YouTube Comments Scraper — Extract Comment Threads & Sentiment Data

Extract comment threads from any YouTube video at scale. Get structured JSON with comment text, author details, like counts, reply counts, timestamps and full reply threads — ready for sentiment analysis, brand monitoring and audience research.

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What you get

• videoId — YouTube video identifier
• commentId — Unique comment ID
• text — Full comment text content
• author — Display name of the commenter
• authorChannelUrl — Link to the commenter's YouTube channel
• likeCount — Number of likes on the comment
• replyCount — Number of replies to a top-level comment (0 on reply rows)
• publishedAt — Age of the comment as YouTube renders it, e.g. "1 year ago" (relative text, not an ISO timestamp)
• updatedAt — Always null; this feed does not expose a comment edit timestamp
• isReply — true when the row is a reply to another comment
• parentCommentId — ID of the top-level comment the reply belongs to (null on top-level rows)

Replies are returned as flat rows, not nested objects — rebuild a thread by grouping on parentCommentId.

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Input parameters

Field	Type	Description	Required
videoUrl	string	Full YouTube video URL	Yes (or videoIds)
videoIds	array	List of YouTube video IDs to process	No
maxComments	integer	Maximum comments to extract per video	No
includeReplies	boolean	Whether to include reply threads	No
sortBy	string	Sort order: top or new	No
minLikes	integer	Only return comments with at least this many likes	No

Defaults and limits for every field are shown in the Input tab — it is generated from the Actor's own input schema, so it is always current.

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Example input

{
  "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "maxComments": 100,
  "includeReplies": true,
  "sortBy": "top"
}

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Example output

A real row from a run of this Actor:

{
  "videoId": "dQw4w9WgXcQ",
  "commentId": "Ugzge340dBgB75hWBm54AaABAg",
  "text": "can confirm: he never gave us up",
  "author": "@YouTube",
  "authorChannelUrl": "https://www.youtube.com/@YouTube",
  "likeCount": 313000,
  "replyCount": 963,
  "publishedAt": "1 year ago",
  "updatedAt": null,
  "isReply": false,
  "parentCommentId": null
}

Reply rows have the same keys, with isReply: true and parentCommentId set to the top-level comment's ID.

If a video has comments disabled, the run finishes with zero items rather than an error.

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Use cases

Brand sentiment monitoring: Track what viewers say about your brand, product launch video, or sponsored content. Identify praise, complaints, and feature requests in bulk.

Content moderation research: Analyze comment toxicity, spam patterns, or policy violations across a channel. Feed data into classification models for automated moderation.

Audience research: Understand what resonates with your audience. Mine top-liked comments to discover which messages, jokes, or reactions your viewers amplify.

Competitive analysis: Scrape competitor video comments to understand their audience pain points and desires. Use insights to sharpen your own content and product positioning.

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Pricing

See the Pricing tab on this Actor's page for the current, authoritative pricing — it is the only place that is guaranteed to be up to date.

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Get started

Run YouTube Comments Scraper on Apify