Export public Steam game reviews with review text, sentiment, votes, playtime, language, purchase flags, timestamps, and author metadata.
Use this Actor when you need repeatable Steam review data for game research, player sentiment analysis, competitor monitoring, product feedback, localization analysis, or dashboards. Results can be downloaded as CSV, JSON, Excel, XML, RSS, or used through the Apify Dataset API.
At a glance
- Steam app input: scrape reviews by Steam app ID or public Steam store URL.
- Review text and sentiment: save review text, recommendation status, language, votes, and comment counts.
- Author and playtime data: include public author IDs, profile URLs, review counts, playtime, and last-played fields when available.
- Filtering controls: choose review order, language, review type, purchase type, and cursor.
- Continuation ready: use
nextCursorto continue review collection from previous runs.
What can it do?
Steam Reviews Scraper turns public Steam review feeds into structured review rows.
- Export Steam review data with text, recommendation status, votes, language, purchase flags, timestamps, and app IDs.
- Collect player context such as public author profile URL, Steam ID, review counts, games owned, and playtime when returned by Steam.
- Filter review feeds by language, sentiment, purchase type, and review ordering.
- Build game-review monitors by scheduling recent-review runs for target apps.
- Use it as a Steam reviews API workflow for CSV, JSON, Excel, or direct Dataset API exports.
Common workflows
- Sentiment analysis: classify positive and negative Steam reviews by language, game, or time period.
- Game launch monitoring: track recent reviews after updates, releases, discounts, or patches.
- Competitor research: compare player complaints and praise across similar games.
- Localization analysis: collect reviews by language to understand regional feedback.
- Product feedback mining: extract recurring issues, feature requests, and player language.
- Review dashboards: feed review rows into BI tools, warehouses, or spreadsheets.
What data can you collect?
Each dataset row represents one public Steam review.
| Field | Description |
|---|---|
appId |
Steam app ID |
appUrl |
Steam app URL |
recommendationId |
Unique Steam review/recommendation ID |
review |
Review text |
language |
Review language |
votedUp |
Whether the player recommends the game |
votesUp |
Helpful vote count |
votesFunny |
Funny vote count |
weightedVoteScore |
Steam weighted vote score |
commentCount |
Number of review comments |
steamPurchase |
Whether the game was purchased on Steam |
receivedForFree |
Whether the reviewer marked it as received for free |
refunded |
Whether the review is associated with a refund flag |
writtenDuringEarlyAccess |
Early-access review flag |
primarilySteamDeck |
Steam Deck related flag when available |
timestampCreated |
Review creation date/time |
timestampUpdated |
Review update date/time |
authorSteamId |
Public author Steam ID |
authorPersonaName |
Public Steam persona name when returned |
authorProfileUrl |
Author profile URL |
playtimeForeverMinutes |
Total recorded playtime in minutes |
playtimeAtReviewMinutes |
Playtime at the time of review |
lastPlayed |
Last played timestamp |
querySummaryTotalReviews |
Total reviews reported by Steam for the query |
nextCursor |
Cursor that can be used for continuation |
scrapedAt |
Timestamp when the row was saved |
Example input
{
"appIdsOrUrls": ["730"],
"maxReviewsPerApp": 100,
"filter": "recent",
"language": "english",
"reviewType": "all",
"purchaseType": "all",
"cursor": "*",
"includeAuthor": true,
"proxyConfiguration": { "useApifyProxy": false }
}
Example output
{
"appId": "730",
"appUrl": "https://store.steampowered.com/app/730/",
"recommendationId": "123456789",
"review": "Great competitive game with a strong community.",
"language": "english",
"votedUp": true,
"votesUp": 12,
"votesFunny": 0,
"weightedVoteScore": 0.72,
"steamPurchase": true,
"playtimeAtReviewMinutes": 2400,
"authorProfileUrl": "https://steamcommunity.com/profiles/7656119...",
"timestampCreated": "2026-07-03T10:30:00.000Z",
"nextCursor": "AoIIPw..."
}
Tips for best results
- Use app IDs for clean inputs: Steam app IDs such as
730are more compact than full URLs. - Use
filter="recent"for monitoring: recent reviews are best for alerts and launch tracking. - Set language deliberately:
englishis cleaner for English NLP;allis broader for global research. - Keep author metadata on for analysis: public playtime and review counts can help segment feedback.
- Store
nextCursor: use it when you need to continue collection later.
Limits and caveats
- The Actor extracts publicly visible Steam review data only.
- It does not access private profiles, private libraries, account-only data, or non-public review information.
- Some author fields may be missing when Steam does not return them.
- Steam review counts, votes, and visibility can change after scraping.
API usage
curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~steam-reviews-scraper/runs?token=YOUR_APIFY_TOKEN' \
-H 'Content-Type: application/json' \
-d '{"appIdsOrUrls":["730"],"maxReviewsPerApp":50,"filter":"recent","language":"english"}'
MCP and AI agents
This Actor can be used through the official Apify MCP server at https://mcp.apify.com.
For a focused single-Actor tool setup, use:
https://mcp.apify.com?tools=fetch_cat/steam-reviews-scraper
Use the same JSON keys shown in the input configuration table, such as appIdsOrUrls, maxReviewsPerApp, filter, language, reviewType, purchaseType, and includeAuthor.
Support
If a run fails, returns no data, or a field looks wrong, open an issue from the Actor page.
Please include the Apify run ID or run URL, input JSON, one example public URL, query, or input item, what you expected, and what the dataset returned. Small reproducible inputs make parsing or site-layout issues much faster to fix.
