# Steam Reviews Scraper — Game Reviews & Playtime (`hichemdev/steam-reviews-scraper`) Actor

Scrape Steam game reviews: full review text, recommended or not, votes up and funny, comment count, date, language, and the reviewer's playtime at review, total playtime, games owned and review count, plus each game's overall positive percentage. No API key.

- **URL**: https://apify.com/hichemdev/steam-reviews-scraper.md
- **Developed by:** [Hichem Ben Moussa](https://apify.com/hichemdev) (community)
- **Categories:** Business, Marketing, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 reviews

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Steam Reviews Scraper — Game Reviews & Playtime

Scrape **Steam game reviews** with the one signal no other review source has: **how long the reviewer actually played**.

Full review text, recommended or not, helpful and funny votes, review date, language, and the reviewer's playtime at the moment they wrote it, their total playtime, how many games they own and how many reviews they have written — plus each game's overall positive percentage.

No API key required.

### What you get

| Field | Description |
|---|---|
| `review` | Full review text |
| `isPositive`, `recommendation` | Thumbs up or down, as a boolean and as a label |
| `playtimeAtReviewHours` | **Hours played when the review was written** |
| `playtimeTotalHours` | Hours played now — a much higher number means they kept playing after a bad review |
| `playtimeLast2WeeksHours` | Whether they still play it |
| `votesUp`, `votesFunny`, `commentCount` | Community response |
| `weightedVoteScore` | Steam's own helpfulness weighting |
| `createdAt`, `updatedAt` | Review dates |
| `language` | Review language |
| `steamPurchase`, `receivedForFree` | Whether they paid, and whether the copy was free |
| `writtenDuringEarlyAccess` | Review predates 1.0 |
| `onSteamDeck` | Played mainly on Steam Deck |
| `authorName`, `authorSteamId`, `authorProfileUrl` | Reviewer identity as Steam publishes it |
| `authorGamesOwned`, `authorReviewCount` | Reviewer credibility |
| `appName`, `developers`, `publishers`, `genres` | Game context |
| `appTotalReviews`, `appTotalPositive`, `appTotalNegative` | Store-wide counts |
| `appPositivePercent`, `appReviewScoreDesc` | e.g. `85.8` and *Very Positive* |

### Example input

```json
{
  "apps": ["730"],
  "language": "english",
  "sortBy": "recent",
  "maxReviewsPerApp": 300
}
```

Build a complaints backlog from players who actually know the game:

```json
{
  "apps": ["https://store.steampowered.com/app/730/"],
  "onlyNegative": true,
  "minPlaytimeHours": 50,
  "keyword": "cheaters"
}
```

App IDs can be given as a bare number or as any Steam store URL.

### Example output

```json
{
  "appName": "Counter-Strike 2",
  "recommendation": "Not Recommended",
  "review": "cheater's don't even try to hide that they are cheating, top 100 are full of cheaters...",
  "playtimeAtReviewHours": 953.2,
  "playtimeTotalHours": 1024.9,
  "votesUp": 12,
  "createdAt": "2026-09-25T18:22:41.000Z",
  "language": "english",
  "steamPurchase": true,
  "authorGamesOwned": 168,
  "appTotalReviews": 9893037,
  "appPositivePercent": 85.8,
  "appReviewScoreDesc": "Very Positive"
}
```

### Who uses this

- **Game studios** — read what players with 100+ hours say, separately from what players with 20 minutes say. The `minPlaytimeHours` filter is the whole point.
- **Publishers evaluating a genre** — sentiment and complaint themes across competing titles
- **Community and support teams** — catch a post-patch review-bomb the day it starts, via `sortBy: recent` on a schedule
- **Market researchers** — positive percentage over time, by language, tells you which regions a game is failing in
- **Store and review aggregators** — a full review corpus with reviewer context
- **Sentiment and LLM pipelines** — dated, versioned, language-tagged text with a credibility signal attached

`playtimeAtReviewHours` versus `playtimeTotalHours` is the interesting pair: a negative review at 10 hours from someone who has since played 400 is a very different datum from one at 10 hours who stopped there.

### Why playtime changes the analysis

Every other review platform gives you a star and some text. Steam tells you the reviewer had 953 hours in the game. That turns review mining from vibes into something you can weight:

- Filter to reviewers above a playtime threshold and the noise largely disappears
- `authorReviewCount` and `authorGamesOwned` surface drive-by accounts
- `receivedForFree` separates paying players from key recipients
- `writtenDuringEarlyAccess` stops pre-1.0 complaints polluting a launch retrospective

### Notes

- **Language matters to your result count.** The default is `english`; set it to `all` to pull every language, which is what you want for regional sentiment and gives you many more reviews.
- **`sortBy: recent` is the right choice for monitoring**, and *Most helpful* for a one-off qualitative read. Steam's pagination is cursor-based, and the actor stops when a page returns nothing new rather than trusting the cursor to end.
- **Steam keeps issuing a cursor indefinitely**, so an all-duplicate page is the real end of the feed. Handled.
- **Review counts in `query_summary` reflect your language and type filters**, not the game's global totals, when those filters are set — the store-wide figures in each row come from the same filtered summary, so read them as "within this slice".
- Reviewer names and profile URLs are published by Steam on the public store page; treat them as public profile data and handle them accordingly.

### Pricing

Pay per result. Each review returned counts as one result, and the playtime, keyword and sentiment filters are applied before charging — so a run that keeps only negative reviews from 50-hour players bills for just those.

### Notes

- Data comes from Steam's public review API and store details endpoint.
- This is an unofficial actor and is not affiliated with Valve or Steam.

# Actor input Schema

## `apps` (type: `array`):

Steam app IDs or store URLs, e.g. 730 or https://store.steampowered.com/app/730/.

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

Steam language code, e.g. english, french, schinese. Use all for every language.

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

Positive, negative or both.

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

Steam purchases only, keys and gifts only, or both.

## `sortBy` (type: `string`):

Newest first, or the reviews Steam considers most helpful.

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

Keep only reviewers with at least this many hours played - a good way to filter out drive-by reviews.

## `onlyNegative` (type: `boolean`):

Shortcut for the review type above. Useful for building a complaints backlog.

## `keyword` (type: `string`):

Keep only reviews mentioning this word, e.g. crash, optimization, cheaters.

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

Cap per game. 0 means no cap.

## `maxReviews` (type: `integer`):

Stop after this many reviews in total.

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

Optional. A proxy is not required for this actor.

## Actor input object example

```json
{
  "apps": [
    "730"
  ],
  "language": "english",
  "reviewType": "all",
  "purchaseType": "all",
  "sortBy": "recent",
  "onlyNegative": false,
  "maxReviewsPerApp": 300,
  "maxReviews": 50,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `results` (type: `string`):

All scraped items as JSON.

## `overview` (type: `string`):

Browse results in the Apify Console.

# 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 = {
    "apps": [
        "730"
    ],
    "language": "english",
    "maxReviews": 50
};

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

# Prepare the Actor input
run_input = {
    "apps": ["730"],
    "language": "english",
    "maxReviews": 50,
}

# Run the Actor and wait for it to finish
run = client.actor("hichemdev/steam-reviews-scraper").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 '{
  "apps": [
    "730"
  ],
  "language": "english",
  "maxReviews": 50
}' |
apify call hichemdev/steam-reviews-scraper --silent --output-dataset

```

## MCP server setup

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

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/5pwkpp39wP4LW1l3o/builds/nQVhmjeBC5P9kgLbq/openapi.json