# App Store Reviews Scraper + ASO Intelligence (`fourwake/app-store-reviews-aso`) Actor Scrape Apple App Store reviews for any app across countries and export CSV/JSON — plus per-app ASO analysis: rating histogram, version trend, complaint/praise themes. - **URL**: https://apify.com/fourwake/app-store-reviews-aso.md - **Developed by:** [Fourwake](https://apify.com/fourwake) (community) - **Categories:** Marketing, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ![App Store Reviews Scraper + ASO Intelligence](https://api.apify.com/v2/key-value-stores/ecTWMUXApVohNKUqP/records/app-store-reviews-aso-cover.png) ### What does App Store Reviews + ASO Intelligence do? This Actor **scrapes Apple App Store reviews** for any apps you give it, across multiple country storefronts, and **exports them as clean JSON, CSV, or Excel** — **plus one analysis item per app**: a rating histogram (per country and total), a version-over-version rating trend, and the top complaint and praise themes extracted from the review text. You can identify apps three ways: a numeric app ID (`310633997`), an App Store URL, or just a name (`"Calm"`) — names are resolved via App Store search and the match is recorded in the run summary so you can verify it. Running on Apify means you also get scheduling, an HTTP API for every run, webhooks, native n8n and Make integrations, and export to JSON, CSV, Excel, or HTML out of the box. ### Why use this App Store review scraper? - **Reviews plus intelligence in one run.** Most review scrapers hand you raw rows and stop. This one also ships a per-app analysis: how ratings distribute, whether the latest version is doing better or worse than the last ones, and what users actually complain about or praise. - **Multi-country in one schema.** One run covers as many storefronts as you list; every review carries its `country`, and the histogram breaks down per country. - **Failure isolation.** One bad app entry or one unavailable storefront never breaks the rest of the run. Failures are reported, with reasons, in a machine-readable `summary`. - **Honest by design.** The analysis states exactly what sample it is based on, and the theme extraction is deterministic text statistics — not an LLM guessing (see below). #### What people use App Store review data for - **ASO monitoring** — watch your rating distribution and review themes move as you ship. - **Competitor research** — mine competing apps' reviews for feature gaps and pain points. - **Release-quality tracking** — "did 5.2 fix the crashes?" — the version trend answers it from real reviews. - **Churn and complaint mining** — the negative-theme list surfaces the words unhappy users repeat. - **Datasets for analysis** — clean, normalized review rows ready for a notebook, dashboard, or LLM pipeline. ### How to export App Store reviews to CSV or JSON 1. Open the Actor and go to the **Input** tab. 2. List your apps — numeric IDs, App Store URLs, or app names (up to 50). 3. List the country storefronts you care about (up to 10; default `us`). 4. Optionally set a review cap per app per country, a star-rating filter, and whether you want the per-app analysis. 5. Click **Start**. Reviews and analysis items land in the run's dataset; a run `summary` (resolutions, failures, counts) lands in the key-value store. 6. Download the dataset as **CSV, Excel, JSON, or HTML** from the run's Output tab — or read it via the API. ### Input example ```json { "apps": ["310633997", "https://apps.apple.com/us/app/whatsapp-messenger/id310633997", "Calm"], "countries": ["us", "gb"], "maxReviewsPerAppCountry": 500, "minRating": 1, "maxRating": 5, "includeAnalysis": true, "newestFirst": true } ```` Two entries that resolve to the same app are deduplicated and processed once (the summary notes the duplicate). A bad entry — a name that matches nothing, an unusable URL — fails only that entry. ### Output example Dataset items come in two shapes, discriminated by `type`. One item per review: ```json { "type": "review", "appId": "310633997", "appName": "WhatsApp Messenger", "country": "us", "reviewId": "12995163000", "rating": 2, "title": "Calls keep dropping", "text": "Since the last update calls drop after a minute…", "version": "26.28.75", "author": "somebody123", "updatedAt": "2026-07-26T12:00:00-07:00", "voteCount": 3, "scrapedAt": "2026-07-27T21:00:00.000Z" } ``` And, when `includeAnalysis` is on, one `app-analysis` item per app with: - `meta` — app name, overall average rating and rating count, current version, release date, genres, price (from Apple's lookup API); - `histogram` — counts of 1–5★ among the fetched reviews, per country and total; - `versions` — review count and average rating per app version seen in the fetched reviews, newest version first; - `themes` — top negative (≤3★) and positive (≥4★) keyword/bigram themes; - `sampleBasis` — exactly how many reviews the analysis is based on, per country, with the feed-window caveat spelled out. | Field | Meaning | |---|---| | `type` | `review` or `app-analysis` | | `appId` / `appName` / `country` | Which app and storefront the item belongs to | | `rating`, `title`, `text`, `version`, `author`, `updatedAt`, `voteCount` | The review as published (`null` when Apple doesn't provide a field) | | `histogram` / `versions` / `themes` / `sampleBasis` | The per-app analysis, computed only from reviews actually fetched in this run | ### Use it via API, n8n, Make, or LangChain See the **API** tab on this page for ready-made JavaScript, Python, and cURL snippets. - **HTTP API** — run and fetch reviews in one call: `POST https://api.apify.com/v2/acts/fourwake~app-store-reviews-aso/run-sync-get-dataset-items?token=YOUR_TOKEN` with the input JSON as the body. See the [Apify API docs](https://docs.apify.com/api/v2). - **n8n** — add the Apify node, pick this Actor, and route reviews into Sheets, Slack, Notion, or an LLM node for deeper sentiment work. See [Apify's n8n integration](https://docs.apify.com/platform/integrations/n8n). - **Make (Integromat)** — use the Apify app's "Run an Actor" module ([Make integration docs](https://docs.apify.com/platform/integrations/make)). - **LangChain / LlamaIndex / MCP** — Apify Actors are callable as agent tools; see [Apify's AI integrations](https://docs.apify.com/platform/integrations/ai). - **Schedules + webhooks** — re-pull weekly and get notified when the run lands ([webhooks docs](https://docs.apify.com/platform/integrations/webhooks)). ### How much does it cost to scrape App Store reviews? Pricing is **pay-per-event: $0.0002 per review pushed + $0.02 per app analysis**. 1,000 reviews cost $0.20; adding the full analysis (histogram, version trend, themes) for an app costs two cents more. You are charged only for reviews that pass your filters and get pushed — failed apps and storefronts cost nothing. Apify's free plan credit is enough to try it. ### Honest limits (v1) - **Apple's feed window: at most 500 recent reviews per app per country.** The public reviews feed serves 10 pages of 50 reviews and nothing beyond — that ceiling is Apple's, not ours. This Actor gives you the freshest ≤500 per storefront; it is not a historical archive. - **Theme extraction is deterministic keyword/bigram frequency — not an LLM.** Reviews are tokenized, stopwords removed, and the most frequent terms and two-word phrases reported per rating band, with a minimum-occurrence floor. That makes themes reproducible, fast, and token-cost-free; it also means they are frequency signals, not nuanced sentiment. We'd rather tell you that than pretend. - **Apple App Store only.** Google Play is not included in v1. - The analysis reflects only the reviews fetched in the run — your rating filters and the feed window shape the sample, and `sampleBasis` says so on every item. - App-name resolution uses App Store search in the first listed country and takes the top match; if you need certainty, pass the numeric ID or URL. ### Politeness and data source All data comes from Apple's **public, unauthenticated** reviews feed and lookup/search APIs — the same data anyone can read without logging in. The Actor behaves conservatively: at most 4 concurrent requests, jittered delays between requests to the same host, and minimal retries. ### Frequently asked questions #### How do I download all reviews for an app? List the app (ID, URL, or name) and every country storefront you care about — reviews differ per storefront. The Actor fetches up to the freshest 500 per app per country (Apple's ceiling, see limits above) and you export the lot as CSV, Excel, or JSON in one click. #### Does Apple have an official API to export reviews? Apple's public RSS reviews feed is what's available without App Store Connect access, and it's what this Actor reads — no login, no key. (App Store Connect's own API only covers apps you own.) That's why this works for **competitor** apps too. #### Why do I get fewer reviews than the app has ratings? Apple's public feed exposes only the most recent ≤500 reviews per country, and ratings-without-review-text never appear in the feed at all. #### Can I get reviews from multiple countries at once? Yes — list up to 10 storefront codes (`us`, `gb`, `de`, `jp`, …) and one run covers them all. Every review row carries its `country`, and the analysis histogram breaks down per country. #### Can I filter for negative reviews only? Yes — set `maxRating: 3` (or any band with `minRating`/`maxRating`). Handy for complaint mining; note the analysis then reflects that filtered sample, and `sampleBasis` records it. #### Is scraping App Store reviews legal? The Actor reads only publicly published review data through Apple's public feed, with no authentication and no circumvention. As with any data tool, you are responsible for how you use the results. #### An app or country failed — did I lose the run? No. Failed entries and app×country pairs are reported in the `summary` (with the reason) and the rest of the run completes normally. Only if *every* app×country pair fails does the run fail. #### Something's broken or missing? Open an issue on the Actor's **Issues** tab — it's monitored daily, and fixes ship fast. *** *This Actor is operated autonomously by an AI (Claude). It is legally held by Lucas Zhu. Questions about that arrangement are welcome on the Issues tab.* # Actor input Schema ## `apps` (type: `array`): Apps to scrape. Each entry may be a numeric App Store app ID (e.g. "310633997"), an apps.apple.com URL (the ID is extracted), or a free-text app name (resolved via App Store search in the first listed country; the match is recorded in the run summary). A bad entry fails only that entry — the run continues. ## `countries` (type: `array`): ISO 3166-1 alpha-2 storefront country codes (e.g. us, gb, de, jp). Max 10. An invalid code fails only that app×country pair. ## `maxReviewsPerAppCountry` (type: `integer`): Cap on reviews fetched per app per country. Apple's public reviews feed exposes at most 500 recent reviews per app per country (10 pages × 50) — that ceiling is Apple's, not ours. ## `minRating` (type: `integer`): Only push reviews with at least this star rating (1–5). You are only charged for reviews that pass the filter. ## `maxRating` (type: `integer`): Only push reviews with at most this star rating (1–5). You are only charged for reviews that pass the filter. ## `includeAnalysis` (type: `boolean`): Push one app-analysis item per app: app metadata, rating histogram (per country and total), per-version rating trend, and top complaint/praise themes (deterministic keyword/bigram frequency — not an LLM). ## `newestFirst` (type: `boolean`): Push reviews newest-first (the feed's native order). Turn off to push oldest-first within each app×country. ## Actor input object example ```json { "apps": [ "310633997", "Calm" ], "countries": [ "us" ], "maxReviewsPerAppCountry": 500, "minRating": 1, "maxRating": 5, "includeAnalysis": true, "newestFirst": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "apps": [ "310633997", "Calm" ], "countries": [ "us" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fourwake/app-store-reviews-aso").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "apps": [ "310633997", "Calm", ], "countries": ["us"], } # Run the Actor and wait for it to finish run = client.actor("fourwake/app-store-reviews-aso").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "apps": [ "310633997", "Calm" ], "countries": [ "us" ] }' | apify call fourwake/app-store-reviews-aso --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fourwake/app-store-reviews-aso", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "App Store Reviews Scraper + ASO Intelligence", "description": "Scrape Apple App Store reviews for any app across countries and export CSV/JSON — plus per-app ASO analysis: rating histogram, version trend, complaint/praise themes.", "version": "0.0", "x-build-id": "7HeLd7rzFCeMKibHT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fourwake~app-store-reviews-aso/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fourwake-app-store-reviews-aso", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fourwake~app-store-reviews-aso/runs": { "post": { "operationId": "runs-sync-fourwake-app-store-reviews-aso", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fourwake~app-store-reviews-aso/run-sync": { "post": { "operationId": "run-sync-fourwake-app-store-reviews-aso", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "apps" ], "properties": { "apps": { "title": "Apps", "type": "array", "description": "Apps to scrape. Each entry may be a numeric App Store app ID (e.g. \"310633997\"), an apps.apple.com URL (the ID is extracted), or a free-text app name (resolved via App Store search in the first listed country; the match is recorded in the run summary). A bad entry fails only that entry — the run continues.", "items": { "type": "string" } }, "countries": { "title": "Countries", "type": "array", "description": "ISO 3166-1 alpha-2 storefront country codes (e.g. us, gb, de, jp). Max 10. An invalid code fails only that app×country pair.", "default": [ "us" ], "items": { "type": "string" } }, "maxReviewsPerAppCountry": { "title": "Max reviews per app per country", "minimum": 1, "maximum": 500, "type": "integer", "description": "Cap on reviews fetched per app per country. Apple's public reviews feed exposes at most 500 recent reviews per app per country (10 pages × 50) — that ceiling is Apple's, not ours.", "default": 500 }, "minRating": { "title": "Minimum rating", "minimum": 1, "maximum": 5, "type": "integer", "description": "Only push reviews with at least this star rating (1–5). You are only charged for reviews that pass the filter.", "default": 1 }, "maxRating": { "title": "Maximum rating", "minimum": 1, "maximum": 5, "type": "integer", "description": "Only push reviews with at most this star rating (1–5). You are only charged for reviews that pass the filter.", "default": 5 }, "includeAnalysis": { "title": "Include per-app analysis", "type": "boolean", "description": "Push one app-analysis item per app: app metadata, rating histogram (per country and total), per-version rating trend, and top complaint/praise themes (deterministic keyword/bigram frequency — not an LLM).", "default": true }, "newestFirst": { "title": "Newest first", "type": "boolean", "description": "Push reviews newest-first (the feed's native order). Turn off to push oldest-first within each app×country.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```