📝 Steam Reviews Scraper — Player Feedback & Ratings

Scrape user reviews for any game on Steam — the full review text, the thumbs-up/down rating, helpful and funny vote counts, the reviewer's playtime, purchase type, early-access flag and timestamps — plus the game's overall review summary on every row. Search by game name and the actor auto-resolves the Steam App ID for you, or pass App IDs directly. Pull the latest reviews, the most helpful ones, or thousands at a time for deep analysis.

The actor is powered by Steam's public review API, so it is fast and reliable with no browser and no login. Export to JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n.

🔎 What is the Steam Reviews Scraper?

Steam reviews are one of the richest sources of honest player feedback anywhere — millions of gamers explaining exactly what they love and hate, alongside hard signals like how many hours they played before writing. This actor turns that into a structured dataset you can actually analyze.

Give it game names (e.g. portal 2) or Steam App IDs, choose a language and a sort/filter mode, and it paginates through the reviews up to your requested limit, returning one clean row per review. Each row pairs the review content and the reviewer's stats with the game's overall summary (score description, total reviews, total positive), so you always have context. It is built for sentiment analysis, competitive research, community management and building game-review datasets.

What data does it extract?

• App ID — the Steam application the review belongs to
• Review score description — overall rating, e.g. "Overwhelmingly Positive"
• Total reviews and total positive — the game's review summary
• Recommendation ID — unique review identifier
• Author SteamID, games owned and reviews written
• Playtime — total hours, and hours at the time of the review
• Language of the review
• Voted up — thumbs up (true) or thumbs down (false)
• Votes up (helpful) and votes funny counts
• Weighted vote score — Steam's helpfulness weighting
• Comment count on the review
• Steam purchase and received for free flags
• Early access — whether it was written during early access
• Created and updated dates
• Review — the full review text

⬇️ Input

Field	Type	Description
searchTerms	array	Game names to scrape (auto-resolves the App ID), e.g. portal 2.
appIds	array	Optional: scrape specific games by Steam App ID, e.g. 620.
language	string	Review language, e.g. all, english, german. Default all.
filter	string	Which reviews to fetch: recent, updated or all (helpful first). Default recent.
maxReviews	integer	Max reviews per game. Default 50, up to 2000.

Example input

{
  "searchTerms": ["portal 2"],
  "language": "english",
  "filter": "all",
  "maxReviews": 200
}

⬆️ Output

Each review is one clean row (view as a table, or export JSON / CSV / Excel):

{
  "app_id": "620",
  "review_score_desc": "Overwhelmingly Positive",
  "total_reviews": 286140,
  "total_positive": 282900,
  "recommendation_id": "123456789",
  "author_steamid": "76561198000000000",
  "author_num_games": 214,
  "author_num_reviews": 37,
  "playtime_forever_hours": 42.3,
  "playtime_at_review_hours": 18.7,
  "language": "english",
  "voted_up": true,
  "votes_up": 12,
  "votes_funny": 1,
  "weighted_vote_score": "0.529411792755126953",
  "comment_count": 0,
  "steam_purchase": true,
  "received_for_free": false,
  "early_access": false,
  "created_date": "2024-11-02",
  "updated_date": "2024-11-02",
  "review": "Still one of the best puzzle games ever made. The co-op campaign alone is worth it."
}

💡 Use cases

• 💬 Sentiment analysis — mine thousands of reviews to quantify what players love or complain about and track sentiment over time.
• 🎮 Player & competitor research — study feedback on your game or a rival's to guide roadmap, balance and marketing decisions.
• 📊 Review datasets & NLP — build labelled corpora (voted-up/down, playtime, language) for machine-learning and analytics.
• 🤖 Community & QA pipelines — feed structured reviews into dashboards, Make/Zapier/n8n flows or LLM summarizers.

❓ FAQ

How do I scrape Steam reviews? Enter game names in searchTerms (or App IDs in appIds), set maxReviews, and Run. You get one structured row per review plus the game's overall summary.

Do I need an API key or login? No. It uses Steam's public review API — just provide game names or App IDs.

Can I search by game name? Yes — the actor auto-resolves the Steam App ID from the name, so you don't have to look it up.

Can I filter by language? Yes — set language (e.g. english, german) or use all for every language.

How do I get the most helpful reviews vs the newest? Use filter: recent for the latest, updated for recently edited, or all for Steam's helpful-first ordering.

How many reviews can I get? Up to maxReviews per game (up to 2000) — the actor paginates automatically through Steam's cursor.

Does it include the reviewer's playtime? Yes — both total playtime and playtime at the moment the review was written, in hours.

Can I run it on a schedule or via API? Yes — schedule recurring runs on Apify, call it via the API/SDK, or connect it to Make, Zapier or n8n.

How does pricing work? Pay per review returned — no subscription, no fixed monthly fee.

Is it legal? It uses Steam's public review API and returns publicly visible review content. Use the data responsibly and in line with Valve's terms.

🔗 You might also like

• Steam Games Scraper — game prices, tags & metadata.
• App Store Reviews Scraper — Apple App Store ratings & reviews.
• Google Play Reviews Scraper — Android app reviews.
• Google Maps Reviews Scraper — local business reviews.

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