Sentiment analysis

Automated classification of review text into positive, neutral, or negative.

Sentiment analysis classifies free-text reviews into positive/neutral/negative buckets and often extracts topics (crashes, ui, performance, value, etc.). Done well, it lets a small team triage hundreds of reviews per day without reading each. Modern sentiment classification uses LLMs (Rank Sonar uses Claude Haiku) which are dramatically more accurate than keyword-based approaches at handling sarcasm, context, and nuanced opinions. The output drives alerting (sudden negative-sentiment spike) and roadmap prioritization (most-cited topics).