This Jupyter Notebook project analyzes YouTube video content and audience reactions using publicly available data from the YouTube Data API. It pulls metadata like video titles, descriptions, and comments from multiple channels, and applies Natural Language Processing (NLP) to detect patterns in tone, sentiment, and engagement.
By analyzing both creator intent (via titles/descriptions) and audience response (via top-level comments), the tool offers powerful insights
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Content Strategy Optimization: Identify what types of videos perform well or poorly, so you can double down on successful content and phase out what underperforms
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Trending Topics Detection: Spot topics with high positive engagement and leverage those macro trends to attract more viewers and subscribers
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Audience Misalignment: Discover where creators may be missing the mark so they can avoid repeated mistakes and improve audience satisfaction
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Collaboration Opportunities: Analyze where different channels share similar audience behavior, helping creators partner effectively to boost engagement and find pain points they share
Whether you're a content creator, social media analyst, or brand strategist, this project helps quantify and visualize messaging tone and community feedback across any set of YouTube channels.
- Pulls metadata and top-level comments from multiple YouTube channels
- Applies NLP-based sentiment analysis to titles, descriptions, and comments
- Flags positive/negative audience sentiment trends
- Visualizes insights with intuitive graphs for quick analysis
Displays sentiment scores for videos across all selected channels, grouped by channel for comparison.
Ranks videos by sentiment score to highlight most positive and most negative content.
Examines viewer comments to identify:
- Videos with strong negative/positive reception
- Common keywords/themes using word clouds or frequency plots
- Audience mood trends over time
| Tool / Library | Purpose |
|---|---|
| YouTube Data API | Fetch video titles, descriptions, comments |
| Python | Programming language |
| Jupyter Notebook | Interactive coding environment |
| NLTK (SentimentIntensityAnalyzer) | Sentiment analysis |
| Matplotlib & Seaborn | Data visualization |
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API Authentication Connect to YouTube using a developer API key
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Data Collection Pull video metadata (title, description, views, likes) and top-level comments
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Sentiment Analysis Apply
NLTK’s sentiment scoring to each text field (titles, descriptions, comments) -
Trend Detection Flag low-performing videos, trending topics, and audience sentiment patterns
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Visualization Graph insights to understand content tone and audience engagement
- Identify videos with negative sentiment or poor reception
- Track which themes resonate most with audiences
- Uncover trending topics across content creators
- Compare tone between channels (e.g., educational vs. entertainment)
- Pinpoint collaboration opportunities with shared audience sentiment
Let me know if you'd like this saved as a .md file or want help integrating comment analysis in code.