Capture repeated comment rows and replies
Extract usernames, comment bodies, reply structures, and visible engagement fields from repeated discussion layouts.
YouTube Comments Scraper
Extract YouTube comments and replies into structured datasets
DataLens is a good fit when you need a YouTube comments scraper for research, moderation review, creator ops, or feedback analysis. Capture visible comments and reply threads in the browser, then export the structured result in the format your team needs.
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Useful for research and moderation review
Export structured discussion data for sentiment review, feedback clustering, creator ops, or community analysis outside the browser.
Choose the export format that fits your workflow
Use Excel or CSV for spreadsheet review, or JSON when your team needs a more structured payload for downstream systems.
How this workflow fits real website data extraction jobs
Review audience feedback and recurring themes across visible YouTube comment sections.
Capture creator-community discussion data for research, moderation, or product analysis.
Export structured comment rows that are easier to filter and analyze than raw page HTML.
How it works
- 1
Open the YouTube video page or comment view you want to analyze.
- 2
Use DataLens to detect repeated comment rows and visible reply structures.
- 3
Review the extracted discussion dataset and export it to Excel, CSV, or JSON.
Frequently asked questions
These are the most common questions teams ask before using DataLens for this workflow.
Can a YouTube comments scraper capture replies?
Yes. DataLens is designed to identify repeated comment layouts and visible reply structures so they can be organized into a structured export.
What can I do with exported YouTube comment data?
Teams use it for audience research, feedback review, moderation analysis, sentiment work, and any workflow that benefits from structured comments in Excel, CSV, or JSON.
Is JSON better than CSV for comment exports?
JSON is often the better fit when reply structure matters, while CSV is useful for faster spreadsheet-style analysis of flattened comment records.
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