Free Word Frequency Counter – Analyze Word Usage

What a Word Frequency Counter Measures and How It Counts

This lists every distinct word in your text alongside how many times it appears, ranked from most to least common. The value is that it shows you patterns you cannot see by reading. Writers discover they used the word actually eleven times in 900 words. SEO editors check whether a target phrase appears often enough to be relevant without tipping into repetition. Students preparing for a viva find the terms their own thesis leans on hardest. Teachers marking essays spot a pupil who has written very four times in one paragraph. Reading your own draft rarely reveals this; a ranked count reveals it in a second. Counting works by tokenising and normalising. The text splits on whitespace, then punctuation attached to a token is stripped, so cat, cat. and cat, all reduce to the same word. Everything is lowercased for comparison, which is why The at the start of a sentence and the mid-sentence count together rather than as two separate entries — treating them separately would make the results almost useless for English. Each distinct token becomes a key in a tally, and the counts are sorted descending. Alongside the raw count, a percentage is shown: that word's occurrences divided by the total word count. Hyphenated compounds are treated as a single token, and numbers are counted as words. A worked example. Paste: The cat sat on the mat. The mat was warm, so the cat stayed on the mat. That is 17 words in total and 9 distinct ones. The ranking comes back as the with 5 occurrences at 29.4 per cent, mat with 3 at 17.6 per cent, then cat and on tied on 2 each, and finally sat, was, warm, so and stayed with 1 apiece. Note how the leads by a wide margin, which is entirely normal — in ordinary English the, of, and, a and to always dominate a raw count, and that is the first thing to understand about reading these results. Specific uses. A blogger checks that a 1,200-word article mentions its focus keyword eight times rather than thirty, which would look forced to a reader before a search engine ever noticed. A novelist runs a chapter through it and finds the word suddenly appearing nine times. A researcher analysing 200 survey free-text answers sees which product complaint word recurs most. A speechwriter checks that the client's key message word actually appears in the closing paragraph. A translator reviewing their own work spots an English filler phrase they have overused across a document. A lecturer preparing a reading list checks which technical terms recur most in a set paper, then defines exactly those in the seminar handout rather than guessing at which ones students will actually struggle with. The thing beginners get wrong is treating the top of the list as meaningful. Function words such as the, of and and will always sit there, and they tell you nothing about the content — look further down for the first substantive noun or verb, which is where the real signal is. If your tool offers a stop-word filter, use it. On percentages, keyword density is a weak signal in modern search and there is no magic figure to hit; writing naturally and checking you have not repeated a phrase absurdly is the sensible standard. Also note that the count is literal, so plurals and inflections are separate entries: run, runs and running are three words here even though they express one idea, so add those entries together yourself if you want a figure for the concept. Analysis runs in your browser, so drafts and confidential documents are never uploaded, stored or logged.

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