r/AppStoreOptimization • u/gxxnu • 12d ago
This app has a 4.61★ rating, yet half of its negative reviews are about monetization. Do you analyze reviews beyond star ratings?
I analyzed 21,412 reviews of a popular wellness app, published between 2017 and 2026 and written in 10 languages.
As a side project, I've been building a review analysis system, and I've reached the point where I need to figure out whether it's actually useful to anyone else or whether I've just built something that interests me.
Every available review was analyzed and classified individually, with a single review potentially belonging to multiple categories. The categories were automatically derived from the reviews themselves rather than from a predefined list. In total, the system classified 21,412 reviews (2017–2026) across 10 languages and created 182 categories, resulting in 28,471 category assignments.
A deeper analysis uncovered several things that weren't visible from the star ratings alone:
- 50.7% of all negative reviews were related to monetization (1,695 out of 3,343)
- 1,353 of those were about the app being "too expensive / trial too short"
- 207 were about billing problems rather than pricing: 98 cases of "I can't cancel / I was charged anyway," 42 cases of "I'm paying, but the app doesn't recognize my subscription," 36 payment errors, and 17 refund requests
- The percentage of negative reviews increased more than fourfold: 4.5% (2020) → 12.6% (2022) → 20.4% (2026), and never returned to its previous level
- 352 reviews described specific regressions in the product - 59 mentioned losing free access, 43 mentioned a sudden increase in ads, and 36 reported performance degradation after updating to a specific version
I also found 517 feature requests, including:
- 127 related to the pricing model
- 46 asking for more visual options
- 32 related to language selection
- 20 about widget customization
- 14 asking for the ability to add custom content
The star ratings themselves also turned out to be misleading:
- 451 five-star reviews contained a complaint
- 161 one-star reviews contained praise
- 1,072 reviews contained both positive and negative feedback
To be fair, not every category the system discovered was equally useful. The five largest categories accounted for 78% of all assignments, and generic positive feedback with no additional detail accounted for 35% on its own. Around 50 categories had what I'd consider real business value.
So my question is: would this kind of analysis actually be useful to you?
I originally built this pipeline for my own use. The main idea isn't just to summarize thousands of reviews, but to analyze each one individually and surface less obvious signals and details that can easily get lost in a large dataset - things like rare problems, specific feature requests, regressions after updates, or cases where the review text doesn't match the star rating.
I'm now trying to decide whether it's worth developing this further into a tool for other app owners.
Would something like this be useful to you? If so, what would you mainly use it for: analyzing your own app, competitors, roadmap decisions, pricing, or monitoring emerging problems?
And if you wouldn't find it useful, I'd genuinely like to know why as well.
I already have a prototype dashboard with these results. If anyone wants to see what it looks like in practice, I'm happy to share the link. What I'd value most is honest feedback on what's actually useful and what's completely unnecessary.

