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Honest note: this tool counts tokens, not meanings — “run” and “running” are counted as different words. No stemming or lemmatization is applied, and the built-in stop-word list is English-only. Click any row to copy it.
Try an example: sample paragraph
Every writer has favourite words they reach for without noticing. For one person it is "really" and "very", for another it is "just" and "actually". Readers notice this repetition long before the writer does, and search engines notice it too. A word frequency counter turns that invisible pattern into hard numbers: paste in your text and it shows exactly which words you use most, how many times each appears, and what share of your text every word represents.
This is useful far beyond curiosity. Bloggers use frequency analysis to check keyword density and catch accidental keyword stuffing before they hit publish. Students use it to see whether an essay leans too heavily on a handful of transition words. Editors run manuscripts through it to find crutch words an author can no longer see. Content marketers compare the vocabulary of competing articles to discover which terms a topic is expected to contain. If you work with words for a living, knowing your word distribution is a genuine advantage.
The FistHost word frequency counter does all of this instantly and privately. There is no signup, no upload and no waiting — your text is analysed inside your own browser and never sent to any server.
The tool reads your text and breaks it into tokens. Tokenizing simply means splitting the text wherever whitespace or punctuation appears, so "hello, world!" becomes two tokens: hello and world. Everything is lowercased before counting, so "The" and "the" are treated as the same token and counted together. Each unique token gets a running total, the totals are sorted from highest to lowest, and the result appears as a ranked table with a bar visualization beside every row so you can see the distribution at a glance.
Alongside each raw count you also get a share percentage — the word's count divided by the total number of tokens, shown as a percent. This makes frequencies comparable between texts of different lengths: a word used twenty times means something very different in a 200-word paragraph than in a 5,000-word chapter. A top-N slider controls how many rows the table shows, a minimum word-length filter lets you drop short tokens, and the stop-word toggle removes the most common English filler words using a built-in list of roughly 150 stop words. When stop words are filtered out, the real content words of your text rise straight to the top.
One honest note about how the counting works: this tool counts tokens, not meanings. The words "run" and "running" are counted as two completely different tokens — the tool performs no stemming or lemmatization, so different forms of the same word are never merged. The stop-word list is English-only, so if you analyse text in another language you should switch the stop-word filter off and rely on the minimum-length filter instead. Share percentages are calculated over the tokens that remain after your filters are applied.
For SEO work, the table is a fast keyword-density check. If your target keyword accounts for four percent of tokens you are probably over-optimizing; if a competing article mentions a key term twenty times and yours mentions it twice, you have found a content gap. Pair the frequency counter with our word counter for total word counts and our readability checker for grade-level scoring, and you have a complete pre-publish audit in under a minute.
For editing and proofreading, give your attention to the top of the table. The most frequent content words are usually fine — they are the topic of the piece — but frequent adverbs and filler phrases are almost always worth cutting. Many writers keep a personal list of overused words and run every draft through the counter before publishing. Students can check an essay for overused transitions, and researchers can get a quick quantitative feel for an unfamiliar body of text before deeper analysis begins. Over time, repeated checks train you to spot your own patterns while you write, so the first draft needs less fixing and the final draft reads more cleanly to every audience it reaches.
The CSV export makes the tool useful for larger projects as well. Export the table, open it in a spreadsheet, and you can compare word distributions across multiple drafts, chapters or competing articles side by side. Content teams use this to keep terminology consistent across writers; novelists use it to check that a character's dialogue vocabulary stays distinct from the narrator's voice. Because the export contains exact counts rather than rounded percentages, it also works as input for deeper statistical analysis if you ever need it.
Want to try the tool right now? Generate test copy with the lorem ipsum generator and run it through the counter to see the table, bars and filters in action. And when a document is simply too long to analyse comfortably, our text summarizer condenses it first — then the frequency counter shows you what the summary is really about.
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It counts how many times each unique word appears in your text and presents the results as a ranked table, usually with percentages and a visual chart. It is the simplest form of text analysis and the starting point for keyword research, editing and content auditing.
No. This tool counts raw tokens, not meanings, and it performs no stemming or lemmatization. "Run", "runs" and "running" appear as three separate entries in the table. This keeps the counting transparent and predictable — what you see is exactly what is in the text.
No. The entire analysis runs in JavaScript inside your browser. Your text never leaves your device, so you can safely analyse private drafts, client work or unpublished manuscripts without any privacy concerns.
Stop words are extremely common words that carry little meaning on their own — "the", "and", "is", "in", "to" and similar. They almost always top any frequency table, which hides the interesting content words. The built-in English stop-word list of about 150 words removes them with one toggle so your real vocabulary stands out.
Each word's count is divided by the total number of tokens counted, then shown as a percentage. The denominator respects your filters: if stop words are filtered out, percentages are calculated over the remaining tokens, which is why they add context instead of merely echoing the raw counts.
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