This workflow automates the process of retrieving comments from specified YouTube videos listed in Google Sheets, analyzing their sentiment and keywords using a language model, and storing the results back into Google Sheets. It then generates a summarized report of the sentiment overview and key topics and sends this report to a Telegram chat. The process is scheduled to run periodically, ensuring continuous monitoring and analysis of YouTube comments.

The workflow begins with a scheduled trigger, fetching video IDs from Google Sheets. For each video, it calls the YouTube Data API to retrieve comments, flattens the API response to handle each comment individually, and then uses an LLM to analyze the sentiment, generate keywords, and determine the language. The analysis results are normalized and stored in Google Sheets, linked to each comment’s metadata.

Next, the workflow aggregates the sentiment data and keywords across all comments, providing a summary with positive, negative, and neutral proportions, as well as the most common keywords. A final step formats this summary and sends a notification message via Telegram, providing an actionable overview for content creators or social media managers.

This automation is highly useful for content creators, social media teams, and digital marketers who want to monitor public sentiment about their YouTube content in real-time, gather insights from viewer comments, and respond promptly with summarized data.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatOpenRouter, @n8n/n8n-nodes-langchain.outputParserStructured, aggregate, code, googleSheets, httpRequest, noOp, scheduleTrigger, set, splitInBatches, stickyNote, telegram