Analyze word frequency, keyword density, and unique word count
Analyze any text to find the most frequently used words, keyword density, and unique word count. Perfect for SEO analysis, content optimization, and linguistics research. This tool runs entirely in your browser — no data is sent to any server.
Word frequency analysis reveals the hidden structure of text. The most common word in English is 'the' (~7% of all words), followed by 'be', 'to', 'of', and 'and'. These stop words dominate any text — filtering them out reveals the meaningful content words that define a document's topic. This tool counts every word occurrence, ranks them by frequency, and optionally removes stop words. Applications include: SEO keyword density analysis, authorship attribution studies, vocabulary assessment for language learners, content analysis for marketing, detecting over-used words in writing, and academic text mining. Zipf's Law predicts that the nth most common word appears at about 1/n the frequency of the most common word — a pattern visible in virtually any large text.
Stop words are extremely common words that carry little meaning: the, a, an, is, are, was, it, in, on, to, etc. They make up 25-30% of most English texts. Filtering them reveals the content-bearing keywords that define what a text is about.
Modern SEO doesn't target specific keyword density percentages — that's an outdated practice. Instead, use your target keyword naturally 3-5 times in a 1000-word article, plus related terms. Google's algorithms understand context and synonyms; keyword stuffing hurts rankings.
Zipf's Law states that word frequency is inversely proportional to rank. The 2nd most common word appears ~½ as often as the 1st, the 3rd ~⅓ as often, etc. This pattern holds across languages, books, websites, and even non-linguistic data.
Spotting overused words improves prose. If 'very' appears 30 times in an essay, replace most with stronger adjectives. If a key term appears too rarely, your content may lack focus. Frequency analysis gives objective feedback on word choices.
Yes — the tool counts words by whitespace separation, which works for most European languages. For languages without spaces between words (Chinese, Japanese, Thai), specialized tokenization is needed.