📊 Monthly YouTube Sentiment Analysis on Indian Finance Channels (2021–2023)

This project analyzes the sentiment of video titles and top 5 comments from 10 major Indian finance YouTube channels over a 3-year period (2021–2023). The goal is to quantify and track public sentiment around financial topics like SIPs, LIC, Sensex, and market performance.

📂 Files

• sentiment_analysis.ipynb: Main notebook that extracts, cleans, and analyzes sentiment
• youtube_monthly_sentiment.xls: Final monthly sentiment scores (weighted by title and comment)

🧪 Methods

• Data extracted using YouTube Data API (via multiple keys to manage quota)
• Titles and top 5 comments per video were collected monthly for each keyword/channel
• Sentiment calculated using:
  • VADER for comments
  • TextBlob or VADER for titles
• Weighted average:
  • 60% title sentiment
  • 40% average of top 5 comment sentiments

📈 Output

• One sentiment score per month per keyword across all channels
• Output stored in Excel (youtube_monthly_sentiment.xls)
• Ready for downstream econometric or trend analysis

🔧 Tools Used

• Python
• Pandas
• VADER Sentiment
• TextBlob
• YouTube Data API
• Jupyter Notebook

👨‍💻 Author

Mashhood Raza Khan

🔗 LinkedIn Project Page

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