🧠 Text Visualization: Concepts, Techniques, and Applications¶

📌 What is Text Visualization?¶

  • Graphical representation of unstructured text to reveal:
    • Patterns
    • Trends
    • Relationships
  • Helps users understand large volumes of text without reading it all.
  • Often powered by language models that extract word counts, sequences, and sentiment.

🧱 Thinking of Text as Data¶

  • Text carries meaning, structure, and context beyond raw characters.
  • Data sources: articles, books, emails, blogs, programs, Wikipedia, social media, etc.
  • Text has:
    • Order (e.g., March → April → May)
    • Grouping/Membership (e.g., swimming, hiking)
    • Relationships (e.g., synonyms, antonyms, entities)

🔄 Preprocessing Pipeline for Visualization¶

  1. Tokenization: Segment text into words.
  2. Stop Word Removal: Remove non-informative words like "the", "a", "of".
  3. Stemming: Reduce words to root form (e.g., "visualization" → "visual").
  4. Vectorization:
    • Bag of Words: Frequencies without order.
    • Bigrams: Consecutive word pairs to preserve relationships.

📊 Core Visualization Techniques¶

1. Word Cloud¶

  • Displays word frequency via size and color.
  • Common, intuitive way to summarize text.
  • Tool: wordclouds.com

2. Word Tree¶

  • Tree-like structure showing how a keyword is used in different contexts.
  • Reveals relationships and common phrases.
  • Example: “Thank you” → “for sharing”, “so much”

3. Term Frequency Heatmap¶

  • Colors represent frequency intensity.
  • Useful for analyzing word distribution across documents or positions.
  • Example: Frequency of letters starting or ending English words.

4. Network Graphs¶

  • Nodes = words, Edges = co-occurrence (e.g., bigrams).
  • Shows how words are related through proximity.
  • Best for revealing semantic or syntactic relationships.

🕓 Visualizing Evolving Documents¶

Why It Matters¶

  • Helps track changes, collaboration, and content evolution in living documents.
  • Common in Google Docs, Wikipedia, and academic writing.

Techniques¶

✅ Timeline-Based¶

  • Shows document revisions over time.

✅ Diff-Based¶

  • Highlights additions, deletions, and edits between versions (e.g., track changes in Word or Wikipedia).

✅ Contributor-Focused¶

  • Visualizes contributions by each author (e.g., percent of content changed).

Examples¶

  • Darwin’s Evolution Editions: Paragraph-level diff visualization over 7 editions.
  • History Flow (Wikipedia):
    • Tracks article revisions by contributor.
    • Color-coded by user.
    • Patterns (e.g., zigzags) can indicate arguments or edit wars.
  • Wikipedia Live Edit Sound Map:
    • Real-time edit activity.
    • Circle size = edit size; sound pitch = frequency.

💬 Visualizing Conversations¶

Key Dimensions:¶

  • Who: Senders/receivers
  • What: Content/topics
  • When: Timing & frequency
  • Cross-products:
    • Who talks to whom
    • Who talks about what at what time

Techniques¶

✅ Network Graph¶

  • Nodes = participants, Edges = interactions
  • Shows who interacts with whom

✅ Timeline Visualization¶

  • Messages plotted chronologically
  • Useful for analyzing tempo and speaker activity

✅ Conversation Maps¶

  • Tree structure of reply chains
  • Shows structure of back-and-forth exchanges

✅ Sentiment Heatmap¶

  • Color-coded emotional tone over time
  • Tracks emotional flow of conversations

✅ Social Proxies (e.g., Babel System)¶

  • Dots represent users in a circular layout.
  • Distance to center = level of participation.
  • Useful for revealing conversation balance (e.g., dominant speakers vs group dialogue).

🔗 Resources Mentioned¶

  • WordClouds.com
  • WordTree Generator
  • WordCon.org
  • Wikipedia History Flow Project
  • Live Wikipedia Sound Visualization (link in course resources)