A safe keyword density checker should disclose how it handles text, calculate percentages transparently, and avoid promising that one density will improve rankings. Keyword Density Checker meets that practical definition: it analyzes up to 500,000 pasted characters in the current browser tab, does not send the text to an AI model, and exposes the denominator behind every density figure. It counts total word tokens, unique normalized tokens, the 50 most frequent eligible single words, and an optional exact keyword or phrase. That makes it suitable for a privacy-conscious review of drafts, provided you paste only the visible main copy and avoid sensitive material on a shared device. Its purpose is editorial: reveal repetition, terminology imbalance, or a possible keyword-stuffing pattern so you can read the surrounding sentences and revise responsibly. It does not prescribe a “safe” ratio, promise ranking gains, fetch a live page, or judge whether copy will rank. Those boundaries are central to using the tool safely and interpreting its output honestly.
What makes a density audit trustworthy?
A trustworthy audit separates three questions: what text was analyzed, how tokens and matches were counted, and what the result does not prove. A small percentage is not automatically “safe,” and a large one is not proof of overuse. The useful test is whether the arithmetic is disclosed and repetition can be inspected in context.
Keyword Density Checker keeps its scope narrow. It produces a frequency report instead of an optimization grade, includes no ranking thresholds, and leaves wording decisions with the editor. That makes the report suitable for noticing patterns, but not for declaring that a draft is ready to rank.
- Reproducible math: Every percentage divides an occurrence count by the complete total-token count.
- Local processing: The analysis runs in the current browser tab, and pasted text is not sent to an AI model.
- Defined scope: The checker reports exact textual frequency without claiming that a particular ratio will improve search performance.
Check pasted copy in three steps
Use the final visible main text you want to review. The checker accepts up to 500,000 characters and rejects empty or token-free content instead of returning a misleading zero report. Because it has no HTML boilerplate remover, navigation, footers, markup, and repeated templates can distort the result.
- Paste the text and choose the term to inspect. Paste the visible main copy into the input area. You can leave the optional keyword field blank or enter one exact keyword or phrase. An exact phrase is matched as a contiguous sequence after tokenization; it is not expanded with synonyms or word variants.
- Set the display filter and run the analysis. Choose the minimum word length for displayed single-word rows, then select Check keyword density. This setting only filters the table. Total words, unique words, and every percentage denominator still include all recognized tokens, including short words hidden from the rows.
- Read the report in context. Review total words, unique words, the most frequent eligible terms, and the optional phrase result. Then inspect the sentences around repeated wording. Treat each percentage as descriptive evidence about the pasted copy rather than a target that the draft must reach.
For a meaningful comparison, analyze each revision with the same keyword phrase, minimum word length, and tokenizer. Changing the input scope or filter can make two reports look different even when the underlying language has barely changed.
Read every percentage with its denominator
Single-word density equals that word’s occurrence count divided by total recognized tokens, multiplied by 100. Exact-phrase density uses the same total-token denominator. It is not divided by the number of possible starting positions and is not multiplied by the number of words in the phrase.
Consider the test sentence “Red blue red. RED green blue red-blue.” The tokenizer finds eight total words, and the exact phrase “red blue” occurs twice. Its documented calculation is 2 ÷ 8 × 100 = 25%.
| Report item | Disclosed counting rule | Why it matters |
|---|---|---|
| Total words | All recognized tokens, including words hidden by the minimum-length filter | Provides the denominator for every displayed density |
| Unique words | Distinct normalized tokens across the complete input | Shows lexical variety without judging its quality |
| Single-word rows | Up to 50 eligible words, sorted by descending count and then alphabetically for ties | Makes frequent terms easy to locate while keeping the table readable |
| Optional exact phrase | Contiguous, overlapping occurrences found with the same tokenizer | Measures exact repetition rather than semantic similarity |
The minimum word-length setting therefore changes what you see, not what the denominator contains. For example, filtering one-character rows does not pretend those tokens were absent from the source. This distinction keeps the report mathematically stable.
Know how words and phrases are counted
Tokenization uses Unicode letter and number properties, so words written beyond basic ASCII remain countable. Matching is case-insensitive through Unicode lowercase conversion. An internal straight or typographic apostrophe can remain inside a token, allowing forms such as “don’t” and “l’été” to stay as single units. Other punctuation, dashes, symbols, and whitespace separate tokens.
The optional phrase follows the same tokenization rules and counts only a contiguous sequence of those tokens. Phrase occurrences may overlap. This matters when checking repeated wording in a long sentence because each valid appearance can be included rather than forcing the phrase to advance by a fixed number of words after every match.
The checker deliberately includes no stop-word list, stemming, lemmatization, synonym expansion, language detector, or search-volume data. It also does not remove HTML boilerplate. Consequently, ordinary words remain in the denominator, different grammatical forms are not silently merged, and semantically similar expressions are not treated as exact phrase matches. Those choices keep the report deterministic and transparent rather than adding language-specific guesses.
Privacy and analysis boundaries
Everything runs in the current browser tab, and the tool does not send pasted text to an AI model. That supports a local review workflow for drafts that should remain in the device-based editing process. It is still wise to avoid exposing sensitive text on a shared machine, so local processing should complement normal device and account-security practices rather than replace them.
The privacy boundary does not expand the analysis scope. The checker does not fetch a URL, render JavaScript, compare competitors, connect to Search Console, estimate ranking difficulty, or publish content. It analyzes only the text pasted into the tool. Its word and phrase counts can describe that text, but they cannot establish search visibility, authority, originality, or success with the intended audience.
Keep the input focused as well as private. Paste the copy the reader would actually see, not a full navigation template or an unprocessed HTML document. Repeated menus, headers, footers, and boilerplate can create frequency patterns that do not represent the article’s main prose.
Use the report as an editorial diagnostic
Density is descriptive, not prescriptive. Google Search Central’s keyword-stuffing spam-policy guidance is relevant context, but the checker does not diagnose every case or define a safe cutoff. Repeating a phrase to reach a number can make copy worse and may look like keyword stuffing. Only the full sentence reveals whether a term is redundant, useful, or missing context.
- Run the final visible copy. Use the exact keyword or phrase you are genuinely concerned about, especially when terminology sounds repetitive while reading aloud.
- Inspect surrounding sentences. Determine whether a frequent word appears because the topic requires it or because headings, transitions, and repeated templates have overloaded it.
- Look for editorial imbalance. A high count may expose awkward repetition, while a low count may prompt you to check whether important terminology is missing. Neither result alone decides the quality of the copy.
- Compare like with like. When testing a revision, keep the tokenizer settings, phrase, denominator, and main-text scope consistent.
A safe density check should lead to better editing decisions rather than target chasing. Use the report to locate passages that deserve attention, revise for clarity and intent, and keep human judgment and actual search-performance data above an isolated percentage. The checker can show what repeated in a draft; it cannot guarantee how that draft will perform.