“Crashes every time I open it after 4.2.1. Unusable on iPhone 15.”
crashesloginiPhone 15v4.2.1
NLP-Powered Keyword and Topic Extraction for App Reviews
AppReviewBot's NLP pipeline reads every review as it arrives and pulls out the keywords, topics, and entities buried in the text — so you're working with structured tags instead of a wall of raw text, across iOS, Google Play, and across languages.
Keyword & topic extractionCross-platform: iOS & Google PlayMulti-language comprehension
Extracted keywords
Raw review text
“App crashes on login after the update. Also got charged after I cancelled.”
NLP extract
Keywords are extracted automatically as reviews arrive
Keyword extraction
Turn raw review text into tags you can filter on.
Every App Store and Google Play review is scanned as it arrives. AppReviewBot pulls out meaningful keywords automatically — crash, login, refund, billing, sync — so you are not maintaining keyword lists by hand or reading every review to spot the pattern.
- ✓Automatic keyword extraction on every new review
- ✓No manual tagging queues or brittle keyword lists to maintain
- ✓Use the same tags for search, Slack routing, and reports
Extraction preview
Review → structured tags
“Still being charged after cancelling Pro. Need a refund.”
billingcancelrefund
Topics & entities
Cluster themes and recognize what users name.
Related reviews group into topics so you can see which themes are rising across a release — not one complaint at a time. Entity recognition picks up feature names, device models, and error terms so you can filter for exactly what people are talking about.
- ✓Topic and theme clustering across the review set
- ✓Entities for features, devices, and error language
- ✓Consistent extraction across iOS and Google Play
Themes this week
FitLife · App Store + Play
Login / crash
48 · 22%
splash screeniPhone 15v4.2.1
Billing / refund
31 · 14%
Pro plancancelcharged twice
Dark mode praise
27 · 13%
dark modeworkout plans
Powers the rest of the product
Structured tags feed routing, sentiment, and the API.
NLP is the extraction layer underneath AppReviewBot. The same keywords and topics power Slack/Teams filters (crash, refund, login), sentiment trends, and custom pipelines via the API — so you are not running a separate tagging tool.
- ✓Route high-risk keywords into eng or support channels
- ✓Feed sentiment analysis and theme reporting with the same tags
- ✓Pull keywords, topics, and entities through the API
Where tags go next
One pipeline · multiple outcomes
Routing rule
keyword: crash OR login → #eng-alerts
Sentiment slice
Negative + topic: billing → support queue
API export
GET reviews with keywords[], topics[], entities[]
What gets extracted from every review
Automatic keyword extraction
Every review is scanned as it arrives and the meaningful keywords are pulled out automatically — no manual tagging or keyword lists to maintain.
Topic & theme clustering
Related reviews are grouped by topic, so you can see which themes are showing up across a whole review set instead of reading one at a time.
Entity recognition
Feature names, device models, and error terms are recognized as entities, so you can filter for exactly what's being talked about.
Cross-region comprehension
Comprehension works across regions and languages without a manual translation step to set up first.
Feeds sentiment & routing
The same extraction pipeline powers the sentiment trends and routing rules used elsewhere in AppReviewBot — it's the layer underneath both.
Available via the API
Pull extracted keywords, topics, and entities through the API to build your own custom pipelines and reports.
Pull extracted keywords, topics, and entities through the API to build your own custom pipelines and reports. See the API docs →
3-step extraction pipeline
01
Reviews are ingested
Reviews come in from iOS App Store and Google Play as they're posted — no manual export needed.
02
NLP extracts structure
The pipeline reads each review and pulls out keywords, topics, and entities automatically.
03
Tags power the rest of the product
Structured tags feed the filters, routing rules, and sentiment trends you use elsewhere in AppReviewBot.
Want to see the trend those keywords feed into? See sentiment analysis →
Frequently asked questions
What does the NLP tool actually extract from a review?+
Each review is broken into keywords, topic clusters, and named entities — for example feature names, device models, and error terms like crash, login, or refund. Instead of a wall of raw text, you get structured tags you can search, filter, route into Slack or Teams, and include in reports. Extraction runs automatically as reviews arrive from the App Store and Google Play, so you do not maintain manual keyword lists or tag queues.
Does it work on non-English reviews?+
Yes. The pipeline understands review text across regions and languages directly, rather than requiring a separate machine-translation step before extraction. That keeps keywords and themes consistent for global apps where critical feedback often lands in local-language markets first. You can still use auto-translation in alerts for human readers while NLP works on the original text.
How is this different from sentiment analysis?+
Sentiment analysis scores emotional trajectory — whether reviews are trending more positive, neutral, or negative over time. NLP is the extraction layer underneath: it turns raw review text into the keywords, topics, and entities that filters, routing rules, theme reporting, and sentiment slices are built on. Teams usually use both together — NLP to find what people are talking about, sentiment to see how strongly the mood is moving.
Can I access extracted keywords via API?+
Yes. Extracted keywords, topics, and entities are available through AppReviewBot’s API so you can pull structured review data into your own reports, warehouses, or custom pipelines. That same structure also powers in-product routing — for example sending crash or refund keywords to dedicated Slack channels — without maintaining a separate tagging system outside AppReviewBot.
Stop reading every review. Start acting on themes.
Extract keywords, topics, and entities automatically — then route, analyze sentiment, or pull the structured data through the API.