YouTube Comments Scraper

This project pulls complete YouTube comment data fast, giving you clean and structured insights from any video. It captures full comment text, authors, metadata, and engagement signals — all without the limits of standard APIs. Ideal for research, analytics, and automation workflows.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This scraper collects every publicly available comment from selected YouTube videos and organizes the results into a consistent dataset. It solves the challenge of manually gathering comment data at scale and is built for analysts, developers, creators, and anyone who needs precise comment insights.

Why This Matters

• Helps explore viewer feedback and sentiment at scale.
• Automates data collection for large video lists.
• Captures engagement signals not available through basic tools.
• Supports detailed research and competitive monitoring.
• Works for trend analysis and community insights.

Features

Feature	Description
Full comment extraction	Collects every available comment from selected video URLs.
Metadata-rich output	Includes authors, IDs, engagement counts, timestamps, and flags.
Multi-format downloads	Export your dataset as JSON, CSV, Excel, XML, or HTML.
Multi-video support	Add single or multiple URLs, including bulk imports.
Creator engagement flags	Detects creator hearts and channel-owner comments.

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What Data This Scraper Extracts

Field Name	Field Description
comment	Full text of the YouTube comment.
cid	Unique comment ID.
author	Username of the commenter.
videoId	Unique video identifier.
pageUrl	URL of the scraped video.
commentsCount	Total number of comments on the video.
replyCount	Number of replies for the comment.
voteCount	Like count for the comment.
authorIsChannelOwner	Indicates if the commenter is the channel owner.
hasCreatorHeart	Whether the creator hearted the comment.
type	Defines whether the item is a comment or a reply.
replyToCid	The parent comment ID (if reply).
title	The title of the corresponding YouTube video.

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Example Output

[
  {
    "comment": "This is up there with their best songs.",
    "cid": "UgxRn0_LUxzRP2MybPR4AaABAg",
    "author": "@Nonie_Jay",
    "videoId": "bJTjJtRPqYE",
    "pageUrl": "https://www.youtube.com/watch?v=bJTjJtRPqYE",
    "commentsCount": 171,
    "replyCount": 0,
    "voteCount": 2,
    "authorIsChannelOwner": false,
    "hasCreatorHeart": false,
    "type": "comment",
    "replyToCid": null,
    "title": "Halestorm - Unapologetic [Official Audio]"
  }
]

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Directory Structure Tree

YouTube Comments Scraper/
├── src/
│   ├── runner.js
│   ├── extractors/
│   │   ├── youtube_parser.js
│   │   └── time_utils.js
│   ├── outputs/
│   │   └── exporters.js
│   └── config/
│       └── settings.example.json
├── data/
│   ├── input.sample.txt
│   └── sample.json
├── package.json
├── requirements.txt
└── README.md

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Use Cases

• Creators use it to understand viewer sentiment so they can refine future content.
• Marketing teams use it to track brand mentions and discover competitor audience behavior.
• Researchers collect large-scale comment datasets to study trends and public opinion.
• Community managers monitor discussions to identify harmful or inappropriate content.
• Data analysts use it to run sentiment analysis and spot engagement patterns.

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FAQs

Is scraping YouTube comments allowed? Only publicly available information is collected. Users should ensure they have valid reasons for storing or processing personal data. When unsure, seek legal guidance.

Do I need proxies? For high-volume comment scraping, rotating or residential proxies help maintain stability and reduce request failures.

Can I automate or schedule runs? Yes, this scraper can be integrated into scheduled tasks or connected to external systems using API endpoints.

Can I use this programmatically? You can connect through standard HTTP APIs using tools like Node.js or Python to trigger runs and fetch results.

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Performance Benchmarks and Results

Primary Metric: Handles an average of 8,000–12,000 comments per minute depending on video size and network conditions.

Reliability Metric: Maintains a 97%+ successful retrieval rate across large batches of URLs.

Efficiency Metric: Processes multiple videos in parallel with moderate CPU usage and steady memory consumption.

Quality Metric: Captures over 99% of visible comments with consistent metadata completeness and minimal duplication.

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