domains / families / marketing / comment-mining

comment-mining

combined

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. Mine comments and replies on social posts, videos, and ads for recurring customer language, questions, objections, desired outcomes, complaints, product requests, purchase signals, and creative opportunities. Use for voice-of-customer research grounded in linked source evidence.

When to use

• 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

When not to use

• flattening comments into generic sentiment
• including personally identifying details unless public and necessary
• treating bot/spam comments as audience signal
• skipping Reddit post context

Skill page

Conflict

Both copies describe the same task—mining public social comments for voice-of-customer signals—but each contains material steps the other lacks. The gooseworks copy adds a distinct buying-intent strength scale, a broader coding taxonomy (e.g., churn risk, workaround, exact product language), and a requirement to report prevalence as sample counts by platform/source type. The ScrapeCreators copy adds concrete API endpoints per platform, a required environment variable, a specific output template, and platform-specific pitfalls (e.g., read Reddit post context). Crowning one would hide these real alternatives.