# Steam Reviews Scraper (`datalayer/steam-review-intelligence`) Actor

Steam reviews with the hours the reviewer had actually played. Some scrapers return that field; none compute with it. This weights sentiment by playtime and bands reviewers from drive-by to veteran, so a 12-minute opinion cannot outvote a 500-hour one.

- **URL**: https://apify.com/datalayer/steam-review-intelligence.md
- **Developed by:** [Datalayer](https://apify.com/datalayer) (community)
- **Categories:** Social media, E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.70 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 — Playtime-Weighted Sentiment

Steam tells you 82% of reviews are positive. It does not tell you that most of
those came from people who played for 40 minutes, while the players with 300
hours have turned on the game.

Steam's API carries **`playtime_at_review`** — how many hours the reviewer had
actually played when they wrote it. Some scrapers hand it back as a column.
None of them do anything with it. This one does the maths.

### What you get that a normal review scraper cannot give you

**Weighted sentiment vs raw sentiment.** Every review is weighted
logarithmically by hours played, so a 12-minute opinion cannot outvote a
500-hour one. The gap between the two numbers is the headline:

| App | Raw % | Weighted % | Shift |
|---|---|---|---|
| Healthy game | 84.0 | 85.1 | +1.1 |
| Game in trouble | 78.0 | 51.4 | **−26.6** |

A large negative shift means the people who know the game best have stopped
recommending it, and the store page score is flattering it.

**Sentiment per credibility band.** Reviews are banded by hours played at the
time of writing:

| Band | Hours | What it means |
|---|---|---|
| `drive_by` | under 2 | Inside Steam's refund window. Mostly refunds in progress. |
| `casual` | 2–10 | Formed a first impression. |
| `engaged` | 10–50 | Played it properly. |
| `invested` | 50–200 | Knows the game. |
| `veteran` | 200+ | Knows it better than most of the dev team. |

`veteranPositivePercent` dropping below `driveByPositivePercent` is the single
strongest churn signal Steam exposes, and it is invisible in a flat review list.

**The distortion flags.** `refunded`, `receivedForFree`,
`writtenDuringEarlyAccess`, `onSteamDeck`. Free copies skew positive. Early
access reviews often describe a build that no longer exists. Deck-specific
complaints hide inside general sentiment. All four are on every row, plus
per-app shares in the analysis.

### Who uses this

- **Game publishers and studios** — watch veteran sentiment after a patch, not
  the review-bomb noise.
- **Competitive analysis** — pull five rival titles and compare weighted
  sentiment side by side.
- **Investors and M\&A** — a raw score is easy to inflate; a weighted score with
  refund share attached is not.
- **Storefronts and curators** — filter out reviews below 2 hours before you
  quote a number.

### Input

```json
{
  "appIds": ["730", "https://store.steampowered.com/app/570/Dota_2/"],
  "maxReviewsPerApp": 1000,
  "filter": "recent",
  "language": "english",
  "minPlaytimeHours": 2,
  "excludeFreeCopies": true
}
```

Paste bare App IDs or full store URLs — both work. The App ID is the number in
the store URL: `730` in `store.steampowered.com/app/730/`.

| Field | Default | Notes |
|---|---|---|
| `appIds` | `["730"]` | Required. IDs or store URLs. |
| `maxReviewsPerApp` | `500` | 500 is enough to see a trend, 2000 for a definitive read. |
| `filter` | `recent` | `recent` | `updated` | `all` (Steam's helpfulness ranking). |
| `language` | `all` | Steam language code, e.g. `english`, `schinese`. |
| `reviewType` | `all` | `positive` or `negative` only. |
| `purchaseType` | `all` | Steam purchases vs keys and gifts. |
| `minPlaytimeHours` | — | Set `2` to drop everything inside the refund window. |
| `excludeFreeCopies` | `false` | Drop reviews from people who did not pay. |
| `includeAnalysis` | `true` | The per-app summary row. |
| `includeReviews` | `true` | Turn off for analysis only. |

### Output

Three row types in one dataset, with tab views for each:

**`analysis`** — one row per app.

```json
{
  "type": "analysis",
  "appName": "Counter-Strike 2",
  "reviewsAnalysed": 500,
  "rawPositivePercent": 71.2,
  "weightedPositivePercent": 63.8,
  "weightingEffect": -7.4,
  "veteranPositivePercent": 58.1,
  "driveByPositivePercent": 79.4,
  "medianPlaytimeAtReviewHours": 41.6,
  "refundedShare": 1.2,
  "freeCopyShare": 0.4,
  "lifetimeTotalReviews": 1490660,
  "lifetimeScoreDesc": "Very Positive"
}
```

**`review`** — one row per review.

```json
{
  "type": "review",
  "votedUp": false,
  "playtimeAtReviewHours": 512.3,
  "playtimeTotalHours": 698.1,
  "credibility": "veteran",
  "weight": 0.98,
  "refunded": false,
  "receivedForFree": false,
  "writtenDuringEarlyAccess": false,
  "onSteamDeck": true,
  "helpfulVotes": 120,
  "review": "...",
  "reviewUrl": "https://steamcommunity.com/profiles/.../recommended/730/"
}
```

**`app`** — store metadata: developers, publishers, price, discount, genres,
release date, Metacritic. Free, one per app.

### Reliability

- Public, official, unauthenticated Steam endpoints. No login, no session
  cookie, no scraping of logged-in pages.
- One bad App ID never fails the run — it is recorded in `RUN_SUMMARY` and the
  remaining apps continue.
- An app with reviews but no store page (delisted, region-locked) still returns
  its reviews.
- 429 and 5xx are retried with exponential backoff; requests are paced.

### Pricing

Pay per result. The per-app `analysis` row and every `review` row are charged;
the `app` metadata row is free.

### Limits

- Steam has no star rating — reviews are Recommended / Not Recommended only.
- `playtime_at_review` is absent on a small number of older reviews. Those get
  `credibility: null` and a low default weight, and they are excluded when you
  set `minPlaytimeHours`.
- The analysis is computed over the reviews collected, before
  `minPlaytimeHours` and `excludeFreeCopies` are applied to the review rows —
  otherwise the analysis would only ever report whatever the filter selected.
- Steam's lifetime totals in the analysis cover all reviews ever written, not
  your sample. Both are on the row so you can see the difference.

# Actor input Schema

## `appIds` (type: `array`):

One or more Steam App IDs. The App ID is the number in the store URL - 730 in store.steampowered.com/app/730/. You can paste the full store URL and it will be read for you.

## `filter` (type: `string`):

Recent = newest first, best for spotting a decline after a patch. Updated = recently edited reviews. All = Steam's helpfulness ranking.

## `reviewType` (type: `string`):

Restrict to positive or negative reviews only. Leave on all if you want the sentiment analysis to mean anything.

## `language` (type: `string`):

Steam language code to restrict reviews to, for example english, schinese, german. Leave as all for every language.

## `purchaseType` (type: `string`):

Steam purchases only, keys and gifts only, or both. Key recipients often review differently to paying buyers.

## `minPlaytimeHours` (type: `integer`):

Drop reviews written by players below this many hours. Set to 2 to remove everything inside Steam's refund window. Reviews with no playtime data are also dropped when this is set.

## `excludeFreeCopies` (type: `boolean`):

Drop reviews from people who received the game for free. Free copies skew positive and are usually not representative of paying customers.

## `includeReviews` (type: `boolean`):

Output every review as its own row. Turn this off if you only want the per-app analysis.

## `includeAnalysis` (type: `boolean`):

Output one summary row per app: raw vs playtime-weighted sentiment, sentiment per credibility band, refund and free-copy share.

## `maxReviewsPerApp` (type: `integer`):

How many reviews to collect for each app before stopping. Sentiment stabilises quickly - 500 recent reviews is usually enough to see a trend, 2000 for a definitive read.

## `proxyConfiguration` (type: `object`):

Steam rate-limits by IP. Datacenter proxies are enough and are the cheapest option.

## Actor input object example

```json
{
  "appIds": [
    "730",
    "570"
  ],
  "filter": "recent",
  "reviewType": "all",
  "language": "all",
  "purchaseType": "all",
  "excludeFreeCopies": false,
  "includeReviews": true,
  "includeAnalysis": true,
  "maxReviewsPerApp": 500,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `analysis` (type: `string`):

One row per app: raw vs playtime-weighted sentiment, veteran sentiment, refund share.

## `reviews` (type: `string`):

Every review with hours played, credibility band and distortion flags.

## `all` (type: `string`):

Apps, analyses and reviews together.

## `csv` (type: `string`):

Everything as a CSV download.

## `runSummary` (type: `string`):

Counts and any app that could not be reached.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "appIds": [
        "730",
        "570"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("datalayer/steam-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "appIds": [
        "730",
        "570",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("datalayer/steam-review-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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",
    "570"
  ]
}' |
apify call datalayer/steam-review-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datalayer/steam-review-intelligence"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/XmYeSy3XuhwgeqAjA/builds/Zxd1DYWR93thl4gi2/openapi.json