Video Dashboard

All sections are recommended. The user can deselect any they don't want.

Step 2: Configure topic keywords

Topic analysis uses keyword matching against transcripts. The default categories are generic:

TOPIC_KEYWORDS = {
    "politics": ["government", "policy", "legislation", "law", "vote"],
    "economy": ["job", "business", "economy", "wage", "worker", "tax"],
    "health": ["health", "hospital", "mental health", "doctor", "care"],
    "education": ["school", "student", "teacher", "education", "university"],
    "environment": ["climate", "green", "pollution", "sustainability"],
    "technology": ["tech", "digital", "software", "AI", "data"],
    "community": ["community", "neighborhood", "local", "together"],
    "safety": ["crime", "police", "safety", "violence", "security"],
}

Ask the user: "Want to customize the topic categories for this subject, or use the defaults?" If the subject is a politician, suggest political topic categories (housing, transit, budget, immigration, etc.).

Step 3: Run content analysis

Generate four JSON files in analysis/:

topics.json — keyword frequency per video, per platform, and overall:

{
  "overall": {"topic": count, ...},
  "per_platform": {"twitter": {"topic": count}, ...},
  "per_video": {"video_id": {"title": "...", "platform": "...", "topics": {...}}}
}

sentiment.json — positive/negative/urgent scoring per video:

{
  "per_video": {"video_id": {"raw_counts": {...}, "dominant_tone": "urgent"}},
  "per_platform": {"twitter": {"positive": N, "negative": N, "urgent": N, "count": N}}
}

cross-platform.json — platform comparison metrics:

{
  "platforms": {
    "twitter": {
      "video_count": N, "total_words": N, "avg_duration_seconds": N,
      "avg_words_per_video": N, "top_words": {"word": count, ...}
    }
  }
}

summary.json — high-level overview stats:

{
  "total_videos": N, "total_duration_minutes": N, "total_words": N,
  "platforms": [...], "top_topics": [...],
  "dominant_tone_distribution": {"urgent": N, "positive": N, ...}
}

Step 4: Generate the dashboard

Build a single HTML file at web/index.html with:

• Zero-build architecture: CDN-loaded Chart.js and Google Fonts, all CSS/JS inline
• Inline SVG favicon (no external files needed)
• Dark theme with editorial typography
• Platform color-coding: Twitter blue, TikTok pink, YouTube red, Instagram gradient, Facebook blue
• Data loading: Fetch JSON from relative paths (../analysis/*.json, ../metadata.json)
• Graceful degradation: Show "data not yet available" for missing sections

Data normalization layer: The dashboard should normalize field names on load to handle variations in analysis script output. Map common patterns:

• overall / frequencies (topics)
• per_video / by_video
• per_platform / by_platform

Dashboard sections (based on user selection):

• Overview stats with large monospace numbers
• Filterable video grid with platform badges and transcript accordion
• Full-text transcript search with debounced input and highlighted matches
• Topic frequency horizontal bar chart (Chart.js) with clickable topic pills
• Sentiment doughnut chart + per-platform stacked bars
• Cross-platform comparison panels with top word lists

Step 5: Test the dashboard

Start a local server and verify:

cd {project-dir} && python -m http.server 8888
# Open http://localhost:8888/web/index.html

Check: charts render, video grid populates, search works, platform filters work across sections.

Step 6: Commit and report

Commit the analysis script, JSON outputs, and dashboard. Report key findings:

• Top topics with counts
• Dominant tone distribution
• Cross-platform patterns (which platform has longest videos, most words, etc.)

Key lessons

• Field name normalization is critical: If the analysis script and dashboard are written separately (or by different subagents), field names will diverge. Add a normalization layer in the dashboard's data loading step.
• total_words not automatic: The analysis script may not calculate total word count. Add it to summary.json by counting words across all transcript .txt files.
• Cross-platform top_words format: The analysis script may output {"word": count} objects, but the dashboard may expect [{word, count}] arrays. Normalize on load.
• Stopword filtering matters: Remove common English stopwords from cross-platform top words, or the lists will be useless (all "the", "is", "and").

Reference scripts

• Content analysis: ${CLAUDE_PLUGIN_ROOT}/scripts/build-analysis.py