Word Cloud

Word clouds create visual representations of text data where word size corresponds to frequency or importance. They provide an immediate visual summary of the most prominent terms in a text corpus, making them popular for social media analysis, survey responses, and content summarization. While not precise for data analysis, word clouds are excellent for communication and exploration.

Example Visualization

Word cloud from speech text with cool color palette

Try this prompt

"Create a word cloud from a 'Customer Feedback Survey' analyzing 500 product reviews. Generate realistic text data with common themes: positive words (excellent, amazing, quality, fast, reliable, love, recommend) and some negative (slow, expensive, difficult, issue, problem). Weight word frequency realistically: 'quality' (150), 'service' (120), 'recommend' (95), etc. Remove standard English stop words. Use a custom colormap (cool blues and greens for professional look). Set max_words=100, prefer_horizontal=0.7. Use a rectangular mask. Add a title 'Customer Feedback Word Cloud (n=500 reviews)' and subtitle showing sentiment ratio (positive vs negative)."

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Python Code Example

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Common Use Cases

1Social media trend analysis
2Survey response summarization
3Document theme extraction
4Brand perception visualization

Pro Tips

Remove stop words for meaningful results

Consider TF-IDF weighting over raw frequency

Use custom masks for creative shapes