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What this word frequency counter does

This word frequency counter tokenizes your text and counts how many times each individual word appears, then ranks every term from most to least frequent. Every row in the table shows the word, its raw count, and its keyword density — the percentage of total words it represents. Paste any document, blog post, speech, or survey response and the full ranked table appears instantly, with a reading-time estimate and unique-word count in the stats bar at the top.

Switch to bigrams (2-word phrases) or trigrams (3 words) to discover the most common collocations and keyword combinations in your text. Unlike a simple word counter that gives you one aggregate total, a word frequency analysis shows exactly which words dominate your writing — essential for SEO audits, academic text analysis, editorial quality reviews, and content strategy decisions. The tool runs entirely in your browser so no text is ever uploaded or transmitted.

N-gram analysis: bigrams and trigrams

Single-word frequency tells only half the story. Bigram and trigram analysis reveals the phrases that consistently appear together. Toggle the phrase length to 2 and 'free shipping', 'machine learning', or 'customer support' may emerge as the most repeated two-word combinations — far more useful for content strategy than knowing that 'free', 'machine', and 'customer' each appear frequently in isolation.

This n-gram counter processes every consecutive pair or triple in your text: a 500-word product description analyzed for bigrams shows exactly which two-word phrases anchor the copy. Combine bigram density percentages with single-word analysis to identify where to diversify your language or reinforce a specific keyword phrase. N-gram frequency data is also how search engines measure topical relevance, so this analysis gives you the same signal SEO tools like Ahrefs and SEMrush surface in their on-page content reports — for free.

Stop words and keyword density

Common function words — articles ('a', 'an', 'the'), conjunctions ('and', 'but', 'or'), and prepositions ('in', 'on', 'at', 'of') — dominate almost any English text but carry little meaning on their own. Turning on Exclude stop words removes these from the frequency table so meaningful content words rise to the top.

Keyword density — each word's count as a percentage of all words — appears in the Density column. For SEO writing, a density of 1–2% for your primary keyword is typical; anything above 3–4% risks looking over-optimized to search engines. Most SEO platforms flag this, but checking here before you publish is free and instant. Use the percentage column to spot any term that's been repeated too heavily and replace some instances with synonyms or related phrases. The combination of stop-word filtering and density percentages turns this into a lightweight on-page SEO audit tool.

Find overused words in your writing

Writers repeat certain words without noticing. Paste a draft essay, email, or marketing report into the tool, enable stop-word filtering, and any content word appearing above 2–3% is a candidate for replacement. The inline frequency bar next to each word makes the heavy hitters obvious at a glance, without you having to scan every number in the table.

The minimum-length filter lets you focus on substantive words: set it to 4 or 5 characters to exclude short filler words not already covered by the stop-word list. The Ignore numbers toggle strips purely numeric tokens — useful when analyzing data-heavy reports where numeric values would otherwise crowd the top of the table. Together these filters give you a clean, actionable vocabulary audit that Microsoft Word's built-in tools simply don't provide.

How it compares: Excel COUNTIF, Python, and Microsoft Word

Searches for word frequency in Excel, Python, and Microsoft Word account for a large share of how people find word frequency tools — which shows how many users need this analysis but face friction getting it. In Excel, you'd write a separate COUNTIF formula for each unique word: time-consuming and error-prone. In Python, collections.Counter(text.split()) does it efficiently but requires a coding environment, library imports, and knowing how to clean punctuation first. In Microsoft Word, there is no built-in word frequency feature at all — only single-word find-and-count via Ctrl+H.

This free online counter removes all that friction. Paste your text, choose your options, and download a CSV ready to open in any spreadsheet. The same analysis that would take ten minutes of formula-writing in Excel happens in seconds here. For content teams doing regular SEO audits or editorial quality checks, this is the faster, more practical path.

Private, instant, and free — and works offline too

All processing runs locally in your browser using JavaScript — your text never leaves your device. There's no sign-up, no limit on text length, and no cost. The tool is Unicode-aware, so it works correctly across English, French, Spanish, German, and most Latin-script languages. After the page loads once, it works even without an internet connection.

This is particularly important for users analyzing sensitive text: client contracts, legal briefs, internal strategy documents, or unpublished manuscripts. Server-based keyword density tools transmit your content over the internet and may log it. This tool doesn't — there's no server to receive anything. Bookmark it for SEO content audits, editorial reviews, academic research, and any time you need to understand the vocabulary composition of a piece of text.

Zipf's law: why a handful of words dominate every text

Zipf's law — named after Harvard linguist George Kingsley Zipf (1902–1950) — is the empirical observation that in any sufficiently large corpus, the frequency of a word is inversely proportional to its rank in the frequency table. The most common word appears roughly twice as often as the second most common, three times as often as the third, and so on. The mathematical form is frequency ∝ 1 / rank^α, where α is close to 1.0 for natural language. In the Brown Corpus of American English — a one-million-word reference collection compiled at Brown University in the 1960s — 'the' appears approximately 69,000 times (rank 1), 'of' roughly 36,000 times (rank 2), and 'and' around 28,000 times (rank 3), closely matching the predicted 1/rank pattern.

The law has a striking practical consequence: a tiny vocabulary covers most of any text. The top 100 most frequent English words account for roughly 50% of all words in most prose; the top 1,000 account for around 85%. This power law distribution is not unique to language — the same curve describes city population sizes, website traffic, income distribution, earthquake magnitudes, and the sales rank of books. When you run a word frequency analysis and notice that the first few entries have dramatically higher counts than everything else, you are seeing Zipf's law in action. The long tail of rare words that appear only once (called hapax legomena) typically makes up 40–60% of the unique vocabulary in any large document — another direct consequence of the power law.

For content analysts and SEO practitioners, Zipf's law explains why stop word lists work so well: the words at the very top of any frequency ranking are nearly always the same function words across every document in every topic, because their extreme frequency is a structural feature of language rather than a signal of the text's subject matter. Stripping the top-ranked function words is what unmasks the meaningful content words underneath — which is exactly what the stop-word filter in this tool does.

Function words, content words, and corpus-specific frequency lists

Every general English text is dominated at the top of its frequency table by function words — the grammatical glue of the language: determiners ('the', 'a', 'an'), prepositions ('of', 'in', 'to', 'at', 'from', 'with'), coordinating conjunctions ('and', 'but', 'or'), and common pronouns ('it', 'you', 'they', 'we'). These words are remarkably stable across genres: 'the' is the most frequent word in medical journals, legal contracts, children's picture books, and software documentation alike. Content words — nouns, main verbs, adjectives, and adverbs that carry semantic payload — are what differ. The most frequent content words in medical literature cluster around anatomy, pathology, and treatment; in legal contracts they are 'party', 'agreement', 'term', 'shall', and 'provision'; in children's books they are concrete, short nouns and simple action verbs. This is why a word frequency analysis, after stop-word removal, immediately reveals what a text is actually about.

Corpus linguists have formalized useful frequency thresholds. Michael West's General Service List (1953) identified the 2,000 most frequent English word families; research consistently shows these cover approximately 90% of the running words in any general text. Averil Coxhead's Academic Word List (AWL, 2000) added 570 academic word families not already in the GSL — terms like 'analyze', 'concept', 'derive', 'establish', 'indicate', 'significant' — that account for roughly another 10% of academic prose. Knowing where a word sits on these frequency scales matters for language learners and content writers alike: high-GSL words are broadly understood, while low-frequency domain vocabulary signals expertise but may exclude general readers. Running a word frequency check on a piece of writing and cross-referencing the top content words against the GSL and AWL is a fast way to calibrate the vocabulary level of any document.

In natural language processing (NLP), stop word removal before frequency analysis is a foundational preprocessing step. Libraries like NLTK, spaCy, and scikit-learn all ship built-in stop word lists for English and dozens of other languages. The stop words vary slightly by library and use case — scikit-learn's list is more aggressive (318 words) while NLTK's default is smaller — but the principle is identical to the toggle in this tool: filter function words first, then the remaining frequency distribution reveals topically meaningful terms. For multilingual text, the Unicode-aware tokenization in this counter handles the same removal across Latin-script languages; for other scripts, the raw frequency table (without stop filtering) still gives valid n-gram and rank data.

TF-IDF: from raw frequency to document-distinguishing relevance

TF-IDF (Term Frequency–Inverse Document Frequency) is the weighting scheme that powers information retrieval and forms the backbone of classical search engines. It was developed by Karen Spärck Jones in 1972 and later formalized by Gerard Salton in the Vector Space Model. The intuition it solves is exactly the limitation of raw word frequency: a word that appears ten times in a document is not necessarily important if it appears in almost every document in the corpus. Term Frequency (TF) is the count of a word divided by the total words in the document — essentially the keyword density percentage this tool displays. Inverse Document Frequency (IDF) is the logarithm of the total number of documents divided by the number of documents that contain the word. TF-IDF = TF × IDF: a word that appears frequently in one document but rarely across the wider corpus receives a high score, identifying it as a distinguishing term for that specific document rather than a generic filler word.

The applications of TF-IDF are broad and practical. Search engines use TF-IDF-derived signals to rank documents by relevance to a query — a page where a query term has a high TF-IDF score is likely genuinely about that topic, not just a page that happens to contain the word. Keyword extraction for SEO relies on the same logic: the high-TF-IDF words on a well-ranked page reveal what the search engine interprets the page to be about. Document similarity is computed by building a TF-IDF vector for each document and measuring the cosine of the angle between the vectors — two documents discussing the same topic will have high cosine similarity because their high-TF-IDF words overlap. Plagiarism detection tools use the same approach to find suspiciously similar passages. For the practical SEO use case, the word frequency analysis this tool produces is effectively a single-document TF calculation: after stop-word removal, the high-frequency content words are the candidates most likely to earn high TF-IDF scores once weighted against a broader corpus.

Modern transformer-based models like BERT and its successors have moved NLP well beyond bag-of-words representations. They understand that 'bank' means something different in 'river bank' and 'bank account', that negation changes meaning, and that sentence structure matters — none of which raw word frequency can capture. However, word frequency analysis has not been rendered obsolete. For quick content auditing, editorial review, keyword density checks, and vocabulary diversity analysis, a frequency table computed in milliseconds in a browser is orders of magnitude cheaper than running a transformer inference job, requires no infrastructure, and produces immediately interpretable results. TF-IDF itself remains in wide production use inside Elasticsearch and Apache Solr for full-text search. Understanding what your text's frequency distribution looks like is the fastest first-pass diagnostic available — which is exactly what this tool provides.