Count occurrences of a word

Count how many times a specific word appears in a passage without accidentally counting it inside longer words. This whole-word counter is useful for writers checking repetition, editors tracking required terminology, teachers reviewing student work, and developers validating generated content. Matching is case-insensitive by default, so Word and word belong to the same count, while an optional case-sensitive mode keeps them separate. Unicode letters and digits are supported, including words with internal apostrophes. The result also reports the total number of recognized words, making it easy to understand the count in context.

How whole-word occurrence counting works

Whole-word matching answers a narrower and more practical question than searching for a sequence of characters. If the target is cat, a substring search might count cat inside catalog, education, or scatter, producing a misleading result. This tool first separates the supplied text into Unicode-aware word tokens and then compares every complete token with the requested word.

Runs of letters and digits form words, and an apostrophe inside a word is retained, which means contractions and names such as don't and O'Brien can be counted as a unit. Spaces, punctuation marks, line breaks, dashes, and other separators establish word boundaries. The algorithm scans from left to right and increments the result only when a complete token equals the target.

It does not stem words or merge grammatical forms, so edit, edits, edited, and editing remain distinct. That behavior is intentional: editors and automated checks usually need an exact, explainable count rather than a linguistic estimate. Identical inputs always return identical outputs because the capability uses no network, random values, external dictionaries, or current time.

Choosing case sensitivity and preparing input

Send text as a non-empty string and word as exactly one token. The target cannot contain surrounding spaces, commas, quotation marks, or multiple words; rejecting those ambiguous forms prevents a caller from believing that a phrase count is a word count. In that mode, BLUE, Blue, and blue all match the target blue after Unicode-aware lowercase normalization.

The output echoes the original target rather than rewriting it, so interfaces can display precisely what the user requested. Text may contain several paragraphs and punctuation-heavy prose, up to the declared limit of 100000 characters. A valid text that contains no matching target returns count zero; zero is a useful result, not an error.

Before automating an editorial rule, test a mixed-case sample and a longer word containing the target to confirm the chosen mode matches your policy.

Using the result in editing and automation workflows

For a quick check, paste a draft into the browser tool, enter the keyword, and read the whole-word count. A content editor can compare counts before and after a rewrite, a curriculum system can verify that a vocabulary word appears in an exercise, and a template test can assert that required terminology occurs at least once. Avoid treating a high count as proof of quality; repetition can indicate emphasis, compliance, or awkward writing depending on the document.

Also remember that exact matching does not combine plurals, inflections, synonyms, or differently punctuated phrases. If your policy needs several acceptable forms, call the capability once for each explicit target and combine the results in your application.