comment-mining

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Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.

AI & Automation 2,792 stars 32 forks Updated 1 months ago MIT

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Skill Content

# Comment Mining ## Overview Mine public comments for what people actually ask, complain about, want, misunderstand, or repeat. The output should help with product research, content ideas, copywriting, objection handling, and audience understanding. ## When to Use Use this skill when the user asks to: - analyze comments on a TikTok, YouTube video, Instagram Reel, Facebook post, Reddit post, or Rumble video - find audience questions, objections, complaints, or buying intent - extract voice-of-customer language - find content ideas from comments - understand sentiment around a post, creator, product, or topic ## Comment Sources | Platform | Endpoint | |---|---| | TikTok comments | `/v1/tiktok/video/comments` | | TikTok replies | `/v1/tiktok/video/comment/replies` | | YouTube comments | `/v1/youtube/video/comments` | | YouTube replies | `/v1/youtube/video/comment/replies` | | Instagram comments | `/v2/instagram/post/comments` | | Facebook comments | `/v1/facebook/post/comments` | | Facebook replies | `/v1/facebook/post/comment/replies` | | Reddit comments | `/v1/reddit/post/comments` | | Rumble comments | `/v1/rumble/video/comments` | ## Workflow 1. **Fetch comments** - Use the post/video URL whenever possible. - Paginate when the endpoint supports it and the user wants depth. - Preserve comment text, author if public, like/upvote count, timestamp, and source URL. 2. **Clean lightly** - Remove obvious spam/duplicates. - Keep slang, misspellings, and emot...

Details

Author: ScrapeCreators
Repository: ScrapeCreators/social-media-research-skills
Created: 3 months ago
Last Updated: 1 months ago
Language: Python
License: MIT

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