Count how often each word (or 2-3 word phrase) appears in any text. Stop-word filter, density %, n-grams, sortable, CSV / JSON export. 100% browser-side.
A free word frequency counter that ranks every word and phrase in your text by how often it appears, with density percentages, vocabulary richness, and sortable views. Switch between single words, two-word phrases (bigrams), and three-word phrases (trigrams) to surface either single-word frequency or recurring multi-word expressions. Toggle the built-in 100-word English stop-word list to focus on meaningful terms instead of "the", "and", "of". Set a minimum word length, choose case-sensitive or case-insensitive counting, and re-sort by frequency, alphabetical order, or word length. Export the full table as CSV (for spreadsheet analysis), JSON (for code), or plain text. Built for SEO content audits, keyword density checks, essay reviews, transcript analysis, and "what is this article actually about" investigations. Everything runs in your browser — no upload, no signup.
Word frequency is the simplest form of text analysis and one of the most useful. It answers questions like: what is this article really about? (top non-stop words give you the topic), am I repeating myself? (a single word at 5%+ density usually feels repetitive), which terms should I link or define? (terms in the top 20 are core vocabulary), and does my content match my SEO target? (your target keyword should appear in the top 5-10 non-stop words for a focused page).
For longer texts (transcripts, essays, books), word frequency reveals patterns invisible to a casual reader: a politician's speech that mentions "freedom" 47 times, a novel where the protagonist's name appears more than the antagonist's, an article that uses "however" 12 times in 800 words.
Single-word frequency is a starting point but rarely the whole story. Two- and three-word phrases (bigrams and trigrams) capture the actual concepts your text is about. A blog post about "machine learning" will rank "machine" and "learning" highly as single words — but the bigram "machine learning" is the actual topic, and showing it ranked separately reveals whether the post discusses the field or just both words coincidentally.
For SEO content audits, bigrams typically tell you more than single words because most search queries are 2-4 words. If you are targeting "credit card rewards", the bigram "credit card" or trigram "credit card rewards" should appear prominently in your content. If only the single word "credit" ranks high, your content may be off-target or unfocused.
It is the ratio of unique words to total words, expressed as a percentage. A 1000-word essay with 400 unique words has 40% richness; a 1000-word essay with 700 unique words has 70% richness. Higher numbers indicate more varied vocabulary; lower numbers indicate repetition. As a rough guide, conversational writing scores 40-55%, professional articles 55-65%, and academic / literary writing 65-80%.
Stop words are extremely common function words ("the", "and", "of", "a", "is", "to") that always dominate raw frequency counts but carry little meaning. Excluding them lets you see the actual subject matter — keep them in if you are studying writing style or grammar, exclude them for SEO keyword extraction, topic modelling, or "what is this article about" analysis.
Single words count individual tokens — "machine" and "learning" each get their own row. Bigrams count consecutive 2-word pairs as a unit — "machine learning" is one row, distinct from "deep learning". Trigrams count 3-word sequences — "natural language processing", "in the end". For SEO and topic analysis, bigrams and trigrams are usually more revealing because they surface multi-word concepts that drive intent.
When OFF (default), "Apple" and "apple" merge into one row counting as 2. When ON, they stay separate. Case-insensitive is right for content analysis. Case-sensitive is useful when proper nouns matter or when analysing source code where identifiers are case-sensitive.
Not the way it was 15 years ago. Modern search engines use semantic understanding rather than literal keyword matching, so stuffing your target keyword to hit a "magic" density no longer helps. What does help: keyword density tells you whether your content is actually about what you claim it is about. If your "guide to organic gardening" article shows "gardening" as the top word at 1.5% but "organic" only at 0.3%, the article is more about gardening generally than organic specifically.
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