Word frequency counter
Paste any text and see its most frequent terms with counts and a visual ranking — stopwords filtered, everything computed in your browser.
Runs 100% in your browser — this page makes no network request with your data.
- 1
Paste text: survey answers, articles, transcripts, reviews.
- 2
Top terms appear instantly, with common stopwords filtered (toggleable).
- 3
Send the frequency table to the workspace to chart it or cross it with other data.
Worked example: finding the themes in 3,000 open-ended answers
A product manager runs a survey and ends up with 3,000 open-ended answers to "what should we build next?". Reading them all is impractical, so they paste the responses into the word frequency counter to see what people actually keep asking for.
The top terms appear instantly, with common stopwords like "the" and "and" filtered out so real signal rises to the top. "Mobile", "export", and "integrations" cluster near the top of the ranking, giving the PM an evidence-based read on demand instead of a gut feeling shaped by the loudest few emails.
A ranked term list is a starting point, not the finished analysis, so the PM sends the frequency table to the workspace. There they can chart the top terms, cross them against respondent segment or plan tier, and assemble a shareable report that backs the roadmap conversation with numbers rather than anecdotes.
A word frequency counter tells you which terms appear most often in a body of text, turning a wall of open-ended feedback into a ranked list of what people actually keep saying. Paste survey answers, reviews, or transcripts into the tool above and the top terms appear instantly, with common filler words filtered out, all computed in your browser.
Frequency is a starting point, not a conclusion. A ranked term list points you at what a large text is about and what shows up most, but the counts mislead in predictable ways, and turning them into a real finding takes a few deliberate steps beyond the raw ranking.
What word frequency reveals in open-ended feedback
The single most useful thing frequency gives you is an evidence base instead of a gut feeling shaped by the loudest few responses. When 3,000 people answer an open-ended question, you cannot read every line, and the three angry emails in your inbox are not a representative sample. Counting the words tells you what the whole set keeps raising.
It also surfaces the language your audience actually uses, which is often not your internal vocabulary. Customers say slow and confusing where your team says latency and onboarding friction. Seeing their words ranked is useful for writing copy, tagging tickets, and naming features in terms people recognize. And a rising term across time, week over week in support tickets, is an early signal that something changed.
Why raw counts mislead without stop-words
Left unfiltered, the top of any frequency list is the, and, to, of, and a, function words that carry no meaning and drown out the signal. Filtering these stop-words is the first correction, and the tool above does it by default with a toggle to include them when you want.
But stop-word filtering is not the end of the cleanup. Two subtler problems remain, and both inflate or scatter counts in ways that hide the real pattern:
- Domain stop-words: after filtering the, the next tier is often product, service, company, generic to your context and equally uninformative. Mentally discount them, or treat them as a second stop-list, so real signal like refund or crash rises.
- Word forms scatter: run, running, and ran count as three different terms, splitting one idea across three rows. Stemming or lemmatizing, collapsing forms to a root, consolidates them so the true frequency of the concept shows.
- Case and punctuation: Good and good, or crash and crash!, can count separately unless the tool normalizes them. Confirm your counts are case-folded before you read too much into a split.
Single words versus phrases: why n-grams carry the meaning
One word rarely tells you enough. Customer is among the most common terms in any support corpus and means almost nothing on its own; customer service, or poor customer service, tells you what people are actually talking about. Phrases, n-grams in the jargon, are where the meaning lives.
The vocabulary is simple: a single word is a unigram, a two-word phrase a bigram, a three-word phrase a trigram. Bigrams and trigrams disambiguate words that are meaningless alone, wait becomes long wait time, and they cluster related mentions that single-word counts scatter.
The counter above ranks individual words, which is the right first pass for a fast read of a text's vocabulary. For phrase-level frequency, and for charting the counts or crossing them with other fields, send the text to the workspace, where it can be analyzed for two- and three-word phrases with full context rather than word by word.
From counts to themes: the coding bridge
Counts do not equal themes, and treating them as if they do is the most common mistake. A theme is a reason or an idea; a word is just a token that may belong to several themes or none. Bridging the gap takes three moves.
First, read the frequent terms in context, not as a list. The same word can carry opposite meanings, fast can be praise (fast delivery) or complaint (fast to break), and only the surrounding sentence tells you which. Pull the actual lines behind a top term before you interpret it. Second, pair frequency with polarity: run the responses through sentiment analysis so you know whether a dominant term rides positive or negative feedback, because frequency alone has no sign. Third, group the terms into named themes and count how often each theme, not each word, appears; that coding step is the actual analysis, and qualitative data analysis covers doing it rigorously. From there, cross the themes against segment or plan and package the result with survey analysis.
Where word frequency earns its keep
The same paste-and-rank move applies across several jobs, each with a slightly different read on the output.
| Use case | What the counts tell you |
|---|---|
| Survey open-ends | The dominant topics across all respondents, an evidence base instead of the loudest few |
| Product reviews | Recurring praise and complaint terms; pair with sentiment to sort which is which |
| Support tickets | The most common issue vocabulary, and rising terms that flag a new problem early |
| SEO content audits | Which terms your draft actually emphasizes, a rough proxy for keyword focus |
Frequently Asked Questions
Everything you need to know about using AnalyzeData.
Finding the dominant themes in open-ended survey answers, checking keyword density in writing, and getting a fast read on what a large body of text is about.
Yes — a standard stopword list is filtered by default, with a toggle to include everything.
No — tokenization and counting run in your browser. Paste-and-count is instant precisely because there is no server round-trip.
Yes. Paste your article or page copy and the counter ranks the most frequent terms with exact counts, which is a quick proxy for keyword density. Toggle the stopword filter off if you want function words counted too. It runs in your browser with no upload, so you can check draft content that is not published yet without it leaving your machine.
It ranks individual words, giving you each term's count and a visual ranking of the most frequent ones. For phrase-level or two-word theme analysis, and for charting the counts or crossing them with other fields, send the frequency table to the workspace, where the text can be analyzed with full context rather than word by word.
Because they are frequent but uninformative, a second tier of noise below the usual stop-words. Standard filtering removes the, and, and of, but domain-generic terms like product, service, or good survive and can dominate. Treat them as domain stop-words to discount mentally, and focus on the specific terms beneath them, refund, crash, shipping, where the actual signal lives.
Usually yes, because otherwise one idea is split across several rows and its true frequency is understated. Collapsing forms to a common root, called stemming or lemmatizing, consolidates run, running, and ran into a single count. A simple word counter treats them separately; when the scattering hides a pattern, analyze the text in a workspace that can normalize word forms first.
They use the same underlying counts, but a word cloud sizes words by frequency for a quick visual impression while a counter gives you the exact numbers and ranking. The cloud is better for a glance in a presentation; the ranked list is better for analysis, because you can see precise counts, compare terms, and decide which ones to investigate rather than just eyeballing relative sizes.
Frequency has no sign, so a top term could be praise or complaint, you cannot tell from the count. Read a sample of the lines containing the word to see how it is used, since the same term can swing both ways depending on context. Running the responses through sentiment scoring alongside the frequency count is the fastest way to attach a rough polarity to each dominant term.
When a table isn't enough
The workspace runs verified AI analysis on the same file and turns the results into a report you can send.
Analyze this data instead