Microsoft Word does not show keyword density anywhere in its built-in statistics. The Word Count dialog (Review → Word Count) reports total words, characters, pages, paragraphs, and lines, and you can flip a checkbox to include text boxes, footnotes, and endnotes in the count, but it never tells you how often a specific word or phrase appears in the document or what share of the running text that repetition represents. To get an actual density percentage, you need to move the visible body text out of Word and into a tool that tokenizes the words, tallies each token, and divides by the total token count. The Keyword Density Checker does exactly that in the browser: paste the text you wrote, optionally type one exact phrase to inspect, and the page returns total tokens, unique tokens, the 50 most frequent eligible words, and the phrase's overlapping occurrences, all with the denominator shown. It runs locally, so your draft never leaves the tab, and it makes no claim that any particular percentage improves search rankings.
Why Microsoft Word Stops at Word Count
Word's statistics are designed to measure document size, not linguistic balance. The dialog answers "how long is this document" and "how much text lives in selected shapes," not "which words dominate this draft." A 1,200-word article and a 1,200-word keyword-stuffed paragraph will return identical Word Count totals, even though the second one is editorially unhealthy. Density is a per-token share, and Word simply does not compute it. Until Microsoft adds a frequency breakdown to Review, a practical path is to copy the visible body text, paste it into a tokenizer, and inspect the report.
What the Keyword Density Checker Reports
The page produces a transparent local frequency report for pasted text. It counts total word tokens, unique normalized tokens, the 50 most frequent eligible words, and an optional exact keyword or phrase. Every percentage uses a disclosed denominator, and the page makes no claim that a particular density will improve search rankings. Tokenization uses Unicode letter and number properties, so words written beyond basic ASCII remain countable. An internal apostrophe or typographic apostrophe can stay inside a token, allowing forms such as don't and l'été to remain single units. Punctuation, dashes, symbols, and whitespace separate tokens. Matching is case-insensitive through Unicode lowercase conversion.
| Metric | Word Count in Microsoft Word | Keyword Density Checker |
|---|---|---|
| Total word tokens | Yes | Yes |
| Character count with and without spaces | Yes | No |
| Pages, paragraphs, and lines | Yes | No |
| Unique word count | No | Yes |
| Frequency of each word | No | Yes (top 50 eligible rows) |
| Single-word density percentage | No | Yes |
| Exact phrase occurrence count | No | Yes (overlapping matches) |
| Phrase density percentage | No | Yes |
| Case-insensitive matching | Toggle for Find and Replace | Always, via Unicode lowercase |
| Text leaves the device | No (local document) | No (current browser tab) |
Extract the Visible Text From Your Document
Before the checker can analyze anything, it needs the prose you actually want to inspect. In Word, select the body text you intend to review, copy it, and switch to your browser. Avoid copying navigation, footers, repeating templates, captions generated from tables, or boilerplate that you would not normally consider part of the article. The checker has no HTML or markup stripper, so anything that lives between paragraphs in your selection lands in the report. If the draft is long, paste it whole up to the 500,000-character ceiling and read the top 50 rows. The analysis runs locally in the tab, which is helpful for drafts that should not leave the device, though you should still avoid exposing sensitive text on shared machines.
Run the Density Check on the Pasted Text
- Paste the visible text into the input area on the Keyword Density Checker page.
- If you want a phrase density figure, type one exact keyword or phrase into the optional field. Match the spelling and spacing exactly, because the report tokenizes your query with the same rules it uses for the body; matching is case-insensitive through Unicode lowercase conversion, so the casing of your phrase does not need to match.
- Choose the minimum word length for displayed single-word rows. Pick one if you want one-character rows to show, or pick a higher number if you only want to see meaningful terms. Remember this control affects only the single-word table, not the denominator.
- Click Check keyword density to generate the report.
- Read the totals first: total word tokens, unique tokens, and, when you entered a phrase, its occurrence count and phrase density.
- Inspect the top 50 single-word rows. The list sorts by descending count and then alphabetically for ties, so the most repeated words sit at the top.
- Compare the surrounding sentences where the high-frequency words appear, and decide whether the repetition is intentional or accidental.
Read the Single-Word and Phrase Rows Together
Two numbers matter: a single-word density and an optional phrase density. The single-word density is that word's count divided by total tokens multiplied by 100. The phrase density follows the same formula: the number of contiguous, overlapping occurrences of the phrase divided by total tokens multiplied by 100. The phrase density is not divided by the number of possible starting positions or multiplied by phrase length. Both numbers share one denominator, which is the total number of tokens the tokenizer recognized in the pasted text.
Consider the test sentence "Red blue red. RED green blue red-blue." 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 exact phrase red blue occurs twice, producing a phrase density of 2 ÷ 8 × 100, or 25%, under the documented denominator. The dash inside red-blue separates tokens, so the count rises without inflating the phrase row.
| Pattern to read in the report | What it points to |
|---|---|
| One word dominates the top row | Likely accidental repetition or a filler term; check the sentences around it |
| The phrase row is much higher than the surrounding single-word rows | Target phrase is being forced; review the cadence of the paragraphs |
| Top single-word rows look generic (the, and, of) | Minimum word length is set too low; raise it to surface content terms |
| Unique word count is much smaller than expected | Repetition is heavy; consider synonyms or sentence restructuring |
| The top 50 cap hides obvious repetition | The cap is a display limit; totals still cover the full input |
What the Report Does Not Promise About Rankings
Density is descriptive, not prescriptive. Search engines evaluate usefulness, intent satisfaction, originality, structure, links, reputation, and many other signals, and there is no percentage at which a page suddenly starts ranking. According to Google's spam policies on keyword stuffing, repeatedly using words or phrases in a way that looks unnatural can affect how a page performs in search results. The checker deliberately has no stop-word list, stemming, lemmatization, synonym expansion, language detector, HTML stripper, or search-volume data, because those features require language- and corpus-specific reference choices. Repeating a phrase to reach a numeric target can make copy worse and may look like keyword stuffing.
Use the Numbers to Edit, Not to Hit a Target
For a useful review, 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. Compare revisions using the same tokenizer and denominator so the numbers line up. Use the report to notice accidental repetition, missing terminology, or editorial imbalance, then revise for readers. Keep human editorial judgment and actual search performance data above any isolated density number. Word users who also work in Google Docs can adapt this workflow by exporting the same body text from Docs instead, and the How to Check Keyword Density in Google Docs guide covers that path in detail.