# Steam Review Miner — Reputation Drop Diagnostic (`muddy_snowshoe/steam-review-miner`) Actor

Analyzes a Steam game's review history to detect an active reputation drop and classify what's actually driving it (bugs, pricing, balance, servers) — for developers and publishers checking their own or a competitor's title.

- **URL**: https://apify.com/muddy\_snowshoe/steam-review-miner.md
- **Developed by:** [해민 이](https://apify.com/muddy_snowshoe) (community)
- **Categories:** Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $30.00 / 1,000 results

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/platform/actors/running/actors-in-store#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

### What does Steam Review Miner do?

**Steam Review Miner tells you whether your game's Steam rating is actively declining right now — and exactly what's driving it.** Point it at any [Steam](https://store.steampowered.com) App ID and it pulls the game's review history, detects a statistically real reputation drop (not noise from a slow week), and — when it finds one — classifies the negative reviews into concrete categories: bugs/performance, DLC pricing, balance patches, server issues, content complaints, or off-topic controversy. No login, no scraping of private data — just Steam's own public review and histogram endpoints.

Built for developers, publishers, and community managers who want an automated early-warning system instead of manually reading through hundreds of reviews after a bad patch or a rocky launch.

### Why use Steam Review Miner?

- **Catch a decline before it snowballs.** A patch that quietly tanks your rating is easiest to fix in the first 48 hours — most teams don't notice until someone forwards them an angry tweet.
- **Skip the "is this actually a problem?" debate.** The tool only flags a game when the drop clears a volume-aware statistical bar, and separates a genuine active decline from a past controversy that already recovered (both look the same from a single star-rating glance).
- **Know if it's even fixable.** "Bugs/performance" and "server issues" are things your team can patch this week. "Off-topic controversy" is a PR conversation, not an engineering ticket — the report tells you which one you're dealing with instead of leaving you to guess from a wall of reviews.
- **Check a competitor, not just yourself.** Same input, any public App ID — useful for competitive/market research on what's currently sinking a rival title in your genre.

### How to use Steam Review Miner

1. Find the Steam App ID for the game you want to check — it's the number in the store URL (`store.steampowered.com/app/553850/...` → `553850`).
2. Click **Try for free**, paste one or more App IDs into the **Steam App IDs** field.
3. Click **Start** and wait — a few seconds per game.
4. Open the **Dataset** tab to see each game's status, the size of the drop, and (if flagged) the ranked complaint breakdown with a suggested next step for each category.

You can also run it via the [Apify API](https://docs.apify.com/api/v2) or on a [schedule](https://docs.apify.com/platform/schedules) to monitor your own game continuously and get notified the moment a drop starts.

### Input

| Field | Type | Description |
|---|---|---|
| `appIds` | array of strings | Steam App IDs to check. Required. |
| `reviewSample` | integer | How many recent reviews to sample for the complaint breakdown when a game is flagged (default 100, only fetched for flagged games — keeps cost down). |

```json
{
  "appIds": ["553850", "2868840"],
  "reviewSample": 100
}
```

### Output

One row per App ID. Example (a real result from a newly-launched game with a rough launch):

```json
{
  "appId": "3787240",
  "name": "MARVEL Tōkon: Fighting Souls",
  "status": "new-release-weak",
  "latestPeriodDate": "2026-08-06",
  "latestPeriodPositivePct": 42.2,
  "baselinePositivePct": 42.2,
  "dropPoints": 0,
  "negativeReviewsSampled": 69,
  "topComplaints": [
    { "bucket": "Bugs/Performance", "count": 30, "pct": 43, "hint": "Real stability issues — prioritize a hotfix; this is the most fixable and most damaging category." },
    { "bucket": "Server/Network", "count": 9, "pct": 13, "hint": "Infrastructure problems — check server capacity/regions, especially around launch-week spikes." }
  ]
}
```

You can download the dataset in various formats such as JSON, CSV, or Excel.

#### Data table

| Field | Meaning |
|---|---|
| `status` | `active-decline`, `new-release-weak`, `new-release-ok`, `recovered`, `stable`, or `insufficient-data` (too few reviews for a reliable signal — common for very small/niche titles) |
| `latestPeriodPositivePct` | % positive reviews in the most recent review-volume period |
| `baselinePositivePct` | % positive reviews across prior periods, for comparison |
| `dropPoints` | `baselinePositivePct − latestPeriodPositivePct`; only meaningfully computed for `active-decline` |
| `topComplaints` | Ranked complaint categories among sampled negative reviews, only populated for flagged games |

### Cost estimation

Priced per game analyzed. A single game check costs a few cents; checking your whole competitive set (10-20 games) costs well under a dollar. Games that aren't flagged skip the (heavier) review-sampling step automatically, so you're not paying extra for good news.

### Tips

- Run it on a schedule against your own App ID(s) to get an early warning the same week something goes wrong, not a month later.
- `status: insufficient-data` isn't an error — it means the game's review volume is too low (per-period) for the histogram trend to be statistically trustworthy. This is expected for very small or very old back-catalog titles.
- Steam's own "Overwhelmingly Positive → Overwhelmingly Negative" label reflects *all-time* history and can stay green for weeks after a real decline starts — that lag is exactly what this tool is built to catch earlier.

### FAQ

**Does this scrape anything private?** No — it only calls Steam's own public storefront endpoints (the same data your browser loads on any store page), no login or private data involved.

**Can I check a game I don't own/publish?** Yes, any public App ID works — useful for competitive research, not just self-monitoring.

**Found an issue or want a feature (e.g. non-English language support)?** Open an issue on the Actor's Issues tab.

# Actor input Schema

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

Steam App IDs to analyze (the number in a store page URL, e.g. store.steampowered.com/app/553850 → 553850). Check your own game, or a competitor's.

## `reviewSample` (type: `integer`):

How many of the most recent reviews to sample for the complaint breakdown, when a game is flagged as declining. Only fetched for flagged games.

## Actor input object example

```json
{
  "appIds": [
    "553850"
  ],
  "reviewSample": 100
}
```

# 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": [
        "553850"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("muddy_snowshoe/steam-review-miner").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": ["553850"] }

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

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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": [
    "553850"
  ]
}' |
apify call muddy_snowshoe/steam-review-miner --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=muddy_snowshoe/steam-review-miner",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/dzLMvg3WEUQE3BBDs/builds/MSqeH3jX2C8GM4EGG/openapi.json