App Store Review Scraper for Sentiment Analysis

What this pipeline does

App reviews are messy, but they are often the clearest public signal of what users love, what broke, and which competitor features are getting noticed. The trick is to keep the raw review, rating, app, store, date, and source together before adding sentiment.

The App Store Review Sentiment Scraper collects Apple App Store and Google Play reviews, ratings, ASO fields, and optional sentiment tags so product teams can compare feedback across their own apps and competitors.

Best first run

Start with one app on one store.

{
  "appUrls": [
    "https://apps.apple.com/us/app/example/id123456789"
  ],
  "maxReviews": 100,
  "enableSentiment": true
}

Review the sentiment labels against the raw text. Automated sentiment is useful for grouping, but product decisions still need the exact review that caused the label.

Feedback row shape

{
  "app_id": "com.example.app",
  "store": "apple_app_store",
  "rating": 2,
  "review": "The latest update made login slower.",
  "sentiment": "negative",
  "author": "example_user",
  "published_at": "2026-05-30T08:00:00Z"
}

Keep the rating and sentiment separate. A three-star review can include a strong product request, and a five-star review can still mention a missing feature.

Product dashboard views

Useful views are simple:

• Rating trend by week
• Negative review topics
• Competitor feature requests
• Reviews after a release date
• Reviews mentioning pricing, login, support, or performance

Those views help product and support teams decide whether a review cluster is a bug, a UX issue, or a positioning problem.

Deployment notes

Run competitor apps weekly and your own app after every major release. Store raw review text, but build dashboards from grouped topics so the team does not have to read hundreds of near-duplicate complaints.