How it works
Sentiment analysis uses language models to read review text and assign a tone (positive / negative / neutral) and often topics such as "login", "price", "crash" or "ads". The star rating alone is a rough signal; text tells you why.
What teams use it for
- Spotting trends: "complaints about battery drain doubled since version 5.2".
- Prioritizing the roadmap by how often a feature request appears.
- Comparing with competitors — see competitor app analysis.
- Reporting to stakeholders who won't read hundreds of reviews.
Limits
Short reviews, sarcasm and mixed opinions ("love it but it crashes") are hard to classify. For small and mid-sized apps, reading every review as it arrives — via app review monitoring — is often more useful than a dashboard of percentages.
Example
An analysis of 5,000 reviews shows "subscription" in 40% of negative reviews but only 5% of positive ones — a clear sign the paywall, not the product, drives low ratings.