# Steam Reviews Scraper - Sentiment & Review-Bomb Detection (`justfeel/steam-reviews-scraper`) Actor

Extract Steam game reviews via Valve's own public API. Playtime at review, hardware specs, helpfulness votes, developer replies. Built-in review-bomb spike detection and pros/cons keyword extraction. No login, no browser.

- **URL**: https://apify.com/justfeel/steam-reviews-scraper.md
- **Developed by:** [Yusuf](https://apify.com/justfeel) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 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.

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 — Sentiment, Playtime & Review-Bomb Detection

Extract **Steam game reviews** for any app — as clean, structured JSON, built directly on Valve's own public review API. No login, no browser, no proxies, so it never breaks the way headless-browser scrapers do.

### What you get per review

```json
{
    "review_id": "180325487",
    "app_id": "730",
    "app_name": "Counter-Strike 2",
    "recommended": true,
    "text": "Been playing since 1.6, still the best.",
    "language": "english",
    "created_at": "2026-06-01T14:22:03+00:00",
    "updated_at": "2026-06-01T14:22:03+00:00",
    "votes_helpful": 12,
    "votes_funny": 0,
    "weighted_vote_score": 0.63,
    "comment_count": 1,
    "steam_purchase": true,
    "received_for_free": false,
    "written_during_early_access": false,
    "playtime_at_review_minutes": 4820,
    "playtime_forever_minutes": 9143,
    "playtime_last_two_weeks_minutes": 620,
    "author_num_reviews": 8,
    "author_num_games_owned": 143,
    "author_steam_id": "76561198012345678",
    "primarily_steam_deck": false,
    "hardware_os": "Windows 11",
    "hardware_cpu": "AMD Ryzen 5 5500",
    "hardware_gpu": "NVIDIA GeForce GTX 1650"
}
```

### Built-in analytics (free, not billed)

When `includeSummary` is on (default), one extra summary record per app is pushed alongside the reviews:

```json
{
    "type": "summary",
    "app_id": "730",
    "app_name": "Counter-Strike 2",
    "steam_review_score": 8,
    "steam_review_score_desc": "Very Positive",
    "steam_total_positive": 1264535,
    "steam_total_negative": 214396,
    "steam_total_reviews": 1478931,
    "sample_size": 200,
    "review_bomb_alert": { "detected": false, "flagged_days": [] },
    "top_pros_keywords": [{ "word": "gameplay", "count": 14 }],
    "top_cons_keywords": [{ "word": "cheaters", "count": 9 }]
}
```

- **Review-bomb spike detection** — flags days where review volume spikes far above the sample's median, a signal used to catch coordinated review-bombing or a patch/controversy blowing up
- **Pros/cons keyword extraction** — top recurring words split by recommended vs. not-recommended reviews, a quick read on *why* players like or dislike the game
- **Steam's own review score** attached to every run, so you can track sentiment drift over time on a schedule

### Why this scraper

- ✅ **Never breaks** — built on Valve's official public review API, the same one the Steam store website itself uses
- ✅ **Richer than a UI scrape** — hardware specs and playtime *at the moment of review* aren't visible on the Steam store page itself, only through the API
- ✅ **Multi-game in one run** — track your own game plus competitors side by side
- ✅ **Fast & cheap** — no browser, no proxies, no login
- ✅ **Review-bomb and sentiment signals out of the box**

### Use cases

- **Indie devs & publishers**: monitor sentiment after every patch, catch a review-bomb or cheating wave early
- **Game marketing / ASO agencies**: mine competitor reviews for feature gaps and messaging
- **Market research & investment analysis**: track public sentiment on gaming studios' flagship titles over time
- **AI & NLP pipelines**: feed structured review text into sentiment/LLM analysis

### Input example

```json
{
    "appIds": ["730", "1245620"],
    "reviewType": "all",
    "filter": "recent",
    "language": "all",
    "purchaseType": "all",
    "maxReviewsPerApp": 200,
    "includeSummary": true
}
```

### Filters

- `reviewType`: `all` / `positive` / `negative`
- `filter`: `recent` (newest first), `all` (Steam's helpfulness-ranked all-time top reviews), `updated`
- `language`: any Steam language code (`english`, `schinese`, `german`, ...) or `all`
- `purchaseType`: `all` / `steam` / `non_steam_purchase`

### FAQ

**Is this legal?** The actor reads Steam's publicly available review API — the same data anyone sees on the store page. No login, no private data.

**Where do I find an app ID?** In the game's store URL: `store.steampowered.com/app/730/CounterStrike2` → `730`.

**How many reviews can I get?** As many as Steam has for that filter — pagination follows Steam's cursor until exhausted or your `maxReviewsPerApp` cap is hit.

# Actor input Schema

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

Numeric Steam app IDs. Find it in the game's store URL, e.g. store.steampowered.com/app/730/CounterStrike2 -> 730.

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

Filter by whether the review recommends the game.

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

"Recent" returns the newest reviews first. "All" returns Steam's helpfulness-ranked reviews (best for a game's all-time top reviews). "Updated" sorts by last edit.

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

Steam language code (english, schinese, german, ...) or "all" for every language.

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

Restrict to reviews from Steam purchasers, non-Steam (key) activations, or both.

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

Stop fetching once this many reviews have been collected for a given app.

## `includeSummary` (type: `boolean`):

Push one extra (unbilled) summary record per app: Steam's overall review score, review-bomb spike detection on the collected sample, and top pros/cons keywords.

## Actor input object example

```json
{
  "appIds": [
    "730"
  ],
  "reviewType": "all",
  "filter": "recent",
  "language": "all",
  "purchaseType": "all",
  "maxReviewsPerApp": 200,
  "includeSummary": true
}
```

# 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"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("justfeel/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 = { "appIds": ["730"] }

# Run the Actor and wait for it to finish
run = client.actor("justfeel/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 '{
  "appIds": [
    "730"
  ]
}' |
apify call justfeel/steam-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,justfeel/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/uNCnDZlAFOrzvcZsf/builds/Godmo0NeApY5gODHt/openapi.json