Analyze Product Feedback in YouTube Comments

Created by

Apizy

Actor

YouTube Comment Scraper [FAST] | Likes, Replies & Authors

Collect public YouTube comments on a product video for market research. This example uses an MKBHD reaction video and exports comment text, authors, likes, reply counts, and dates. Review the dataset to find objections, expectations, and questions people raise about the product; change the video URL for your market.

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YouTube Comment Scraper [FAST] | Likes, Replies & Authorsapizy/youtube-comment-scraper

Comment id

Text

Author name

Author

+11 fields

Input

Max Comments:20

Sort By:top

Video URL(required):https://www.youtube.com/watch?v=scwlR0QHGOk

Output fields

Comment id

Text

Author name

Author

Avatar

Published at

Published at raw

Likes

Replies

Has Replies

Is reply

Parent comment id

Pinned

Edited

Video id

How it works

Sign up on Apify or log in

The YouTube Comment Scraper [FAST] | Likes, Replies & Authors Actor is ready to run with the inputs already set for "Analyze Product Feedback in YouTube Comments". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "Analyze Product Feedback in YouTube Comments" task

The YouTube Comment Scraper [FAST] | Likes, Replies & Authors Actor will start running based on the input automatically. You can also change all the input fields, like max comments, sort by, and more, to match your specific use case.

Receive the "Analyze Product Feedback in YouTube Comments" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as comment id, text, author name, and more.

Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Analyze Product Feedback in YouTube Comments" task via API.