Measure one keyword token or list the 50 most frequent recognized tokens. Percentages describe your pasted sample; they are not SEO scores or ranking targets.
The keyword density checker counts recognized tokens in pasted text and shows the share represented by a selected token. Paste the passage, optionally enter one keyword and select Analyze. With the keyword field empty, the tool instead lists up to fifty frequent tokens with counts and percentages. The calculation is useful for inspecting repetition in a draft, but it does not evaluate whether the page answers a search question or contains accurate information.
Select the relevant content before copying it into the box. Navigation labels, repeated footers and unrelated recommendations can distort a review of the main article if they are included. For comparisons between versions, keep those selection boundaries consistent. A different percentage may reflect an added sidebar or a shorter introduction rather than a meaningful change in the repeated wording you intended to inspect. Keep the input scope with any result you report to another editor.
The function lowercases the text and extracts items using a basic word-character pattern. It recognizes English A–Z letters, digits and underscores rather than performing a complete linguistic word analysis. Punctuation can split contractions and hyphenated expressions. Accented words and many other scripts may be split or omitted, which changes the denominator as well as the matches. Check that the token total represents your sample before making decisions based on the percentage.
The keyword input is also lowercased and trimmed, then compared with each complete recognized token. “Tea” and “tea” are combined, while “teapot” is a different token. The tool does not stem “write” into “writing” or group synonyms. Those are separate vocabulary items under its rule. If you need to inspect related forms, look for them separately rather than interpreting a single keyword result as a measure of the whole topic's coverage.
The displayed percentage equals matching token occurrences divided by the total recognized tokens, multiplied by one hundred. If “coffee” occurs four times among two hundred recognized tokens, the result is two percent. Removing one occurrence while leaving the denominator otherwise comparable lowers that figure. The output rounds to two decimal places, so slightly different underlying ratios can look equal in the display, especially when you compare long passages with small changes.
The denominator matters independently of the count. Four matches among one hundred tokens give four percent, even though the number of occurrences is unchanged from the two-hundred-token example. Adding unrelated text can therefore lower the ratio without fixing a repetitive paragraph. Record the absolute occurrence count and the token total alongside the percentage. This makes comparisons easier to interpret than reporting only a ratio without the sample size or source boundaries that produced it.
This page does not search a multi-word phrase across neighboring tokens. Entering “coffee beans” compares that entire string with individual recognized items, so it can return zero even when the phrase appears in the passage. A zero for a phrase is therefore not evidence that the phrase is missing. Use the Keyword Frequency Checker for its different literal phrase rule, and review that tool's boundary and spacing behavior before comparing the results.
The distinction also applies to punctuation. A hyphenated keyword input may not match the separate tokens extracted from the text. Whitespace, scripts and accents can create further differences between what a reader sees and what the simple pattern recognizes. Start with a small supported sample to confirm the behavior. When your task requires multilingual segmentation or phrase statistics, use a method designed for that requirement rather than changing the source to force a misleading match.
Leaving the keyword field empty produces a frequency breakdown sorted by count and limited to fifty entries. The percentage for each displayed row still uses all recognized tokens in the sample, not only those fifty rows. Common connecting words are included because the function does not remove a stop-word vocabulary. A frequent “the” or “and” is normal in many passages and should not automatically be treated as something to delete.
A token absent from the visible table may simply lie outside the top fifty. Enter a supported single token to inspect its count directly. The tool preserves repeated occurrences for the calculation rather than counting only unique vocabulary. It does not assign a semantic importance score to the first row. Review repeated words in context, particularly when a product name or technical term must be repeated to avoid ambiguous pronouns in instructions.
Read the surrounding sentences before changing a frequent word. Two adjacent sentences may repeat the same explanation unnecessarily, while several mentions of an exact object can make a procedure clearer. Remove redundant claims, shorten an overlong introduction or improve a vague reference where that helps the reader. Substituting an unfamiliar synonym at every occurrence can make the text less precise even though the visible keyword percentage becomes smaller.
Do not use a fixed keyword density as a requirement for rankings. Google's keyword-stuffing policy concerns unnatural filling of pages to manipulate results, not a percentage supplied by this calculator. The useful question is whether the repeated wording serves the explanation. A draft with a modest ratio can still be unhelpful or inaccurate, and a necessary technical term can legitimately occur several times in a focused sample without the number alone resolving the editorial decision.
Keep the same content boundaries, chosen token and language assumptions when comparing a before-and-after result. If the revision adds a glossary or removes a table, note that scope change explicitly. A ratio combines two values, so you need to know which one changed. For a useful handoff, identify the specific repeated passages you edited and explain what became clearer instead of presenting a lower percentage as the only evidence of improvement.
When another tool reports a different density, compare tokenization and phrase definitions before assuming an error. Some methods count whitespace items, some use language-aware word segmentation and some count phrase words differently. This checker uses single-token occurrences and its own recognized-token denominator. Save the relevant sample and calculation settings in your normal workflow if the number needs to be reproduced, and separate that measurement from factual review or a broader search-performance investigation.
Select Analyze again after editing the passage or keyword. The old result represents the previous run, not a live analysis of every new keystroke. Clearing the fields hides the result panel, and another analysis repopulates its visible values. If the input contains no supported tokens, examine the language and punctuation instead of treating a zero total as an empty or meaningless passage from a reader's perspective.
Before using a result, check one short example with known counts and then inspect the real passage. Keep the final draft in your document editor, where you can review headings, examples and source accuracy alongside repeated wording. The checker should help you locate potential editing work. It cannot certify originality, detect every semantic repetition, inspect competing pages or establish that changing a token percentage will restore traffic lost after a search-system update.
It divides exact single-token matches by the total recognized tokens and multiplies by one hundred. The display rounds to two decimal places.
No. The selected-keyword mode compares against individual tokens. A phrase can return zero even when it occurs in the text.
No. The text and keyword are lowercased before comparison.
Not in the selected-token comparison. “tea” does not match the separate token “teapot”.
The blank-keyword mode limits the visible breakdown to the fifty most frequent recognized tokens. Other tokens still contribute to the denominator.
No. Common words can appear in the table. Use the separate Stop Words Counter when you need that defined vocabulary analysis.
The current token pattern is not a reliable Hindi word segmenter. Review language coverage before interpreting counts or percentages.
No target supplied by this measurement guarantees rankings or quality. Inspect repetition in context and edit for a useful explanation.
The total token count may have increased. A larger denominator can lower the percentage without changing the repeated word count.
No. Select Analyze again after changing either the passage or keyword.