# Steam Reviews Scraper - Sentiment & Review-Bomb Detection (`justfeel/steam-reviews-scraper`) Actor Extract Steam game reviews via Valve's own public API. Playtime at review, hardware specs, helpfulness votes, developer replies. Built-in review-bomb spike detection and pros/cons keyword extraction. No login, no browser. - **URL**: https://apify.com/justfeel/steam-reviews-scraper.md - **Developed by:** [Yusuf](https://apify.com/justfeel) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 reviews 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/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 ## Steam Reviews Scraper — Sentiment, Playtime & Review-Bomb Detection Extract **Steam game reviews** for any app — as clean, structured JSON, built directly on Valve's own public review API. No login, no browser, no proxies, so it never breaks the way headless-browser scrapers do. ### What you get per review ```json { "review_id": "180325487", "app_id": "730", "app_name": "Counter-Strike 2", "recommended": true, "text": "Been playing since 1.6, still the best.", "language": "english", "created_at": "2026-06-01T14:22:03+00:00", "updated_at": "2026-06-01T14:22:03+00:00", "votes_helpful": 12, "votes_funny": 0, "weighted_vote_score": 0.63, "comment_count": 1, "steam_purchase": true, "received_for_free": false, "written_during_early_access": false, "playtime_at_review_minutes": 4820, "playtime_forever_minutes": 9143, "playtime_last_two_weeks_minutes": 620, "author_num_reviews": 8, "author_num_games_owned": 143, "author_steam_id": "76561198012345678", "primarily_steam_deck": false, "hardware_os": "Windows 11", "hardware_cpu": "AMD Ryzen 5 5500", "hardware_gpu": "NVIDIA GeForce GTX 1650" } ```` ### Built-in analytics (free, not billed) When `includeSummary` is on (default), one extra summary record per app is pushed alongside the reviews: ```json { "type": "summary", "app_id": "730", "app_name": "Counter-Strike 2", "steam_review_score": 8, "steam_review_score_desc": "Very Positive", "steam_total_positive": 1264535, "steam_total_negative": 214396, "steam_total_reviews": 1478931, "sample_size": 200, "review_bomb_alert": { "detected": false, "flagged_days": [] }, "top_pros_keywords": [{ "word": "gameplay", "count": 14 }], "top_cons_keywords": [{ "word": "cheaters", "count": 9 }] } ``` - **Review-bomb spike detection** — flags days where review volume spikes far above the sample's median, a signal used to catch coordinated review-bombing or a patch/controversy blowing up - **Pros/cons keyword extraction** — top recurring words split by recommended vs. not-recommended reviews, a quick read on *why* players like or dislike the game - **Steam's own review score** attached to every run, so you can track sentiment drift over time on a schedule ### Why this scraper - ✅ **Never breaks** — built on Valve's official public review API, the same one the Steam store website itself uses - ✅ **Richer than a UI scrape** — hardware specs and playtime *at the moment of review* aren't visible on the Steam store page itself, only through the API - ✅ **Multi-game in one run** — track your own game plus competitors side by side - ✅ **Fast & cheap** — no browser, no proxies, no login - ✅ **Review-bomb and sentiment signals out of the box** ### Use cases - **Indie devs & publishers**: monitor sentiment after every patch, catch a review-bomb or cheating wave early - **Game marketing / ASO agencies**: mine competitor reviews for feature gaps and messaging - **Market research & investment analysis**: track public sentiment on gaming studios' flagship titles over time - **AI & NLP pipelines**: feed structured review text into sentiment/LLM analysis ### Input example ```json { "appIds": ["730", "1245620"], "reviewType": "all", "filter": "recent", "language": "all", "purchaseType": "all", "maxReviewsPerApp": 200, "includeSummary": true } ``` ### Filters - `reviewType`: `all` / `positive` / `negative` - `filter`: `recent` (newest first), `all` (Steam's helpfulness-ranked all-time top reviews), `updated` - `language`: any Steam language code (`english`, `schinese`, `german`, ...) or `all` - `purchaseType`: `all` / `steam` / `non_steam_purchase` ### FAQ **Is this legal?** The actor reads Steam's publicly available review API — the same data anyone sees on the store page. No login, no private data. **Where do I find an app ID?** In the game's store URL: `store.steampowered.com/app/730/CounterStrike2` → `730`. **How many reviews can I get?** As many as Steam has for that filter — pagination follows Steam's cursor until exhausted or your `maxReviewsPerApp` cap is hit. # Actor input Schema ## `appIds` (type: `array`): Numeric Steam app IDs. Find it in the game's store URL, e.g. store.steampowered.com/app/730/CounterStrike2 -> 730. ## `reviewType` (type: `string`): Filter by whether the review recommends the game. ## `filter` (type: `string`): "Recent" returns the newest reviews first. "All" returns Steam's helpfulness-ranked reviews (best for a game's all-time top reviews). "Updated" sorts by last edit. ## `language` (type: `string`): Steam language code (english, schinese, german, ...) or "all" for every language. ## `purchaseType` (type: `string`): Restrict to reviews from Steam purchasers, non-Steam (key) activations, or both. ## `maxReviewsPerApp` (type: `integer`): Stop fetching once this many reviews have been collected for a given app. ## `includeSummary` (type: `boolean`): Push one extra (unbilled) summary record per app: Steam's overall review score, review-bomb spike detection on the collected sample, and top pros/cons keywords. ## Actor input object example ```json { "appIds": [ "730" ], "reviewType": "all", "filter": "recent", "language": "all", "purchaseType": "all", "maxReviewsPerApp": 200, "includeSummary": 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 = { "appIds": [ "730" ] }; // Run the Actor and wait for it to finish const run = await client.actor("justfeel/steam-reviews-scraper").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": ["730"] } # Run the Actor and wait for it to finish run = client.actor("justfeel/steam-reviews-scraper").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": [ "730" ] }' | apify call justfeel/steam-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=justfeel/steam-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Steam Reviews Scraper - Sentiment & Review-Bomb Detection", "description": "Extract Steam game reviews via Valve's own public API. Playtime at review, hardware specs, helpfulness votes, developer replies. Built-in review-bomb spike detection and pros/cons keyword extraction. No login, no browser.", "version": "0.1", "x-build-id": "Godmo0NeApY5gODHt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/justfeel~steam-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-justfeel-steam-reviews-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/justfeel~steam-reviews-scraper/runs": { "post": { "operationId": "runs-sync-justfeel-steam-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/justfeel~steam-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-justfeel-steam-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Steam App IDs", "type": "array", "description": "Numeric Steam app IDs. Find it in the game's store URL, e.g. store.steampowered.com/app/730/CounterStrike2 -> 730.", "items": { "type": "string" } }, "reviewType": { "title": "Review type", "enum": [ "all", "positive", "negative" ], "type": "string", "description": "Filter by whether the review recommends the game.", "default": "all" }, "filter": { "title": "Sort order", "enum": [ "recent", "all", "updated" ], "type": "string", "description": "\"Recent\" returns the newest reviews first. \"All\" returns Steam's helpfulness-ranked reviews (best for a game's all-time top reviews). \"Updated\" sorts by last edit.", "default": "recent" }, "language": { "title": "Language", "type": "string", "description": "Steam language code (english, schinese, german, ...) or \"all\" for every language.", "default": "all" }, "purchaseType": { "title": "Purchase type", "enum": [ "all", "steam", "non_steam_purchase" ], "type": "string", "description": "Restrict to reviews from Steam purchasers, non-Steam (key) activations, or both.", "default": "all" }, "maxReviewsPerApp": { "title": "Max reviews per app", "minimum": 1, "maximum": 20000, "type": "integer", "description": "Stop fetching once this many reviews have been collected for a given app.", "default": 200 }, "includeSummary": { "title": "Include analytics summary", "type": "boolean", "description": "Push one extra (unbilled) summary record per app: Steam's overall review score, review-bomb spike detection on the collected sample, and top pros/cons keywords.", "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 } } } } } } } } } } ```