Word Cloud Generator
Generate a word cloud from pasted text with word frequency analysis, stop word filtering and visual display
Only show words that appear at least this many times
How a Word Cloud Reveals Hidden Patterns
Paste any text into the box and the tool counts how often each word appears, ignoring common stop words ('the', 'and', 'is', 'of') that would otherwise dominate. The most frequent words appear largest in the visual output; the colour cycles through hues so each word is distinct. This visual ranking is genuinely useful: a 5,000-word document compressed into a word cloud often surfaces the actual themes faster than reading the whole thing. A customer feedback dump that looks like 'love' and 'hate' spread evenly might reveal that 'wait', 'queue', and 'slow' all show up 30+ times, pointing to the real complaint.
The tool has a minimum frequency slider so you can hide rare words and focus on the recurring ones. For a small text (a single article, a meeting transcript), set the minimum to 2 or 3. For a large dataset (a year of survey responses), bump it to 10+ to filter noise. The cloud caps at the top 50 words to keep the visual readable; beyond that it's hard to compare sizes meaningfully.
When Word Clouds Are Genuinely Useful
Customer feedback analysis: paste in 100+ survey responses and immediately see what people actually talk about. If 'price' is huge and 'quality' is tiny, the cost/value perception is your top issue regardless of what the survey questions tried to measure. Speech and presentation prep: paste a draft and check whether your key terms appear prominently. If you're presenting on 'sustainability' but the cloud highlights 'we', 'our', 'company', the message isn't landing.
Resume keyword analysis: paste a job description, see the most-used terms, then check that your CV uses the same vocabulary. ATS (applicant tracking systems) often filter on keyword matches, so word clouds are a quick visual diagnostic. Content writing and SEO: paste competitors' top-ranking articles and see which terms recur. A cloud showing 'guide', 'beginner', '2026' tells you Google is rewarding evergreen 'how-to' framing in your niche. The [word counter](/word-counter) gives the same data in numeric form if you need exact counts.
Limitations Worth Knowing
Frequency isn't importance. The word that appears 50 times might be a filler word that escaped the stop list. The word that appears 5 times might be the most valuable insight. Word clouds are a starting point for analysis, not the analysis itself. They also lose context completely - 'no problem' and 'problem' both contribute to the 'problem' count even though they mean opposite things. Sentiment analysis tools handle this; word clouds don't.
Stop words are language-specific. The default stop list is English; the same text in French would see 'le', 'la', 'de' dominating because those aren't in the list. Multi-word concepts get split: 'customer service' shows as two separate words even though the meaningful unit is the phrase. For deeper text analysis, the [readability score checker](/readability-score-checker) examines structure rather than word frequency.
Frequently Asked Questions
What stop words are filtered automatically?
Common English stop words: 'the', 'be', 'to', 'of', 'and', 'a', 'in', 'that', 'have', 'i', 'it', 'for', 'not', 'on', 'with', 'as', 'you', 'do', 'at', and similar function words. The list includes about 40 of the most common English words, which removes the bulk of grammatical noise without filtering meaningful terms.
Can I add my own stop words?
Not directly in the current version. If you want to exclude a specific word (your company name, a recurring filler term), find-and-replace it out of the source text before pasting. For example, if you're analysing customer feedback and 'company' isn't useful, replace 'company' with nothing in your source, then paste.
Does case matter (Word vs word)?
No - the tool lowercases all text before counting, so 'Word', 'WORD', and 'word' all merge into a single entry. This is usually what you want; capitalisation differences are rarely meaningful for frequency analysis.
What's the maximum text size?
There's no hard limit but performance degrades with very large texts. A novel-length input (50,000+ words) takes a few seconds to process and the resulting cloud caps at 50 words anyway, so most of the data is discarded. Sweet spot is 500-10,000 words; beyond that, manual filtering of the source is more effective than relying on the tool.
How do I save the word cloud as an image?
Take a screenshot of the visual output. The tool focuses on instant analysis rather than export; for a publication-quality word cloud with custom shapes and colours, dedicated tools (WordItOut, Wordclouds.com) offer more control. For business reports, the table of word counts at the bottom often communicates more than the visual.