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YouTube Comment Sentiment Analyzer

This workflow automatically reads YouTube video links from Google Sheets, captures comment data in real-time, and uses an AI model to perform sentiment analysis on the comments, classifying them as positive, neutral, or negative. The analysis results are updated back to Google Sheets, ensuring consistency and timeliness in data management. By supporting pagination for comment retrieval and allowing flexible update frequencies, it greatly enhances the ability of content creators and brand teams to gain insights into audience feedback, helping to optimize content strategies and market responses.

Workflow Diagram
YouTube Comment Sentiment Analyzer Workflow diagram

Workflow Name

YouTube Comment Sentiment Analyzer

Key Features and Highlights

This workflow automatically reads YouTube video URLs from Google Sheets, fetches the corresponding video comments in real-time, and leverages OpenAI’s GPT-4o-mini model to perform intelligent sentiment analysis on the comments (classifying them as positive, neutral, or negative). The analysis results along with detailed comment data are synchronized back to Google Sheets. It supports paginated retrieval of comments to ensure data completeness. By automatically updating timestamps, it guarantees the timeliness and continuity of data collection.

Core Problems Addressed

  • Automates bulk extraction of YouTube video comments, eliminating manual and tedious operations
  • Utilizes advanced AI models for accurate sentiment classification, enabling users to quickly gain insights into audience feedback trends
  • Centralizes management of comment data and sentiment analysis results for easier subsequent data analysis and decision-making support
  • Supports scheduled or manual triggers, providing flexible control over data update frequency

Use Cases

  • Content creators and marketers monitoring video comment feedback to optimize content strategies
  • Brand and PR teams analyzing user sentiment trends to promptly respond to negative feedback
  • Data analysts collecting user feedback data to support market research and user behavior analysis
  • Educational and research institutions studying social media public opinion dynamics

Main Workflow Steps

  1. Read YouTube video URLs and last fetch timestamps from Google Sheets (Sheet2)
  2. Determine if the next fetch time condition is met to avoid duplicate data retrieval
  3. Use YouTube Data API v3 to batch fetch video comments, supporting pagination
  4. Perform sentiment analysis on each comment text using OpenAI GPT-4o-mini model, classifying as positive, neutral, or negative
  5. Format comment details and sentiment results, including comment ID, author, like count, reply count, publish time, and other fields
  6. Append or update the comment data and sentiment analysis results back into Google Sheets (Sheet1)
  7. Update the fetch timestamp to ensure accurate timing for the next data retrieval

Systems and Services Involved

  • Google Sheets: Stores video URLs and comment data, enabling data reading and writing
  • YouTube Data API v3: Retrieves video comment information
  • OpenAI API (GPT-4o-mini model): Conducts sentiment analysis on comment texts
  • n8n Automation Platform: Orchestrates the automated execution of each step, supporting manual triggers and logical conditions

Target Users and Value

  • YouTube content creators and operations teams seeking rapid understanding of audience sentiment to optimize video content and engagement strategies
  • Marketing and brand management professionals who need to monitor public sentiment on brand-related videos and adjust marketing plans accordingly
  • Data analysts and researchers requiring systematic social media data collection for sentiment trend analysis and user behavior studies
  • Automation enthusiasts and technical teams looking for integrated multi-platform API solutions to enable intelligent social data processing

By integrating Google Sheets, YouTube API, and OpenAI’s intelligent analysis, this workflow automates the collection and sentiment analysis of YouTube comments, significantly enhancing data processing efficiency and analytical depth, empowering users to accurately grasp the emotional dynamics of their video audiences.