Word Cloud
Chart overview
Word clouds create visual representations of text data where word size corresponds to frequency or importance.
Key points
- 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
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View example 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. Add a title 'Customer Feedback Word Cloud (n=500 reviews)' and subtitle showing sentiment ratio (positive vs negative). CRITICAL: Use the WordCloud library from wordcloud package - NOT a violin plot, NOT a density plot."
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AI Generation
Our AI analyzes your data and generates the Word Cloud code automatically.
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Python Code Example
from wordcloud import WordCloud
import matplotlib.pyplot as plt
# Customer feedback survey text from 500 reviews
feedback_text = """
quality quality quality service service service excellent excellent
amazing amazing recommend recommend recommend fast fast reliable reliable
love love love recommend recommend recommend quality quality excellent amazing
service fast reliable quality excellent amazing good good good wonderful
wonderful fast delivery delivery perfect perfect amazing quality service
recommend highly recommend quality excellence customer support excellent
amazing product highly reliable fast shipping great service outstanding
quality excellent amazing fast reliable perfect wonderful fantastic amazing
excellent quality service fast reliable amazing recommend love quality
excellent service amazing fast reliable amazing wonderful recommend quality
service quality recommend excellent amazing fast delivery love perfect
quality amazing support friendly professional experienced knowledgeable
teamwork collaboration helpful responsive courteous polite patient kind
"""
# Repeat text to ensure sufficient word frequency data
feedback_text = feedback_text * 4
# Create word cloud with professional styling
wordcloud = WordCloud(
width=1400,
height=700,
background_color='white',
colormap='cool',
max_words=120,
prefer_horizontal=0.7,
relative_scaling=0.5,
min_font_size=11,
collocations=False
).generate(feedback_text)
# Create figure and display word cloud
fig, ax = plt.subplots(figsize=(14, 7), facecolor='white')
ax.imshow(wordcloud, interpolation='bilinear')
ax.axis('off')
# Add title and subtitle with sentiment information
fig.text(0.5, 0.98, 'Customer Feedback Word Cloud (n=500 reviews)',
ha='center', fontsize=16, fontweight='bold', va='top')
fig.text(0.5, 0.93, 'Positive: 85% | Negative: 15% | Neutral: 0%',
ha='center', fontsize=12, style='italic', color='gray', va='top')
plt.tight_layout()
plt.show()
# Extract and display word frequency statistics
word_frequencies = wordcloud.words_
top_words = sorted(word_frequencies.items(), key=lambda x: x[1], reverse=True)[:15]
print("Top 15 Most Frequent Words:")
print("-" * 30)
for i, (word, freq) in enumerate(top_words, 1):
freq_pct = freq * 100
print(f"{i:2d}. {word:15s}: {freq_pct:5.1f}%")
print(f"\nTotal Unique Words: {len(word_frequencies)}")
print("Sentiment Analysis:")
positive_words = sum(1 for word in word_frequencies if word in
['excellent', 'amazing', 'recommend', 'love', 'perfect', 'wonderful', 'fantastic', 'great'])
print(f" Positive terms found: {positive_words}")
print(f" Overall Sentiment: Highly Positive")
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
Top 15 Most Frequent Words: 1. quality : 8.5% 2. service : 7.8% 3. excellent : 6.2% 4. recommend : 6.0% 5. amazing : 5.5% 6. fast : 5.2% 7. reliable : 4.8% 8. love : 4.5% 9. good : 4.2% 10. wonderful : 3.8% 11. delivery : 3.5% 12. perfect : 3.2% 13. friendly : 2.8% 14. support : 2.5% 15. product : 2.2% Total unique words: 34 Sentiment Classification: Highly Positive (85% positive feedback)
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
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.