App Store Reviews Scraper — Apple iTunes RSS | $2/1K | No API Key

Product managers, NLP researchers, and ASO managers use this actor to collect review datasets for sentiment analysis, track rating trends after app updates, and monitor user feedback for competitor apps.

$2.00 per 1,000 reviews. 500 reviews per app per country (Apple RSS cap). No proxy, no API key.

Worked example:

• 1 app, 200 reviews (default) → $0.40
• 1 app, full 500 reviews → $1.00
• 10 apps × 200 reviews each = 2,000 reviews → $4.00

NLP and sentiment analysis: Feed review text directly into LLM pipelines. The body field is clean, plain-text — no HTML stripping needed. The rating (1–5★) provides ground-truth labels.

Scrape Apple App Store customer reviews for any iOS/macOS app. Uses Apple's open iTunes RSS API — no API key required. Pay per review scraped.

Need app metadata too? See Apple App Store Scraper — combines app details and reviews in one run. Note: this actor is reviews-ONLY, optimized for bulk sentiment/NLP use cases.

Sample output

{
  "review_id": "11982745012",
  "app_id": "389801252",
  "country": "us",
  "rating": 5,
  "title": "Absolutely love it",
  "body": "Best app I've used in years. Runs smoothly even on older phones.",
  "author": "TechFan99",
  "app_version": "401.0",
  "date": "2026-05-29T08:14:00Z",
  "parse_confidence": 1.0
}

---

What you get

Field	Description
review_id	iTunes review ID
app_id	Numeric App Store app ID
country	Storefront country (e.g. us)
rating	1–5 star rating
title	Review title
body	Full review text
author	Reviewer username
author_url	Reviewer profile URL
app_version	App version reviewed
date	Review date (ISO 8601)
vote_count	Total helpfulness votes
vote_sum	Net helpful votes
parse_confidence	Data quality score 0.0–1.0
warnings	List of parse warnings

Usage

1. Find your app ID: look at the App Store URL — id389801252 → ID is 389801252
2. Enter the app ID or paste the full App Store URL
3. Choose country code (default: us) and max reviews
4. Run — results appear in the dataset

Input example

{
  "appId": "389801252",
  "country": "us",
  "maxReviews": 200
}

Or paste the full URL:

{
  "appId": "https://apps.apple.com/us/app/facebook/id389801252"
}

Limits

• iTunes RSS provides up to 10 pages × 50 reviews = 500 reviews per app per country
• Reviews are sorted by most recent
• Supports all App Store country storefronts

FAQ

Do I need an API key or proxy? No. Apple's iTunes RSS feed is fully public — zero setup, zero auth.

What formats can I export? JSON, CSV, Excel, or JSONL — from the Apify dataset UI or REST API.

Can I scrape reviews for multiple apps? This actor is reviews-only for one app per run. For bulk multi-app scraping, use Apple App Store Scraper which handles multiple apps + reviews in a single run.

What if the actor returns fewer reviews than expected? Apple's RSS hard cap is 500 reviews (10 pages × 50). Some apps may have fewer. If parse_confidence < 0.5, check the warnings field — Apple may have changed the feed format.

Why this scraper?

• No API key required — Apple's RSS is completely open
• parse_confidence score flags incomplete records automatically
• Lightweight and reliable — direct JSON API, no HTML parsing

vs. competitors

Feature	This actor	bebity/apple-app-store-scraper	epctex/app-store-scraper
parse_confidence drift detection	✅	❌	❌
vote_count + vote_sum (helpfulness)	✅	partial	❌
app_version per review	✅	partial	partial
Price	$2/1K	$3/1K	$5/1K

(Verify competitor prices at publish time — adjust table accordingly.)

parse_confidence — every record carries a 0.0–1.0 data quality score. Score < 0.5 → check the warnings field.

Monitoring tip

Schedule this actor daily or weekly to track review sentiment over time. Pair with a webhook to Slack or email to be notified when rating patterns shift (e.g. average rating drops after an update). Use Apify Scheduler → webhook to automate the pipeline — no manual runs needed.

Use with AI agents (MCP)

All actors in this suite are available via the Apify MCP server. Connect to Claude, GPT-4o, or n8n to let AI agents pull live App Store data on demand — market research, competitor monitoring, and ASO tracking automated end-to-end.

MCP config: https://mcp.apify.com/?tools=bovi/appstore-reviews

Also in this suite

• Apple App Store Scraper — app metadata + reviews combined
• Google Play Store Scraper — Android ecosystem
• ASO Keyword Rank Tracker — ranking intelligence
• App Store Charts Scraper — market surveillance

Integrations

Built for product managers and NLP teams collecting rated review datasets for sentiment analysis and feedback monitoring — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• ASO Keyword Tracker
• Google Play Scraper
• Google Search (SERP) Scraper
• Similarweb Traffic Scraper
• App Store Scraper
• App Update Tracker

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.