This article provides a complete, practical walkthrough of the Splitout Noop Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automate YouTube Comment Sentiment Analysis with n8n, OpenAI & Google Sheets
   
   Meta Description:
   Learn how to set up a no-code workflow using n8n to fetch comments from YouTube, analyze sentiment with OpenAI, and log the results into Google Sheets. Fully automated, scalable, and customizable.
   
   Keywords:
   n8n workflow, YouTube sentiment analysis, YouTube comments, OpenAI GPT-4, Google Sheets API, video comment analysis, automation, no-code sentiment analysis, YouTube Data API v3, OpenAI comment classification, AI comment tagging, automation for creators
   
   Third-Party APIs Used:
   1. YouTube Data API v3 – To fetch video comments
   2. OpenAI API (GPT-4 & Sentiment Analysis) – To perform sentiment analysis on comments
   3. Google Sheets API – To read/write/update video URLs and comments in two sheets
   
   Article:
   
   📊 Automate YouTube Comment Sentiment Analysis with n8n, OpenAI & Google Sheets
   
   If you're a content creator, marketer, or data enthusiast who wants deeper insights into audience sentiment on YouTube videos, this n8n workflow is your perfect launchpad. Imagine automatically fetching new comments from your selected videos, analyzing the emotional tone of each comment, and logging everything neatly into Google Sheets—without writing a single line of code.
   
   This article guides you through a powerful, AI-powered YouTube Comment Sentiment Analyzer built with n8n, powered by OpenAI’s GPT technology for real-time sentiment analysis, and integrated with Google Sheets for seamless data storage and reporting.
   
   🚀 Overview: What Does the Workflow Do?
   
   Here’s the end-to-end breakdown of what happens when you execute this workflow:
   
   1. Fetch video URLs from a designated Google Sheet (used as the data source).
   2. For each video, check if it should be analyzed based on time triggers (last fetched and next fetch time).
   3. Use YouTube Data API to pull the latest comments for each video.
   4. Analyze the sentiment of each comment using OpenAI’s sentiment model.
   5. Structure and reformat the data including author name, likes, replies, and sentiment category.
   6. Append or update this information into a second Google Sheet with all historical and new comments.
   7. Update the timestamp for when comments were last fetched to control future fetches.
   
   🧠 Key Components in the Workflow
   
   Let’s explore the main building blocks of this solution:
   
   📥 1. Google Sheets Input (Sheet2)
   The workflow starts by reading from a Google Sheet (“Sheet2”) that contains YouTube video URLs under the column video_urls. It also tracks the last_fetched_time and next_fetch_time, which allow it to avoid re-querying when it’s not needed.
   
   ⏱ 2. Time-Based Logic
   Two IF condition nodes determine the eligibility for a fetch. If next_fetch_time is either empty or in the past, the workflow proceeds; otherwise, it skips to the NoOp (No Operation) node.
   
   🎥 3. Fetch Comments with YouTube Data API
   Using the YouTube Data API v3, the workflow requests up to 100 top-level comments per video in JSON format. Paging is handled via nextPageToken to ensure all comments are pulled during a given run.
   
   💬 4. Sentiment Analysis via OpenAI
   For each retrieved comment, the workflow sends its text to OpenAI's sentiment analyzer via a LangChain wrapper inside n8n. The AI evaluates the tone and classifies the comment as Positive, Neutral, or Negative.
   
   📄 5. Format and Log Comments (Sheet1)
   The sentiment result is combined with other metadata like:
   - commentId
   - comment text
   - author name
   - number of likes
   - reply count
   - publish date
   - video URL
   
   All of this data is inserted or updated into a second sheet (“Sheet1”), serving as your finalized sentiment dashboard.
   
   📆 6. Update Fetch Timestamps
   After processing, the original data sheet is updated with a new last_fetched_time and a calculated next_fetch_time (set to 5 minutes later), keeping everything in sync and schedules intact.
   
   🧰 Technologies and APIs Used
   
   A powerful combination of APIs is responsible for different parts of this workflow:
   
   - YouTube Data API v3: Fetch comments directly from YouTube videos based on videoId.
   - OpenAI GPT-4: Highly accurate language processing used for sentiment classification.
   - Google Sheets API: Serves as both the input and output data warehouse, making it easy to interface with your team, create dashboards, or build chart visualizations.
   
   🥇 Benefits of This Workflow
   
   - 100% automated: Once configured, you can set it to run on a schedule (e.g., with a Cron node instead of manual trigger).
   - Customizable sentiment engine: Swap out the sentiment prompt or categories based on your context.
   - Scalable: Works with 1 or 100 video URLs.
   - Visual & accessible: Google Sheets format makes it easy to collaborate with team members or build reports from the data.
   - Privacy-respecting: Only pulls public data and processes it within controlled infrastructure.
   
   📝 Required Setup
   
   To get this up and running, you’ll need:
   
   - n8n installed (cloud or self-hosted)
   - Google Cloud Platform service account for Google Sheets
   - API key for YouTube Data API v3
   - OpenAI API Key with access to the GPT-4 model
   
   👨‍💻 Pro Tips
   
   - Replace the Manual Trigger node with a Cron node in production for interval-based execution.
   - Use filters or conditional formatting in Google Sheets to auto-tag highly liked negative comments for closer review.
   - Extend the workflow by adding Slack or email alerts for particularly toxic comments or trending positive feedback.
   
   🔚 Wrapping Up
   
   This YouTube Comment Sentiment Analyzer demonstrates the power of no-code tools like n8n combined with cutting-edge AI. Whether you’re assessing viewer feedback, doing brand monitoring, or running research for content optimization, this workflow saves hours of manual review and delivers structured feedback in real-time.
   
   Bring sentiment insight to your fingertips—automated, intelligent, and all in spreadsheets. Ready to give it a try?
   
   —
   
   Need help setting it up? The sticky note inside the workflow offers step-by-step instructions and a helpful structure to manage your Google Sheet setup effectively.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.