A bulk keyword density check inspects every word in a long pasted draft against one consistent denominator, and the Keyword Density Checker accepts up to 500,000 characters of pasted text per run. The "bulk" part is the input size, not a separate mode: there is no URL fetching, no batched list of pages, and no API call to make — you paste one large body of text and read a transparent frequency report back. The denominator for every displayed percentage is the total count of recognized word tokens in the paste, so each number stays comparable as drafts grow from a paragraph to a full article. Because the analysis runs entirely in the current browser tab, long drafts can be inspected without uploading the file to a remote server, which keeps the workflow local even when the document is several thousand words long.
What "Bulk" Means for a Keyword Density Check
For most editorial reviews, "bulk" simply means a single document that is larger than a paragraph or two. A long blog post, a product category page, a transcript, or a chapter draft can each run several thousand words, and pasting the full body into one analysis keeps the denominator stable. That stability matters because density percentages only make sense when the same denominator is used to compare revisions; splitting a draft into chunks and averaging the results would create numbers that mean something different than the actual share of the word in the source.
The Keyword Density Checker treats "bulk" as input size rather than as a parallel processing feature. There is no queue of URLs, no batched list of competitors, and no API endpoint to call. You paste one body of text, optionally enter one exact phrase whose repetition you want to track, and read a single frequency report. If the paste grows beyond the 500,000-character cap, the analysis is bounded at that limit rather than returning a misleading partial count, which preserves the integrity of the denominator.
How the Keyword Density Checker Handles Large Pastes
Once the text is pasted, the checker lowercases the entire input through Unicode lowercase conversion and walks through it with tokenization rules that accept Unicode letters and numbers, plus optional internal straight or typographic apostrophes. Punctuation, dashes, symbols, and whitespace separate tokens, which means words like "red-blue" count as two tokens ("red" and "blue") and contractions such as "don't" or "l'été" stay as single units. Tokenization is deliberately mechanical: there is no stop-word list, no stemming, no lemmatization, no language detection, and no synonym expansion, because each of those requires a corpus- and language-specific reference set.
The analysis counts every recognized token for the total and unique word totals and for the denominator of every percentage. A separate Minimum word length control filters only which single-word rows appear in the frequency table; the underlying counts and the denominator are unaffected. That separation matters in bulk runs: a one-character word filter set to "2" hides rows such as "a" or "I" from view, but those tokens still count toward total words and toward any single-word density displayed, so the percentages do not silently inflate. The table itself shows at most 50 rows, sorted by descending count and then alphabetically for deterministic ties, while total and unique counts still cover the complete input.
Tokenization rules at a glance
| What the checker does | What the checker does not do |
|---|---|
| Counts Unicode letters and numbers as tokens | Apply a stop-word list or any language-specific filter |
| Keeps internal straight or typographic apostrophes inside a token (don't, l'été) | Stem, lemmatize, or expand synonyms |
| Matches case-insensitively through Unicode lowercase conversion | Detect language or guess part of speech |
| Splits on punctuation, dashes, symbols, and whitespace | Strip HTML markup, navigation, or footer boilerplate |
| Counts every token toward total, unique, and the percentage denominator | Cap the denominator at the 50 displayed rows |
Run a Bulk Keyword Density Check in Three Steps
- Paste the visible text you want to inspect into the input area. Leave out navigation menus, footer copy, repeated templates, and any markup — only the prose you want to evaluate should be in the buffer. The input cap is 500,000 characters, which covers long-form drafts in a single run on the Keyword Density Checker.
- Optionally enter one exact keyword or phrase whose repetition you want to track, and choose a Minimum word length for the single-word frequency rows. A value of 2 hides one-character rows such as "a" and "I" without removing them from the denominator.
- Select Check keyword density and read the report: total word count, unique normalized tokens, the up-to-50 most frequent eligible single words with their counts and percentages, and — if you entered one — the count and density of your exact phrase. Compare revisions using the same tokenizer and denominator rather than acting on the percentages alone.
Why Percentages Are Descriptive, Not Prescriptive
Single-word density for a term equals that term's count divided by total tokens multiplied by 100. Phrase density for an exact contiguous query uses the same denominator — the total number of recognized tokens — so a phrase match at position 7–8 of an 8-token sentence produces a phrase density of 12.5%, not a different figure adjusted for phrase length or starting positions. Phrase matches can overlap, and the document explicitly does not promise that reaching a particular density will improve search rankings.
Search engines evaluate usefulness, intent satisfaction, originality, structure, links, reputation, and many other signals. Google's published guidance on spam policies describes keyword stuffing as loading pages with keywords in a way that creates a negative user experience, and the checker itself makes no claim that any specific percentage is a safe or optimal target. Treat the report as descriptive evidence: it surfaces accidental repetition, missing terminology, or editorial imbalance, and the surrounding sentences remain the unit you should actually revise. For readers who want the math walked through more deliberately, the transparent-math walkthrough covers the same denominator behavior in more depth.
Reading the Frequency Table for Long Drafts
The 50-row cap keeps the interface readable even for very long pastes, which means a single run will not return every word in the document but it will return the 50 terms with the highest counts and a stable denominator underneath. If a target term does not appear in the top 50, that fact alone is useful information: either the term is genuinely rare in the draft, or the draft is long enough that other words dominate the top of the table. The exact-phrase row is independent of that cap, so a phrase you enter is always counted and reported even when it would not otherwise qualify for the top 50.
For a worked example from the documented test sentence "Red blue red. RED green blue red-blue.", the tokenizer finds eight total tokens after the dash splits "red-blue" into two pieces. Red appears four times (Red, red, RED, red) for 50% of the total; blue appears three times (blue, blue, blue) for 37.5%; green appears once for 12.5%. The exact phrase "red blue" matches twice as a contiguous case-insensitive sequence — at token positions 1–2 and at token positions 7–8 — yielding a phrase density of 25% under the documented denominator of 2 ÷ 8 × 100. Those figures are the kind of evidence the report returns for review; the surrounding prose still decides the rewrite.
Density formulas used in the report
| Quantity | Formula | Notes |
|---|---|---|
| Single-word density | (word count ÷ total tokens) × 100 | Denominator includes every recognized token, including those hidden by the Minimum word length filter. |
| Phrase density | (occurrence count ÷ total tokens) × 100 | Matches contiguous overlapping sequences case-insensitively; not adjusted for phrase length or starting positions. |
| Unique words | Count of distinct normalized tokens | Reported separately from total tokens so both figures remain visible. |
When to Re-Run the Check Across Revisions
The most useful comparison is between two drafts of the same piece. Paste the same final visible body before and after a structural rewrite, enter the same exact phrase, and read the deltas in count and density under one consistent denominator. If the percentage on a target phrase moves up, look at the surrounding sentences rather than at the number — a higher density on a phrase you did not intend to repeat usually means a paragraph now echoes itself, which is the kind of imbalance the tool was designed to surface. If a number moves down, the draft may now be too thin on a term you actually need; the report does not tell you which direction is correct, only what the current draft contains.
Because the entire analysis runs in the current browser tab and does not send the pasted text to an AI model or a remote endpoint, it is reasonable to inspect drafts that should not leave the device, although any shared machine still carries the usual physical-access risks. For a useful review, paste only the final visible copy, enter the exact phrase whose repetition you want to inspect, run the analysis, and let human editorial judgment sit above any isolated density number — the report helps you notice patterns, it does not decide the rewrite.