Calculate phrase occurrences as a share of recognized tokens after punctuation normalization. The colored status uses fixed cutoffs and is not a validated SEO assessment.
The keyword ratio on this page is the number of matching occurrences divided by the recognized token total, multiplied by one hundred. The text is normalized before matching, so the result is not simply a count of the untouched original string. Paste the passage, enter the desired word or phrase and select Check Ratio. Review both the numerator and denominator rather than treating the displayed percentage as a complete judgment of the passage.
This utility differs from the single-token Keyword Density Checker and the unnormalized Keyword Frequency Checker. It can search a phrase in its cleaned text, while the denominator is a count of recognized word-character pieces. Those definitions matter when you compare tools. Two pages with similar-looking names can report different values without either implementing the same measurement, and an editorial decision needs the actual rule rather than an assumed universal meaning of “keyword density”.
The function lowercases the passage and replaces characters outside basic word characters and whitespace with ordinary spaces. English letters, digits and underscores remain recognized; punctuation can become separators. A hyphenated expression can therefore produce multiple denominator tokens. Accented letters and many non-Latin characters are also outside that basic set, so this is not a complete multilingual normalization method and should not be interpreted as preserving every language's words faithfully.
Afterward, the denominator is built by splitting the cleaned passage at whitespace and removing empty pieces. The keyword itself is lowercased and escaped for literal matching, but it is not passed through the same punctuation-removal step. A punctuation-containing keyword can therefore fail to match the altered passage. Inspect the spelling and preprocessing on both sides before assuming that a zero means your original text lacked the requested expression.
Take the input “Tea, tea and coffee.” with keyword “tea”. After normalization, there are four recognized tokens: tea, tea, and, coffee. Two matches divided by four tokens produce fifty percent. The high value reflects a tiny sample and a repeated subject, not an automatic diagnosis that the writing is spam. Check the actual source and sample size before interpreting the status message displayed next to that calculation.
If you add six other recognized tokens without adding another “tea”, the count stays two and the denominator becomes ten, giving twenty percent. The ratio changed because the sample became longer. That does not explain whether the original sentence needed editing. Save both occurrence count and token total with the result so a reviewer can understand the arithmetic instead of seeing only a percentage disconnected from the passage and its selection boundaries.
A phrase occurrence counts once, even when the phrase contains several recognized words. For “seo tools seo tools”, the denominator is four tokens and the phrase “seo tools” occurs twice, so the ratio is fifty percent. It is not the percentage of individual tokens covered by the phrase, which would answer another question. Do not silently multiply the occurrence count by phrase length when explaining the value produced by this implementation.
The search also uses non-overlapping matches. For a repeating phrase that shares words with another possible occurrence, those overlaps are not all counted. Internal whitespace remains relevant because matching occurs against the cleaned string, not a rebuilt single-space version of its tokens. Document both the occurrence convention and the phrase spelling if you use this output in an analysis, especially when comparing it with a method that defines phrase density differently.
Replacing punctuation with spaces does not collapse every resulting space before phrase matching. “seo, tools” becomes a cleaned string with two spaces between the terms: one replacing the comma and the original space. A one-space keyword “seo tools” can therefore return zero in that context, even though the denominator still recognizes two tokens. This difference between splitting for the denominator and matching the cleaned string is important when a result looks inconsistent.
Tabs, newlines, multiple spaces and punctuation-heavy terms can produce related effects. The added word-boundary checks are basic regular-expression boundaries, not a language-aware interpretation of each word. Test the exact sample you intend to analyze instead of adjusting the text only to produce a favorable ratio. If you deliberately normalize spacing first, preserve the original and disclose that preprocessing when comparing the count with another source or a different tool's result.
The current interface labels values below 0.5 percent as low, values above 3 percent as high and the interval between them as healthy. These thresholds are hard-coded display rules. They are not supplied by an analysis of the topic, audience or search results, and the tool does not verify that those labels predict rankings or writing quality. A green status can accompany inaccurate, irrelevant or incomplete text just as a red one can appear for a harmless short example.
Do not add or remove wording solely to reach the green interval. Review whether the phrase helps a reader follow the explanation, and avoid unnatural repetition intended to manipulate search performance. Google's keyword-stuffing guidance is not a validation of these numeric cutoffs. Use the arithmetic as a descriptive check, then make editorial decisions from the actual paragraph, the information it conveys and the requirements of the task rather than the color of the generated message.
Select the same relevant content boundaries for both versions, excluding menus or unrelated text unless those are the actual subject of your analysis. Record the phrase, normalized-token denominator and occurrence count. A new appendix can reduce the ratio without resolving repetition in the original section, while a shorter introduction can increase it without adding a single mention. Explain what changed in the passage instead of presenting the percentage alone as evidence of improvement.
For Hindi, accented vocabulary or other unsupported scripts, the basic normalization can omit meaningful letters and distort the denominator. A low total or an error saying no valid words does not establish that the passage lacks words for a reader. Use a suitable tokenizer and matching convention for the language when accuracy matters. This tool's output remains meaningful only within its documented character, spacing and phrase rules, not as a universal content metric across writing systems.
Select Check Ratio again after editing either input. The output shows the latest explicit calculation and does not automatically refresh on every keystroke. Begin with a small example whose expected matches you can enumerate, then inspect the real passage and its recognized token total. If the count surprises you, check punctuation normalization, whitespace and the boundary rule before drawing an editorial conclusion from the displayed low, high or healthy label.
Save final copy in your editor and retain the source selection if the result supports a review. A ratio cannot verify a fact, establish originality, inspect indexing or diagnose a traffic decline. Use it to understand a narrow pattern in the text you supplied, alongside separate checks for clarity, completeness and evidence. The practical goal is an explanation that serves the reader, with transparent arithmetic available when repetition needs inspection, rather than an article written to satisfy a colored percentage badge.
Matching occurrences divided by recognized tokens, multiplied by one hundred. The display rounds to two decimal places.
No. A matched phrase contributes one occurrence, even though its words contribute separately to the denominator.
No. The passage is normalized by replacing punctuation with spaces, while the keyword is lowercased and escaped but not normalized the same way.
Punctuation replacement can leave two spaces in the cleaned passage. The one-space phrase is a different literal sequence for matching.
No. The search returns non-overlapping occurrences.
They use fixed cutoffs: below 0.5 percent, above 3 percent and the interval between them. They are interface rules, not verified SEO judgments.
No. Review relevance, clarity and necessary repetition. A color does not establish content quality or predict search performance.
Its basic normalization is not a reliable Hindi word analysis. Meaningful script characters can be removed or split, distorting results.
The denominator may have changed after adding, removing or differently normalizing other text. Review counts and sample boundaries together.
No. Their preprocessing, outputs and measurement purposes differ. Choose according to the exact matching question you need to answer.