Steam Reviews Scraper — Game Reviews & Ratings

Extract user reviews and ratings for any game on Steam. Paste one or more store links (or App IDs), choose your filters, and get one clean row per review — the review text, whether it's recommended, helpful and funny votes, the author's playtime, purchase type, language, early-access flag and dates. Export to JSON, CSV or Excel, or pull straight from the API.

Perfect for game market research, competitor analysis, community sentiment tracking, spotting bugs and feature requests, review monitoring around launches and updates, and building review datasets for dashboards or LLMs.

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What you get

One flat, ready-to-use record per review:

Field	Description
appId / appName	Steam App ID and game title
recommendationId	Unique review ID
steamId	Reviewer's Steam ID
language	Review language
votedUp	true = Recommended, false = Not Recommended
review	Full review text
votesUp / votesFunny	"Helpful" and "Funny" vote counts
weightedScore	Steam's helpfulness score (0–1)
commentCount	Comments on the review
playtimeForeverMin / playtimeAtReviewMin	Author playtime, total and at review time (minutes)
authorGamesOwned / authorReviews	Reviewer's library size and review count
steamPurchase / receivedForFree / earlyAccess / steamDeck	Review context flags
createdAt / updatedAt	Posted and last-edited dates (ISO 8601)
reviewUrl	Link to the review
collectedAt	When the row was scraped

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How to use

1. Add games

Paste Steam store links or numeric App IDs:

{
  "appUrls": [
    "https://store.steampowered.com/app/570/Dota_2/",
    "730"
  ],
  "filterBy": "recent",
  "maxItems": 500
}

2. Filter & order

• Order reviews by — Most recent, Recently updated, or All (helpfulness).
• Recommendation — all reviews, positive (recommended) only, or negative (not recommended) only.
• Purchase type — all, players who bought on Steam, or key activations only.
• Language — all, or a Steam language code such as english, spanish, german, french, schinese, russian, japanese, koreana, brazilian.
• Max reviews — cap the total (0 = no limit).

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Example output

{
  "appId": "570",
  "appName": "Dota 2",
  "recommendationId": "187654321",
  "language": "english",
  "votedUp": true,
  "review": "Best MOBA out there once it clicks.",
  "votesUp": 42,
  "votesFunny": 3,
  "weightedScore": 0.61,
  "playtimeForeverMin": 69765,
  "playtimeAtReviewMin": 41230,
  "steamPurchase": true,
  "earlyAccess": false,
  "createdAt": "2026-09-19T14:10:00.000Z",
  "reviewUrl": "https://steamcommunity.com/profiles/7656119.../recommended/570/",
  "collectedAt": "2026-09-20T03:15:00.000Z"
}

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Common use cases

• Game market research — measure sentiment, review volume and recommendation ratio across titles.
• Competitor analysis — pull rivals' reviews to find complaints, praised features and churn drivers.
• Launch & patch monitoring — track how each update lands by filtering recent and updated reviews.
• QA & feedback — surface bug reports and feature requests straight from players.
• Player insight — segment by playtime, purchase type and language.
• Datasets — build clean, labelled review corpora for analysis or model training.

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Frequently asked questions

Do I need any login, account or key? No. Just paste game links and run.

How many reviews can I get? Set Max reviews to whatever you need (0 = no limit) — the scraper pages through a game's entire review history.

Can I get only positive or only negative reviews? Yes — use the Recommendation filter.

Can I scrape reviews in one language only? Yes — set the Language field to a Steam language code.

What format is the data? A flat table you can export as JSON, CSV or Excel, or fetch from the Apify API.

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Related scrapers

Building review intelligence across platforms? Pair this with our App Store reviews scraper and Google Play reviews scraper for mobile, all with the same clean, one-row-per-review output.