Word Frequency Counter
Analyze word frequency in any text. See the most common words ranked by count and percentage. Free word frequency counter, no signup.
Three steps to get started
Paste your text
Paste any text - an article, essay, webpage content, or document - into the input area.
Configure filters
Choose how many top words to show, enable "Hide common words" to remove stop words, and set a minimum word length.
Analyze results
See each word ranked by frequency with a visual bar chart showing relative occurrence. Sort alphabetically or by count.
Why word frequency matters
Word frequency analysis counts how often each distinct word occurs in a body of text and ranks the results. It is the oldest technique in computational linguistics - concordances of the Bible were compiled by hand in the thirteenth century - and it still underpins search engines, plagiarism detectors, topic models, and spam filters today.
The distribution of words in text is rarely random. In any well-written piece, a handful of key terms dominate - reflecting the core topic. Word frequency analysis makes this visible, turning a wall of text into a ranked list that reveals what a document is actually about.
Two settings control what you see. Hide common words removes stop words - the closed class of function words (the, of, and, is) that make up roughly 40–50% of any English text but carry almost no topical signal. The minimum word length filter is a blunter version of the same idea: raise it to 5 or 6 and only substantive vocabulary survives. Leave both off when you are studying style rather than subject matter, because function-word patterns are exactly what forensic authorship attribution relies on.
Practical applications
- SEO optimization - check that target keywords appear at an appropriate density and that relevant terms co-occur naturally
- Content editing - identify overused words or phrases that should be varied for better readability
- Academic analysis - compare vocabulary profiles between authors or time periods
- Competitive research - analyze competitor content to understand their topical focus
- Summarization - the top-frequency meaningful words often describe the main topics of a document
Zipf's Law
A fascinating property of natural language: word frequencies follow Zipf's Law - the most common word appears roughly twice as often as the second most common, three times as often as the third, and so on. This power-law distribution is surprisingly consistent across all natural languages and large enough text samples.
In practice that means the shape of your ranked list is predictable, and what you are really looking for is the deviation from it. A content word that sits far higher than Zipf would predict is either your genuine topic or an unintentional tic. In the Brown Corpus, the top 135 word types account for about half of all word occurrences - a useful reminder of how little of any text is topical vocabulary.
What a raw count cannot tell you
Counting treats every surface form as a separate word, so run, runs, and running are three entries rather than one lemma - a job that stemming or lemmatization would normally handle. It also has no notion of phrases: a document about "machine learning" splits into two unrelated rows. And frequency alone says nothing about distinctiveness, which is why search engines weight terms by TF-IDF, discounting words that are common across every document in a collection. Read the ranking as a starting point for judgment, not as a verdict.
All analysis runs entirely in your browser. No text is ever transmitted to any server.