Random Word Frequency List Generator
Generates a list of random words with simulated frequency counts for testing word clouds and NLP tools
About this generator
Word clouds, NLP demos, and text-analytics dashboards all need one awkward ingredient before any real corpus exists: a list of words with believable counts attached. This generator produces exactly that — up to 50 distinct terms drawn from a fixed pool of technical-sounding words like algorithm, cascade, and threshold, each paired with a random integer count in the format 'word: 78'. Two inputs shape the output. Number of Words sets how many pairs you get — the pool holds 50 terms, so that is the practical ceiling — and Max Frequency caps the counts anywhere from 10 to 1,000, letting you match the scale your chart or parser expects. Counts are drawn uniformly, not on a Zipf curve, which is fine for testing font scaling, parsing, and layout but won't mimic natural-language statistics. Words never repeat within a single run; across runs you will see the same vocabulary reshuffled, which helps when you want test fixtures that feel consistent from session to session.
How to use
- Set the Number of Words field to the vocabulary size your tool needs to handle.
- Set Max Frequency to match the count range your visualisation or algorithm expects.
- Click Generate to produce a fresh word-frequency list with randomly selected words.
- Copy the output and paste it directly into your word cloud library, NLP script, or CSV file.
- Re-click Generate to get a different dataset for regression testing or additional mockups.
Use Cases
- •Testing d3.js or WordCloud2.js layouts before loading real corpus text
- •Populating a demo analytics dashboard with believable term-frequency data
- •Stress-testing a Python NLP tokenisation pipeline with varied vocabulary sizes
- •Generating mock TF-IDF input to validate scikit-learn matrix-building code
- •Creating live word-frequency examples for corpus linguistics classroom exercises
Tips
- →Set Max Frequency to 10 and word count to 50 to simulate a low-signal corpus where most terms are rare — good for testing how your tool handles flat distributions.
- →Use two separate runs with different Max Frequency values to compare how your word cloud handles narrow versus wide frequency ranges in the same layout.
- →For client mockups, generate at 30 words and Max Frequency 500 — this range produces visually varied clouds without overwhelming the layout with tiny text.
- →If your NLP pipeline uses a stop-word filter, paste the output through it after generating — this validates that filtered words don't break your frequency matrix.
- →Combine two generated lists by merging their word-count pairs to simulate a larger corpus built from multiple documents, a common real-world NLP input pattern.
- →When testing responsive or canvas-based word clouds, generate at 20, 50, and 100 words sequentially to catch layout breakpoints before they appear in production.
FAQ
how do I feed this output into a Python word cloud
Split each line on the colon, cast the count to an int, and build a dict. Then call WordCloud().generate_from_frequencies(your_dict). The 'word: count' format needs only one split per line, so a three-line loop covers the whole parsing step.
why do the frequencies look uniform instead of like real language
Counts are drawn uniformly at random between 1 and your Max Frequency, so there is no Zipf-style long tail the way natural text has. That is fine for testing rendering, parsing, and font scaling, but if you are validating statistical behavior, apply your own power-law transform to the counts after generating.
what happens if I request more than 50 words
The pool contains 50 fixed terms and a run never repeats a word, so requests above 50 return exactly 50 pairs. If you need a larger vocabulary, generate several runs and merge them — counts differ across runs, so deduplicate on the word and keep whichever count you prefer.
what max frequency should I set for a word cloud preview
Keep the default 100 when you want readable proportional sizing across the batch. Push the ceiling toward the 1,000 maximum to stress-test font-scaling logic against extreme count spreads — that is where clipping and overlap bugs usually surface in word cloud libraries.
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