Check one keyword, or leave the keyword field empty to see the 50 most frequent tokens in your text.
Paste your text, optionally enter one keyword and select Analyze. With a keyword entered, the tool shows its match count and share of all recognized tokens. Leave the field blank to review a frequency table sorted by count.
Use the table to find repeated wording that deserves an editorial check. A frequent word may be essential to the topic, part of a quotation or an ordinary connecting word; frequency alone does not make it a problem.
Text and the keyword are converted to lowercase before comparison. A keyword must match a complete recognized token. Searching for “cat” matches “Cat” but does not match the token “catalog”.
The percentage is matching tokens divided by total recognized tokens, multiplied by 100. If “coffee” occurs three times among 100 tokens, its share is 3%. Results are displayed to two decimal places.
The current implementation identifies tokens with a basic pattern for letters A–Z, digits and underscores. It can split contractions and hyphenated expressions. It does not provide reliable word segmentation for Hindi or many other scripts, and accented words can be split.
This is a single-token counter. Entering a phrase such as “coffee beans” does not search across adjacent words, so the phrase may return zero even when it appears in the draft. For phrases, review the text manually rather than interpreting that zero as an absence.
Read the paragraphs containing the repeated term before making changes. Remove redundant sentences or clarify the subject where needed. Replacing every occurrence with a synonym can make writing less precise.
There is no target percentage supplied by this tool that guarantees rankings or quality. The number describes the pasted text; it does not evaluate search intent, factual accuracy, competing pages or the usefulness of your answer.
Paste the body text you want to review rather than an entire page copied with menus, cookie notices and footer links. Those repeated interface words can dominate the frequency table and hide the wording you intended to inspect. If your goal is to review an introduction or a product description, analyze that section on its own. The result represents the input selection, not an automatically detected main-content region.
For comparisons between versions, keep the boundaries the same. A keyword can represent a smaller share after you add an unrelated paragraph even though its original repetition remains unchanged. Record both the absolute count and the recognized token total. Looking at the two values together gives a clearer picture of your edit than comparing percentages without knowing whether the passage became longer, shorter or substantially different in scope.
If “tea” appears four times among 200 recognized tokens, its share is two percent. If you remove 100 other tokens without removing any occurrence of “tea”, its share becomes four percent while the match count stays four. That change does not by itself prove that the writing became worse. It describes a different ratio, and you need to read the revised passage to decide whether any repetition now feels unnecessary.
Small passages can produce large percentage changes from a single edit. One matching token among ten is ten percent; one among 100 is one percent. Rounded results can also look equal when the underlying ratios differ slightly. Use the percentage as a convenient summary of the supplied text, and retain the raw count when explaining a revision to another writer or comparing drafts of very different lengths.
Leave the keyword field empty to see up to 50 recognized tokens sorted by occurrence count. Common connecting words are included because this tool does not filter a stop-word list from the table. A frequent “the” or “and” is not automatically an editing error. If your purpose is to examine a predefined list of common English words, use the separate Stop Words Counter and review its own matching rules.
When a topic word appears repeatedly, inspect the sentences containing it. Some occurrences may be needed to identify the subject clearly, especially in instructions where a vague pronoun would confuse the reader. Remove duplicate explanations, awkward phrasing or unnecessary restatement when those are the real problem. Do not replace every occurrence with an obscure synonym merely to make a number smaller or the vocabulary table look more varied.
The keyword “run” does not automatically include “runs”, “running” or “runner”. These are distinct tokens under the current rule. The tool does not perform stemming, identify synonyms or group related concepts. If you want to compare several forms, inspect them individually or use the blank-field table. A word absent from the table may be outside the displayed top 50 rather than completely absent from the input.
Contractions and punctuation require care as well. A hyphenated expression can split into multiple tokens, and digits or underscores can appear in recognized items. Accented letters may split differently from basic English letters. Hindi and many other scripts are not reliably segmented here. A low or zero result in those cases should not be described as evidence that a topic is missing from the passage; the tokenizer may not represent your language correctly.
After identifying a repeated word, read its immediate context and ask what the reader gains from each sentence. A necessary definition may justify repetition; two adjacent sentences that make the same claim may not. Check headings, examples and conclusions separately if their intended roles differ. The counter cannot determine whether a passage answers the reader’s question or whether the examples support the claims surrounding the repeated term.
Keep factual review separate from frequency review. Reducing a percentage cannot verify a statistic, repair a misleading explanation or make an unsupported claim trustworthy. Likewise, adding a target word cannot fill a missing instruction by itself. Finish by reading the complete edited text for clarity, consistency and coverage. The output should help you locate wording to inspect, while the final decision remains grounded in the actual purpose and meaning of the passage.
Record the selected keyword, the passage boundaries and the recognized token total when comparing a revision. The keyword counter works on the pasted sample, so a different introduction or an added sidebar changes the measurement. Using the same selection lets you distinguish a wording change from a change in scope.
For a useful editorial handoff, identify the repeated sentences as well as reporting the keyword count. Another writer can then inspect why the term appears in each place. A frequency table is an index to the wording that needs review; it cannot explain whether a repeated product name, technical term or quoted phrase should be replaced.
No. Uppercase and lowercase versions are combined for this calculation.
Not with the current implementation. The keyword field compares against individual tokens, not sequences of words.
When the keyword field is empty, the table displays up to 50 tokens with the highest counts. The denominator still uses all recognized tokens.
No. It is a frequency measurement, not a ranking or writing-quality score.
No. “book” and “books” are different recognized tokens. The tool does not stem words or group related forms automatically.
Yes. The blank-field table includes recognized tokens without removing a stop-word list. Use the Stop Words Counter for that separate analysis.
Yes. The frequency table displays up to 50 entries. Enter one supported token in the keyword field to inspect its count directly.
Punctuation itself is not counted as a token by this pattern, but it can split expressions into separate recognized tokens and change the total.
It means no recognized token matched the supplied keyword exactly after lowercasing. Unsupported scripts, phrases or different word forms can also lead to zero.
Yes. Increasing the denominator can lower the percentage while the match count stays unchanged. Read both values and review the passage rather than treating the ratio alone as an improvement.