Word Frequency Counter
Count how often each word appears and rank them by frequency.
Total Words: 0Unique Words: 0
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Analyzing Word Frequency & Density
Word frequency counters are essential for SEO keyword density checks, readability analysis, writing-style audits, and natural language processing tasks. Toggle case-insensitive mode to fold "The" and "the" into the same bucket. The table is limited to the top 100 words for readability; use the TSV export to save the full list to a spreadsheet. All analysis runs entirely in your browser — no data is sent anywhere.
Related tools: Word Counter · Character Map Explorer
Built and maintained by Meet Shah · Last updated
What this tool is used for
- Finding which terms a document actually leans on.
- Checking a draft for an unintentionally repeated word.
- Comparing two documents' vocabulary to see which one is narrower.
- Producing a ranked term list from survey responses.
- Spotting filler words that appear far more than you expected.
Frequently Asked Questions
- Why are common words excluded?
- Because without stopword removal the top of every list is the, of, and, to — true of all English text and therefore uninformative. Filtering them is what makes the remaining counts describe THIS document rather than the language.
- Should words be stemmed or lemmatised?
- It depends what you are counting. Stemming crudely chops suffixes (running → run), lemmatisation uses a dictionary (better → good). Without either, run, runs and running count as three unrelated words.
- Is keyword density still an SEO signal?
- Not as a target. Modern search engines use semantic analysis, and writing to a density percentage produces stilted text that reads as manipulation. Frequency analysis is now more useful for editing than for ranking.
- What is Zipf's law?
- The observation that the nth most common word appears about 1/n as often as the most common. It holds remarkably well across languages, so a frequency distribution that badly violates it is a sign of unusual or generated text.
- How are non-English languages handled?
- Tokenisation is the problem, not counting. Whitespace splitting fails on Chinese and Japanese, which need dictionary segmentation, and the stopword list is English-specific — so results for other languages need a matching list.
- Should n-grams be counted as well as single words?
- Often yes. Single words miss phrases — "machine learning" counted separately says less than the pair does. Bigrams and trigrams are what surface the actual subject matter of a document.
- How should case and possessives be handled?
- Lowercase before counting, or `The` and `the` split one entry into two. Possessives and plurals are harder — merging them needs stemming, which introduces its own errors, so most counters leave them separate and say so.
Common errors and gotchas
- Leaving stop words in, which makes 'the' the top result and the analysis useless.
- Counting a word and its plural separately, which understates the real emphasis.
- Comparing counts between documents of very different lengths without normalising.
- Ignoring case, or not ignoring it, without deciding which the question needs.
- Treating frequency as importance, which it is only loosely related to.
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