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I run bdccomenets. We classify YouTube comments into a fixed taxonomy
(content idea / audience question / criticism / praise) with weight
derived from likes + replies, then cluster recurring requests across
all uploads on a channel — not per-video.
This post is the reasoning behind the design: why per-video paste
tools miss patterns, why keyword search ("feature", "wish", "please")
catches ~30% of real requests, and why a fixed taxonomy beats
free-form tagging once a team grows past one person.
Would love feedback on the taxonomy and edge cases I'm not seeing.
ForesynWanna keep in touch?
Built this solo over a weekend. Soft-launching before the HN post on Monday. If you scored a draft and the prediction either nailed it or whiffed, I want to know.