Sentiment analysis is the automated reading of text to judge whether it expresses a positive, negative, or neutral attitude. Applied to ad comments, it lets a moderation system sort an incoming flood into rough buckets so a human does not have to open and read every single reply.
For a busy ad account that is the difference between keeping up and falling behind. Sentiment signals can route an angry or complaint-laden comment to someone who can respond quickly, hold a likely-spam reply for a closer look, and help surface the warm ones — a "where do I buy this?" reads very differently from "scam." Pairing sentiment with intent is how a ad comment moderation workflow turns raw volume into a short, prioritized queue.
It is not magic, and we are honest about that. Sarcasm, slang, mixed messages, and missing context all trip up automated sentiment — "great, another broken order" is positive on the surface and negative in reality. For that reason the right design treats sentiment as a triage hint, not a verdict: anything the model is unsure about gets flagged for human review rather than acted on automatically. For how this plays out with the hardest comments, see negative comment management for paid ads.