A transparent positive/negative/neutral breakdown of a video's real comments — simple word-count heuristic, not a black-box AI model.
Paste a video URL. We pull real comments via the official API and tag each one using a fixed, visible list of positive/negative words.
Every word in the positive/negative list is real, ordinary English vocabulary — you can see exactly why a comment was tagged the way it was, unlike a black-box sentiment model.
Pulls real comments via the official API and tags each one against a fixed, visible list of positive/negative English words.
Use the percentage split as a rough gauge, and spot-check individual comments for context the heuristic misses.
This is a simple, fixed word-count heuristic — explicitly not a trained AI sentiment model, and it can miss sarcasm or context.
No — it's a simple, fixed word-count heuristic: real comments are checked against a visible list of positive and negative English words. It's transparent, not a trained NLP model, and can miss sarcasm or context.
Up to 300 real, current top-level comments via the official YouTube Data API.
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