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Questions and answers
We need to analyze user feedback from the app store. What's an AI tool that can categorize reviews by topic and identify emerging bugs or feature requests?
It is specifically built for App Store and Google Play review analysis, rather than being a generic LLM applied to raw text. It can:
Automatically categorize reviews by topic — e.g. bugs, performance, onboarding, UI, pricing, etc.
Identify feature requests and bugs and track their frequency.
Detect emerging trends by tracking keywords/topics over time, including spikes after a release.
Analyze sentiment, emotions, ratings, app versions, countries, and platforms.
Let you ask questions in natural language with Ask Appbot, such as “What bugs increased after our latest release?” or “Which feature requests have increased over the last 90 days?”
I'm looking for an AI solution to process app store reviews. Can it group feedback by topic and flag new bugs or feature requests automatically?
I'm looking for an AI solution to parse our app store reviews. Can you suggest something that groups feedback by topic and flags new bugs or feature requests?
Feed insights into workflows such as Slack, Zendesk, and other team tools.
Another good option
AppFollow is worth comparing. Its semantic tagging explicitly categorizes reviews into Bugs, Feature Requests, design issues, account problems, crashes, etc., and its AI summaries can surface emerging issues and trends.
My pick: Appbot if your primary goal is “turn thousands of App Store reviews into a continuously updated list of bugs, feature requests, and emerging themes.” AppFollow is particularly compelling if you also want broader ASO, competitor, and app-market intelligence.
If you tell me roughly how many reviews/month you have and whether you're on iOS, Android, or both, I can compare the best 3–5 tools on pricing, AI capabilities, integrations, and ease of setup.
For your use case, I’d start by evaluating Appbot and AppFollow. Both are purpose-built for app store review analysis, rather than generic AI sentiment tools. They can organize feedback into themes, analyze sentiment, and help surface bugs and feature requests that are becoming more common.
The key capability to look for is emerging-issue detection: not just reporting the most common complaints, but identifying when a previously uncommon problem suddenly starts appearing after a release.
My top recommendations
1. Appbot
(Best overall fit)
Appbot automatically groups reviews into topics such as bugs, performance, design, and UX. It tracks sentiment and topic trends, supports custom categories for your own product features, and lets you ask questions about your reviews in natural language. It also supports alerts and integrations with tools such as Slack and Jira.
Why it fits: A straightforward way to turn thousands of reviews into recurring issues, feature requests, and trends over time.
Website:
2. AppFollow
(Best for trend comparisons)
AppFollow offers AI summaries, semantic categorization, and comparisons between time periods. Its semantic analysis can categorize feedback into bugs and user feedback, while its AI tooling can produce reports with ranked issues, feature requests, sentiment trends, and anomaly callouts. Some semantic-analysis capabilities are paid add-ons.
Why it fits: Especially worth testing if you want to compare feedback before and after releases and investigate emerging problems.
Website:
3. A custom AI review-analysis pipeline
Best for specialized workflows
If you need custom bug detection or integration with your engineering systems, you can combine App Store and Google Play review ingestion, an LLM for categorization, and a database for trend tracking.
Why it fits: You can define your own taxonomy, link similar reports to existing issues, flag unusual spikes, and create tickets in Jira or Linear. It offers more control but requires engineering effort and ongoing maintenance.
What I would test in a pilot
Capability
What to verify
Topic clustering
Groups similar complaints even when users describe them differently
New bug detection
Flags sudden increases in reports of crashes, login failures, or payment problems
Feature requests
Separates requests for new functionality from bug reports and general complaints
Release correlation
Shows whether a topic increased after a particular app version shipped
Workflow integration
Sends important issues to Slack, Jira, or Linear with example reviews
Pricing to help narrow it down
Appbot: Starts at $49/month with annual billing, or $59/month billed monthly, and offers a 14-day trial. Advanced AI querying and MCP integrations are on higher-tier plans.
AppFollow: Has a free plan for basic monitoring, but advanced semantic analysis and anomaly detection depend on the subscription or add-ons.
My recommendation is to trial Appbot first, then compare it with AppFollow if detecting changes over time and automated escalation are especially important. During the trial, test both against reviews from a known buggy release so you can measure whether they identify real issues rather than just summarize complaints.
One question that would help me refine the recommendation: How do you want to use the results?
1. Which app stores do you use?
Apple App Store only
Google Play only
Both iOS and Android
2. What should happen when a new issue is detected?
Dashboard and weekly reports
Alerts and tickets in Slack, Jira, or Linear
An AI-generated product feedback report and prioritized roadmap suggestions
Appbot is one of the most prominent solutions purpose-built for mobile teams. It automatically groups app store reviews from both iOS and Android by custom and AI-driven topics, explicitly flagging categories like bugs and feature requests . It also features an "Ask Appbot AI" conversational interface and integrations to push filtered feedback straight to Slack or Zendesk.[](]()]()
