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Analyzing YouTube Data: Comment Sentiment and Metadata Extraction with Python

· 7 min read
Serhii Hrekov
Serhii Hrekov
Senior Software Engineer & System Architect specializing in Python, Web Systems, Cloud Infrastructure & Automation

Analyzing video metrics and comment threads is a powerful way to leverage Python for data science, market research, or content optimization. By analyzing the public reception of a video, you can measure audience mood and extract key metadata features.

This guide demonstrates how to build a complete YouTube data extraction pipeline using two separate strategies:

  1. Metadata Extraction: Retrieving views, tags, categories, and upload details using yt-dlp (no API key required).
  2. Comment Sentiment Analysis: Scoping comment sections to calculate positive or negative audience polarity using the official YouTube Data API v3 and TextBlob.