Sentiment analysis is a technique that uses AI to work out the tone of written or spoken feedback. It turns thousands of reviews, survey comments, social posts and emails into measurable categories such as positive, neutral or negative, and often the topics behind them.
Modern tools use language models that understand context, so "the wait was short but the coffee was cold" is split into a positive about speed and a negative about quality. Results are tagged by theme, location, product or date, so you can see trends rather than reading everything.
Retailers and hospitality groups use it to compare stores, spot a recurring problem such as slow service on weekends, and track how a menu or policy change landed. It can also flag urgent complaints so managers respond quickly.
Sarcasm, slang and mixed comments can fool the model, so spot-check results against the raw text. Reviews skew toward extremes, so treat them as a signal, not a full picture. Check the tool covers all the platforms you care about, such as Google, social and surveys, and keep humans responsible for acting on the findings.
It saves multi-site operators the effort of reading every comment while still surfacing what customers actually care about. A monthly review of top themes by location is usually enough to turn the data into a short list of practical fixes.