YouTube Comment Scraper

What is YouTube Comment Scraper?

YouTube Comment Scraper is a data extraction tool designed to batch collect comment content, commenter information, likes, replies, author interaction, and other data from public YouTube videos via video ID. Using CoreClaw, you can obtain structured data with zero coding, helping with content analysis and user research.

• ✅ Comment Content Extraction - Automatically scrape comment text, comment time, likes, and other basic information
• ✅ Commenter Information Collection - Extract commenter username, channel link, user ID, and other user data
• ✅ Reply Level Scraping - Obtain comment replies, nested replies, and complete conversation chains
• ✅ Author Interaction Data - Track whether video author liked or replied to the comment

What data can you extract from YouTube comments?

How to scrape YouTube comments via video ID?

CoreClaw YouTube Comment Scraper handles proxy rotation, task scheduling, data standardization, and final delivery for you in the background. In just a few minutes, you can obtain data through the following steps:

1. Create a free CoreClaw account using your email
2. Open the YouTube Comment Scraper control panel
3. Enter YouTube video ID
4. Click "Start" and let our cloud servers handle the scraping work
5. Download the dataset in JSON or CSV format

➡️ Input

Enter YouTube video ID.

Example: 8RePenzQH80

(Dashboard input image example)

⬅️ Output

For your convenience, output results are displayed in tables and tabs. You can choose to download results in CSV/JSON format. Here is an example of comment data scraped using video ID:

Table Example

(Dashboard output image example)

JSON Example

(JSON output example)

Explore Other YouTube Data Scrapers

If you need to scrape other types of data, CoreClaw offers the following professional toolsets optimized for different scraping scenarios.

FAQ

How many comments can be scraped from one video?

The tool will automatically paginate and scrape all public comments under the video. YouTube comments are dynamically loaded, so it's recommended to scrape in batches to obtain complete data.

How to handle errors during scraping?

The tool supports detailed error feedback and intelligent retry mechanisms. When scraping anomalies occur, the service will return a response containing error codes and detailed prompt information. For temporary errors, the service defaults to automatic retry.

How to identify high-value comments and potential users?

Filter high-value comments through the following data dimensions:

• Comment Length: Long comments usually mean higher engagement and authentic feedback
• Like Count: Highly liked comments reflect user resonance and content quality
• Reply Activity: Comments that spark discussion have more 传播 value
• Commenter Follower Count: Judge influence by combining commenter channel data
• Comment Time: Early comments are more likely to be interacted with by creators

Can comment data be used for sentiment analysis?

Comment data is very suitable for sentiment analysis applications:

• Text Mining: Extract text features such as keywords, topic tags, emojis
• Sentiment Judgment: Identify positive, negative, neutral comments, evaluate content reputation
• User Emotion Tracking: Monitor comment sentiment changes, detect public opinion risks in time
• Competitor Comparison: Compare comment sentiment distribution of similar videos

Can scrape all comments under one channel?

Yes. Use YouTube Channel Scraper, enter video ID, you can batch scrape comments from multiple or all videos under the channel.