# App Store Review Intelligence — Sentiment & Competitor Gaps (`datasignalslab/app-store-review-intelligence`) Actor Turn App Store reviews into aspect-based sentiment, top complaints, emerging issues, and competitor feature-gap comparison across countries. A cheap, self-serve alternative to Appbot & AppFollow. - **URL**: https://apify.com/datasignalslab/app-store-review-intelligence.md - **Developed by:** [DataSignals Lab](https://apify.com/datasignalslab) (community) - **Categories:** AI, Agents, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $200.00 / 1,000 app analyzeds 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 Review Intelligence - Sentiment & Competitor Gaps **Turn App Store reviews into decisions.** Give one or more iOS App Store app IDs (yours *and your competitors'*) and get **aspect-based sentiment**, **top complaints & praise**, **emerging issues**, and a **side-by-side competitor feature-gap comparison** - across countries and languages. Not a raw review dump: the analysis layer that product managers, ASO and growth teams actually pay for. > Most tools only analyze *your* app and cost $49-$599/month (Appbot, AppFollow, Appfigures, Sensor Tower). This is a self-serve, pay-per-use alternative that also analyzes **competitors**. **Real example:** *Spotify (4.0 stars) vs YouTube Music (2.1 stars) - per aspect: price/billing **3.71 vs 1.53**, ads **3.98 vs 2.02**, UI/UX **4.29 vs 1.96**.* Instantly see exactly where each app wins or loses. ### What makes it different - **Aspect-based sentiment** - not a vague score, but per-topic: stability/crashes, performance, UI/UX, price/billing, login, ads, features, support, updates, content. - **Competitor comparison** - analyze multiple apps at once and see exactly where each is weak or strong, aspect by aspect (the validated market gap most tools miss). - **Emerging issues** - flags aspects whose sentiment is dropping in *recent* reviews (e.g. "crashes -1.5 stars recent") so you catch problems early. - **Multi-country / multi-language** - pull reviews from multiple App Store countries. Sentiment is derived from star ratings, so it works **in any language**, no translation needed. - **Real review quotes** - every top complaint comes with a representative customer quote, so you see exactly what users say, plus the direct App Store link. ### Who it's for App developers, product managers, ASO/growth teams, and competitive-intelligence analysts who want fast, affordable review insight - including on apps they don't own. ### Input | Field | Type | Description | |---|---|---| | `appIds` | array | Numeric iOS App Store IDs (e.g. `324684580` = Spotify). Add competitors for comparison. | | `countries` | array | App Store country codes (e.g. `us`, `gb`, `nl`, `de`). Multi-country = multi-language. | | `pagesPerCountry` | integer | Review pages per country (50/page). | | `minMentions` | integer | Minimum mentions for an aspect to be reported. | ### Output - One **app_analysis** item per app: `avg_rating`, per-aspect `avg stars` / `mentions` / `%negative`, `top_complaints`, `top_praise`, `emerging_issues`. - One **comparison** item (when ≥2 apps): per-aspect average rating for each app side by side. ### How sentiment works (transparent, language-agnostic) Sentiment per aspect = the average **star rating** of reviews mentioning that aspect. Star ratings are universal, so this works across languages without a translation model. Text is used only to detect *which* aspects a review talks about. ### Pricing **Pay-per-event: per app analyzed.** A 3-app competitor comparison = 3 charges. You pay only for the apps you analyze. ### Data source Official Apple App Store customer-reviews RSS feed - public, free, no login. Rate-limited and respectful. --- *Keywords: app store reviews, app review analysis, review sentiment analysis, aspect-based sentiment, ASO, competitor app analysis, app store optimization, product feedback, iOS app reviews, customer sentiment, app intelligence.* # Actor input Schema ## `appIds` (type: `array`): One or more numeric iOS App Store app IDs (e.g. 324684580 for Spotify). Add competitors to get a side-by-side feature-gap comparison. ## `countries` (type: `array`): Two-letter App Store country codes to pull reviews from (multi-country = multi-language). E.g. us, gb, nl, de. ## `pagesPerCountry` (type: `integer`): Review pages to fetch per country (50 reviews/page, ~max 10). ## `minMentions` (type: `integer`): An aspect must be mentioned at least this many times to be reported. ## Actor input object example ```json { "appIds": [ "324684580", "1017492454" ], "countries": [ "us" ], "pagesPerCountry": 3, "minMentions": 3 } ```` # 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 = { "appIds": [ "324684580", "1017492454" ], "countries": [ "us" ] }; // Run the Actor and wait for it to finish const run = await client.actor("datasignalslab/app-store-review-intelligence").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 = { "appIds": [ "324684580", "1017492454", ], "countries": ["us"], } # Run the Actor and wait for it to finish run = client.actor("datasignalslab/app-store-review-intelligence").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 '{ "appIds": [ "324684580", "1017492454" ], "countries": [ "us" ] }' | apify call datasignalslab/app-store-review-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datasignalslab/app-store-review-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "App Store Review Intelligence — Sentiment & Competitor Gaps", "description": "Turn App Store reviews into aspect-based sentiment, top complaints, emerging issues, and competitor feature-gap comparison across countries. A cheap, self-serve alternative to Appbot & AppFollow.", "version": "0.1", "x-build-id": "ORoPogx3vQqd0MEFA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datasignalslab~app-store-review-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datasignalslab-app-store-review-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datasignalslab~app-store-review-intelligence/runs": { "post": { "operationId": "runs-sync-datasignalslab-app-store-review-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datasignalslab~app-store-review-intelligence/run-sync": { "post": { "operationId": "run-sync-datasignalslab-app-store-review-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "appIds" ], "properties": { "appIds": { "title": "App Store app IDs", "type": "array", "description": "One or more numeric iOS App Store app IDs (e.g. 324684580 for Spotify). Add competitors to get a side-by-side feature-gap comparison.", "items": { "type": "string" } }, "countries": { "title": "Country codes", "type": "array", "description": "Two-letter App Store country codes to pull reviews from (multi-country = multi-language). E.g. us, gb, nl, de.", "default": [ "us" ], "items": { "type": "string" } }, "pagesPerCountry": { "title": "Pages per country", "minimum": 1, "maximum": 10, "type": "integer", "description": "Review pages to fetch per country (50 reviews/page, ~max 10).", "default": 3 }, "minMentions": { "title": "Minimum mentions per aspect", "minimum": 1, "maximum": 20, "type": "integer", "description": "An aspect must be mentioned at least this many times to be reported.", "default": 3 } } }, "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 } } } } } } } } } } ```