YouTube Comments Scraper — Comments & Sentiment

YouTube Comments Scraper — Comments & Sentiment

YouTube Comments Scraper extracts structured YouTube Comments records, metadata, metrics, and direct links in real time. Built on a zero-browser HTTP/2 engine running in 256 MB RAM, it delivers normalized datasets with zero null fields ready for CSV, Excel, JSON, or API pipelines.

Looking for related scrapers? Check out YouTube Scraper, YouTube Transcript Scraper, YouTube Channel & Creator Intelligence Scraper, YouTube Shorts Scraper, or browse our full catalog of 118+ dedicated scrapers.

---

What does YouTube Comments Scraper do?

Ultra-fast YouTube comments scraper powered by Google's internal InnerTube RPC API. Extract video comments, replies, timestamps, like cou.... Compatible Make.com, Zapier, n8n, Sheets & Webhooks. 256MB RAM & sub-second speed.

Whether you need to monitor YouTube Comments at scale, enrich B2B/B2C databases, or feed structured records into analytics and AI workflows, YouTube Comments Scraper handles pagination, field normalization, and schema validation automatically:

• Bulk Data Extraction: Collect YouTube Comments listings, items, and detailed attributes in batches using keywords, filters, or direct URLs.
• Complete Public Metadata: Capture identifiers, pricing/engagement metrics, author/vendor details, timestamps, and canonical URLs.
• Multi-Format Export: Download results in JSON, CSV, Excel (XLSX), XML, or HTML Table directly from the Apify Console.
• Ready for Automation & AI: Connect seamlessly to Apify Integrations (Make, Zapier, n8n, Google Sheets), REST API endpoints, or the Apify MCP Server.

Beyond the Official YouTube Comments API

• No API Keys or Approval Delays: Start extracting public YouTube Comments data immediately without registering developer apps or waiting for quota approvals.
• No Restrictive Rate Quotas: Bypass strict per-minute API limits and expensive enterprise tiers while capturing the complete public data points displayed on YouTube Comments.
• 256 MB Zero-Browser Architecture: Uses native HTTP/2 and TLS session emulation instead of heavy Chromium instances, executing in seconds at a fraction of standard compute costs.

---

What data can you scrape from YouTube Comments?

YouTube Comments Scraper extracts the following data points from YouTube Comments:

🔗 Video URLs (videoUrls)	📝 Sort By (sortBy)	👍 Min Likes (minLikes)
📝 Clean Text (cleanText)	💬 Comment ID (commentId)	🆔 Video ID (videoId)
👤 Author (author)	👤 Channel ID (channelId)	🖼️ Author Avatar (authorAvatar)
🖼️ Author Thumbnail (authorThumbnail)	👤 Is Channel Owner (isChannelOwner)	📝 Text (text)

Data Category	Extracted Fields & Data Points
Identity & Core Identifiers	commentId (Comment ID), videoId (Video ID), channelId (Channel ID)
Metrics, Pricing & Engagement	minLikes (Min Likes), likes (Likes)
Details, Attributes & Context	sortBy (Sort By), cleanText (Clean Text), author (Author), isChannelOwner (Is Channel Owner), text (Text)
Direct Links, Media & Timestamps	videoUrls (Video URLs), authorAvatar (Author Avatar), authorThumbnail (Author Thumbnail), publishedTime (Published Time), videoUrl (Video URL), scrapedAt (Scraped At)

---

Why scrape YouTube Comments? (Typical Use Cases & Guides)

You can use YouTube Comments Scraper for a wide range of data and business workflows:

• Audience Sentiment & Feedback: Analyze user reactions, questions, and feature requests on product demo videos.
• Video Chapter Generation: Extract timestamps mentioned in comments to generate automatic video bookmarks.
• Community Moderation: Identify recurring spam patterns or negative feedback across branded videos.
• AI Agents, RAG & LLM Knowledge Pipelines: Stream clean, structured YouTube Comments JSON records into vector databases, Claude, GPT-4, or LangChain workflows.
• Automated BI Dashboards & Scheduled Alerts: Schedule daily or hourly runs and push live YouTube Comments updates directly to Google Sheets, Looker Studio, Slack, or webhooks.
• Market Research & Competitor Analysis: Benchmark YouTube Comments offerings, pricing strategies, and market share across regions.
• Lead Generation & CRM Enrichment: Build targeted prospect lists and sync verified records straight into HubSpot, Salesforce, or Clay.
• AI Agents & RAG Pipelines via MCP: Connect live YouTube Comments data directly to Claude Desktop, Cursor, or LangChain agents.

---

How to scrape YouTube Comments data

YouTube Comments Scraper is designed for a zero-configuration start, even if you have never scraped data before:

1. Create a free Apify account using your email (includes $5.00 in free monthly credits).
2. Open YouTube Comments Scraper and click on the Input tab — all required fields are already pre-filled with a working example.
3. Enter your search terms, target URLs, or filter criteria (or keep the default pre-filled configuration for an instant 1-Click test).
4. Click the green "Start" button and wait a few seconds for the data to be extracted.
5. Download your dataset in JSON, CSV, Excel (XLSX), or HTML, or fetch it via the API tab.

For more technical details on every parameter, check out the Input Schema tab or read the Apify Platform Documentation.

🔗 How to scrape YouTube Comments by Search Term or Direct URL

Scraping by Search Term / Filter 🔑	Enter keywords, categories, or filter parameters just as you would search on YouTube Comments. Set the maximum number of items you want to extract and click Start.
Scraping by Direct URL 🔗	Paste one or more direct YouTube Comments links (or import a list of URLs from a Google Sheet / CSV) to extract targeted item or profile details in bulk.

---

⬇️ Input Configuration & Examples

To start scraping YouTube Comments, fill in the visual input form in the Apify Console or pass a JSON payload via the API. Full technical specifications are available on the Input Schema tab.

Input Parameters Reference

Parameter	Type	Default / Prefill	Description
videoUrls	array	["https://www.youtube.com/watch?v=5...	List of YouTube video URLs (https://www.youtube.com/watch?v=...) or 11-character video IDs.
sortBy	string	top	Sort comments by Top upvoted or Newest comments first. Options: top, newest.
minLikes	integer	0	Only keep comments with at least this number of thumbs up.
keywordFilter	string	—	Filter comments containing a specific keyword or product term.
cleanText	boolean	false	Normalize comment text, strip emojis and repeated whitespace for clean sentiment analysis.
onlyWithReplies	boolean	false	Only extract comments that sparked replies or active discussion.
maxCommentsPerVideo	integer	40	Maximum number of comments to extract per video.
proxyConfiguration	object	—	Apify Proxy (Residential or Datacenter) to prevent IP rate limits during bulk extraction.

Example 1: Standard Extraction (1-Click Default)

{
  "videoUrls": [
    "https://www.youtube.com/watch?v=5kcaHAuGxmY"
  ],
  "sortBy": "top",
  "minLikes": 0,
  "cleanText": false
}

Example 2: Filtered & Batch Extraction

{
  "videoUrls": [
    "https://www.youtube.com/watch?v="
  ],
  "sortBy": "newest",
  "minLikes": 0,
  "cleanText": true,
  "onlyWithReplies": true,
  "maxCommentsPerVideo": 80
}

---

⬆️ Output Example (Table View, JSON & Fields Reference)

The scraped results are stored in a structured Apify Dataset available in the Storage tab. Below is a preview of the dataset in both Table View (CSV/Excel format) and JSON format.

Table View (CSV / Excel Dataset Preview)

Video URLs	Sort By	Min Likes	Clean Text
https://www.youtube.com/watch...	top	0	false
https://www.youtube.com/watch...	top	1	false
https://www.youtube.com/watch...	top	1	false

JSON Output Example

{
  "videoUrls": [
    "https://www.youtube.com/watch?v=5kcaHAuGxmY"
  ],
  "sortBy": "top",
  "minLikes": 0,
  "cleanText": false
}

Output Fields Reference

Field Name	Type	Description
videoUrls	array	Extracted video urls value.
sortBy	string	Extracted sort by value.
minLikes	number	Extracted min likes value.
cleanText	boolean	Extracted clean text value.
commentId	string	Extracted comment id value.
videoId	string	Extracted video id value.
author	string	Extracted author value.
channelId	string	Extracted channel id value.
authorAvatar	string	Extracted author avatar value.
authorThumbnail	string	Extracted author thumbnail value.
isChannelOwner	boolean	Extracted is channel owner value.
text	string	Extracted text value.
likes	integer / number	Extracted likes value.
publishedTime	string	Extracted published time value.
videoUrl	string	Extracted video url value.
scrapedAt	string	Extracted scraped at value.

---

🔗 Scrape any data you need with dedicated scrapers

Combine YouTube Comments Scraper with our other dedicated scrapers from @RBBscrap if you want to extract specialized data across platforms:

🔹 YouTube Scraper	🔹 YouTube Transcript Scraper	🔹 YouTube Channel & Creator Intelligence Scraper
🔹 YouTube Shorts Scraper	🔹 YouTube Playlist & Course Scraper	🔹 YouTube Trending & Viral Charts Scraper

Scraper	Extracted Data & Primary Use Case
YouTube Scraper	Videos, Shorts & Channels
YouTube Transcript Scraper	Subtitles for AI
YouTube Channel & Creator Intelligence Scraper	Extract structured YouTube Channel & Creator Intelligence datasets and metrics
YouTube Shorts Scraper	Viral Videos, Sounds & Creators
YouTube Playlist & Course Scraper	Extract structured YouTube Playlist & Course datasets and metrics
YouTube Trending & Viral Charts Scraper	Extract structured YouTube Trending & Viral Charts datasets and metrics

---

⚠️ Error Items & Resilience Reference

YouTube Comments Scraper is engineered to process multi-query and batch runs resiliently without crashing if a single query or URL fails:

Status / Code	Meaning & Automatic Handling
OK	Record successfully extracted, validated, and normalized with zero null fields.
DEFAULT_PREFILL_APPLIED	When an empty input payload {} is submitted, the Actor automatically applies its built-in default query so 1-Click runs always succeed.
RATE_LIMIT_RETRY	Transient HTTP 429 / 503 responses trigger automatic retry with exponential backoff and session rotation.
INVALID_TARGET_SKIPPED	Unreachable or malformed target items in a batch are logged with [WARN] and skipped without interrupting the run.

---

❓ Frequently Asked Questions (FAQ)

How much does scraping YouTube Comments cost?

YouTube Comments Scraper uses transparent Pay-Per-Event (PAY_PER_EVENT) pricing set 10x cheaper than the market leader ($\div 10$):

• Event Rate: $0.00025 per Scraped YouTube Comments Scraper Record ($0.25 per 1,000 items).
• 🎁 1st Run 100% FREE: Your very first execution is 100% free ($0.00 event charges are applied on your initial run).
• Apify Free Plan ($5.00/month included): Every Apify account receives $5.00 in free monthly credits, which is enough to extract up to ~20,000 items per month for free.
• View full details on the Pricing tab and the Apify Pricing page.

Can I integrate YouTube Comments Scraper with Make, Zapier, n8n, or Google Sheets?

Yes. YouTube Comments Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform, including Make, Zapier, n8n, Slack, Airbyte, Google Sheets, Google Drive, and webhooks.

Can I use YouTube Comments Scraper with the Apify API (Python & Node.js)?

Yes. The Apify API gives you programmatic access to run YouTube Comments Scraper, schedule jobs, and fetch datasets. Use the apify-client PyPI package for Python or the apify-client NPM package for Node.js, or check the Apify API Reference:

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run = client.actor("rbbscrap/youtube-comments-scraper").call(run_input={
    "videoUrls": ["https://www.youtube.com/watch?v=5kcaHAuGxmY"],
    "sortBy": "top",
    "minLikes": 0,
    "cleanText": False
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

Node.js (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_API_TOKEN' });

const run = await client.actor('rbbscrap/youtube-comments-scraper').call({
    videoUrls: ["https://www.youtube.com/watch?v=5kcaHAuGxmY"],
    sortBy: "top",
    minLikes: 0,
    cleanText: false
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Can I use YouTube Comments Scraper through an MCP Server (Claude Desktop, Cursor, AI Agents)?

Yes. With the Apify API, you can use YouTube Comments Scraper in conjunction with an MCP server to connect directly to Claude Desktop, Cursor, Windsurf, or LibreChat. Read the official guide on how to set up Apify Actors with MCP, or go to the MCP tab:

1. Start a Server-Sent Events (SSE) session to receive a sessionId.
2. Send API messages using that sessionId to trigger YouTube Comments Scraper with your input parameters.
3. Receive structured YouTube Comments dataset records directly inside your AI agent conversation.

Do I need residential proxies or login cookies to scrape YouTube Comments?

No. YouTube Comments Scraper operates on a zero-browser HTTP/2 protocol engine (256 MB RAM) and works out-of-the-box without requiring login cookies or external proxies for standard workloads. For high-volume enterprise runs, you can optionally use Apify Proxy in the input configuration.

Is it legal to scrape data from YouTube Comments?

Our scrapers are ethical and do not extract any private user data behind login walls. They only extract what is shared publicly on the web. However, please keep in mind that personal data is protected by GDPR (EU Regulation 2016/679) and other regulations worldwide. You should not scrape personal data unless you have a legitimate legal basis. Read more in Apify's guide on the legality of web scraping.

Your feedback & support

We are continuously improving the performance of our Actors. If you have any technical feedback for YouTube Comments Scraper, need a custom feature, or found a bug, please create an issue on the Actor's Issues tab in the Apify Console.