Keyword density is the share of a text taken up by a single word or an exact phrase, calculated as (occurrences ÷ total words) × 100, and the fastest way to check it in Google Docs is to copy the body of your document and paste it into the Keyword Density Checker for a transparent breakdown that runs entirely in your browser. The tool counts every recognized word token, lists the top 50 most frequent eligible words, and reports an optional phrase you supply as a contiguous sequence. It accepts up to 500,000 characters, handles Unicode words like l'été and café, and uses case-insensitive matching so "Red" and "RED" count as the same token. Because Google Docs does not ship with a density meter of its own, the practical workflow is a small round-trip: copy the visible copy from your doc, paste it into the report, read the percentages alongside the surrounding prose, then return to the document and edit. The report is descriptive evidence, not a target, and it deliberately offers no ranking promise, no stop-word list, and no language guesswork.
Why Google Docs Has No Built-In Density Checker
Google Docs focuses on writing, collaboration, and lightweight formatting. Its built-in word count (Tools > Word count) returns a single total and an optional page count, but it does not break that total down by frequency, does not track a target phrase, and does not expose a percentage of any kind. Add-ons published through the Google Workspace Marketplace can extend Docs, but most density add-ons are thin wrappers around a JavaScript tokenizer that re-implement the same arithmetic the Keyword Density Checker performs in a separate tab. The hidden cost of an add-on is permission scope: many request access to read, edit, or send your document content to a third-party server.
Running the analysis in a separate browser tab avoids that scope entirely. You copy the visible copy you want to inspect, paste it into the Keyword Density Checker, and the page produces a report without contacting any external service. You also keep Google Docs' version history untouched, which matters when an editor still needs to review the changes you made between revisions.
What the Keyword Density Checker Does for Your Doc
The checker accepts a single block of pasted text up to 500,000 characters and processes it locally. Internally it lowercases the text, splits it into Unicode letter-and-number tokens, and counts every recognized token for the denominator used in the percentage math. The output that is most useful for a Google Docs workflow is four numbers: total words, unique words, the top 50 single-word rows by frequency, and an optional phrase count.
Tokenization rules are predictable once you see them in one place. The table below shows how the same characters are split into tokens, which lets you predict what the report will count before you paste.
| Input string | How it tokenizes | Reason |
|---|---|---|
| don't | One token: don't | Internal apostrophe stays inside the token |
| l'été | One token: l'été | Typographic apostrophe stays inside the token |
| red-blue | Two tokens: red, blue | Hyphen is a separator |
| RED | One token: red | Case-insensitive matching via Unicode lowercase |
| company2024 | One token: company2024 | Letters and numbers stay together without a separator |
| U.S.A. | Three tokens: u, s, a | Periods are separators; one-letter rows can be hidden by the Minimum word length setting |
An optional exact phrase field lets you ask a specific question about your draft. If the headline of your article is "remote team onboarding," type that phrase into the field and the report will tell you how many times the contiguous sequence appears and what percentage of the total tokens it represents. Matching is case-insensitive, and overlapping matches are allowed. For a closer look at the arithmetic behind the percentage, the guide Calculate Keyword Density Without Guesswork walks through the same denominator rule with additional examples.
How to Check Keyword Density in Google Docs
- Open your Google Docs draft and select the visible body you want to analyze. Highlight from the first heading area you want to inspect through the end of the last paragraph you want to count. Leave navigation, footer text, repeated author bios, and brand templates out of the selection, because those repeated strings will inflate the numbers.
- Copy the selection with Ctrl+C on Windows or Cmd+C on macOS. The keyboard shortcuts in Google Docs match the rest of the browser, so the edit menu's Copy command works the same way.
- Open the Keyword Density Checker in a new browser tab. Click the main text area and paste with Ctrl+V or Cmd+V. The text field accepts up to 500,000 characters and rejects empty or token-free content rather than returning a misleading zero report.
- Optionally type the exact phrase you want to inspect in the keyword field. Use the lowercase form of the phrase; the matcher is case-insensitive, so "Remote Team Onboarding" and "remote team onboarding" produce the same count. The phrase is matched as a contiguous sequence, and overlapping matches are allowed.
- Set the minimum word length for the single-word table. A minimum of two, for example, hides one-character rows so the table is not dominated by stray letters and numbers. The control only affects which single-word rows appear; total words and the density denominator still include every recognized token.
- Click Check keyword density. A report appears with total words, unique words, the top 50 eligible single-word rows, and the count and density for the optional phrase.
- Return to Google Docs and read the report alongside the surrounding paragraphs. Focus on the prose around the words with the highest counts rather than the raw percentage, then edit the doc to fix any repetition, missing terminology, or accidental imbalance.
How to Read the Density Report
The report has a transparent structure. Every percentage is count divided by total tokens, multiplied by 100. The total tokens number is the same denominator for every row, so you can compare rows directly. Rows sort by descending count and then alphabetically for deterministic ties, and the table displays at most 50 terms to keep the interface readable. Total and unique counts still cover the complete input, even when the table is truncated.
A worked example makes the math concrete. Paste the single sentence "Red blue red. RED green blue red-blue." into the checker. The tokenizer finds eight total words. Red appears four times for 50%, blue appears three times for 37.5%, and green appears once for 12.5%. The dash inside "red-blue" acts as a separator, so red and blue each gain one more match from that compound. The exact phrase "red blue" occurs twice and produces a phrase density of 25% under the documented denominator, which is twice the count divided by eight total tokens, not divided by the number of possible starting positions or multiplied by phrase length.
The unique words count is the number of distinct normalized tokens in the input. In the example above, the unique count is three: red, blue, and green. The hyphenated pair is already counted inside red and blue, so it does not produce extra unique entries. If the same sentence is repeated later in your draft, the unique count will not change but the total count will, which lowers the percentage of every individual row.
Common Patterns the Report Catches in Google Docs Drafts
Once you start running the checker on real articles, three patterns show up often. The first is accidental repetition: a phrase that appears in the introduction, the middle, and the conclusion without the writer noticing. The report makes the count obvious, and the prose can be edited to vary the wording. The second is missing terminology: a concept that the article is about but does not name often enough to match a target phrase. The phrase count surfaces this directly. The third is editorial imbalance, where boilerplate like "click here to learn more" or repeated author signs inflate a few short words and push percentages upward without adding substance.
Google's own documentation treats keyword stuffing as a negative signal. Per Google's spam policies on keyword stuffing, repeating a phrase to reach a numeric target can make copy worse for readers and may look like keyword stuffing. The right use of the checker is to notice accidental repetition, missing terminology, or editorial imbalance, then revise for readers rather than for a number.
Bringing the Numbers Back Into Your Document
The checker is most useful when it feeds an editorial decision, not when it dictates one. A practical loop is to paste the final visible copy, enter the exact phrase whose repetition you want to inspect, run the analysis, and read the surrounding sentences rather than acting on percentages alone. Then compare revisions using the same tokenizer and denominator, which the checker holds constant for every run. Keep human editorial judgment and actual search performance data above any isolated density number.
Two practical guards help the loop stay useful. First, paste only the main text you intend to inspect; navigation, footer, markup, and repeated templates can distort the result. Second, avoid pasting drafts that contain sensitive content on shared machines, even though the page runs entirely in the current browser tab and does not send the text to an AI model. The analysis is bounded at 500,000 characters and rejects empty or token-free content rather than returning a misleading zero, so a blank paste will fail loudly instead of producing a fake "all clear" report.
For Google Docs specifically, the round-trip is the entire workflow. Write in Docs, copy the visible body, open the Keyword Density Checker in a new tab, paste, run, read, and return to Docs to revise. The check takes very little time once the draft is ready, and it produces a transparent record of how often each word and the chosen phrase appear before you hit publish.