, so a long-form article with a rich vocabulary will still report a complete unique count even when only the top 50 terms fit in the visible table. The minimum word length control filters which rows appear but never alters the total token count, which prevents a filtered report from quietly inflating percentages.
Pitfalls That Skew Long-Form Reports
| Pitfall | What happens to the report |
|---|---|
| Pasting with the cookie banner and footer included | Template tokens inflate the denominator and lower every body-term percentage |
| Counting repeated CTA copy toward your target phrase | Phrase percentage rises because of marketing chrome, not editorial emphasis |
| Lowering the minimum word length to 1 to "see everything" | The full unique count is already reported; the table only becomes harder to scan |
| Comparing reports taken with different minimum-length settings | The total tokens match, but the visible rows differ and the eye-skew hides real shifts |
| Rewriting the copy to chase a numeric target | Prose quality drops, and the change can read as keyword stuffing, which Google's spam policies for keyword stuffing warn against |
Two practical habits keep the report trustworthy on long drafts. First, always paste from the same source boundary across revisions: the published copy with chrome stripped, or the raw draft with everything included, but never a mix. Second, read the surrounding sentences that produce high counts; a 4% single-word density on a long article almost always points to a paragraph that needs editing, not a number that needs more repetition. The report's value is in surfacing those editorial decisions, then letting human judgment make the change. For a closer look at how the denominator and the minimum-length control interact, see the guide on checking keyword density with transparent math.
Long drafts reward patience more than tweaking. With one consistent paste boundary and one consistent minimum-length setting, two reports on two revisions tell you whether the change you actually made moved the needle on the words that matter to your readers, or whether the edit only shifted numbers in a way that no reader would notice. The percentage is descriptive, not prescriptive, and the most useful follow-up after any large-text report is a careful re-read of the sentences that produced the count, not a rewrite aimed at a numeric target.
If you're weighing options, How to Copy Keyword Combinations From Keyword Combiner covers this in detail.