# Rakuten 楽天市場 Review VOC Collector (`research_master/rakuten-review-voc`) Actor

Complete, auditable Rakuten Ichiba review collection for VOC research. Batch selected products, crawl all review pages, and verify coverage with page/run audit records.

- **URL**: https://apify.com/research\_master/rakuten-review-voc.md
- **Developed by:** [Research Master](https://apify.com/research_master) (community)
- **Categories:** E-commerce, Agents, Automation
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 2 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $0.50 / 1,000 dataset records

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

## Rakuten Review VOC Collector

Collect complete, auditable review corpora from public Rakuten Ichiba product review pages for Voice of Customer (VOC), market research, and downstream text analysis.

> **Use this Actor when you already know which products matter and need the review corpus with evidence that every expected page was collected.**

### Why this Actor is different

Many Rakuten Actors are designed for product discovery, listing extraction, or a small sample of recent reviews. This Actor is deliberately narrower: it starts from a researcher's selected product list and collects the review corpus in a form that can be audited.

| Need | Product/search Actors | Sample-oriented review Actors | This Actor |
| --- | --- | --- | --- |
| Find products by keyword/category | Primary use | Sometimes included | **Not included** |
| Collect a few recent reviews quickly | Indirect | Primary use | Supported with a page cap, but not the main purpose |
| Collect every available review page | Often not included | May stop at a review limit | **Default behavior** |
| Run a ranked batch of selected products | Varies | Often URL-by-URL | **Yes; research IDs and ranks are preserved** |
| Verify collection completeness | Usually row count only | Usually row count only | **Page-level and run-level audit records** |
| Prepare raw data for VOC coding/NLP | Requires reshaping | Often limited fields | **Rich review, reviewer, SKU, item, and page metadata** |
| AI-generated review summary | Sometimes included | Sometimes included | **No; returns source data for your own analysis** |

Apify compute, storage, and data-transfer usage may incur charges according to the user's Apify plan and this Actor's Store pricing configuration.

### When to use it

Choose this Actor when:

- You have a defined product universe, such as a top-N list, competitor set, or tracked SKU list.
- You need all available public reviews rather than a small preview.
- You must retain product rank and stable research keys across a multi-product crawl.
- You need to prove which review pages were collected and identify gaps before analysis.
- You want raw review-level evidence for taxonomy coding, sentiment analysis, topic modeling, or Excel/BI reporting.

Choose another Actor or workflow when:

- You still need to discover products by keyword, category, ranking, or seller.
- You only need a handful of recent reviews for a quick check.
- You want an AI summary instead of the underlying review corpus.
- You need non-public, authenticated, or account-specific Rakuten data.
- You need browser automation for interactions beyond public product review pages.

### Input

`products` is required. Each product must provide either:

- `review_url`, or
- both `shop_id` and `item_id`.

Research fields such as `product_rank`, `item_key`, `shop_code`, and `item_url` are strongly recommended. They are carried into the output so the dataset can be joined back to the selected product universe.

```json
{
  "products": [
    {
      "product_rank": 1,
      "item_key": "e-techpowershop:10000062",
      "item_id": "10000062",
      "shop_id": 429056,
      "shop_code": "e-techpowershop",
      "item_url": "https://item.rakuten.co.jp/e-techpowershop/set-d1018/",
      "review_url": "https://review.rakuten.co.jp/item/1/429056_10000062/1.1/"
    }
  ],
  "concurrency": 5,
  "max_pages_per_product": 0
}
```

#### Controls

| Field | Meaning |
| --- | --- |
| `concurrency` | Parallel requests, from 1 to 10. Default: `5`. Reduce it if Rakuten starts returning errors. |
| `max_pages_per_product` | `0` collects all detected pages. A positive number caps pages per product for pilots or quick samples. |

Rakuten currently exposes up to 30 reviews per review page. The Actor reads the displayed review count on page 1 and calculates the expected page count automatically.

### Output

The default dataset intentionally contains three record types. Filter on `record_type` before analysis.

| `record_type` | One row per | Purpose |
| --- | --- | --- |
| `review` | Review | VOC text and metadata, including rating, title, body, dates, reviewer attributes when public, SKU information, helpful count, media, item metadata, and source URL. |
| `page_audit` | Review page | Coverage evidence: page number, expected pages, displayed review count, and reviews found on the page. |
| `run_audit` | Actor run | Run totals and a `failures` array for products or pages that could not be collected. |

For a complete-corpus run, validate all three conditions before analysis:

1. `run_audit.failures` is empty.
2. Every expected page number has a corresponding `page_audit` row.
3. Unique `review_key` count is consistent with the collected page totals and the displayed product review count.

The `review_key` and `encrypted_review_key` fields support downstream deduplication. The Actor itself returns source rows and does not silently remove records.

### Validation evidence

In an internal production QA run on 2026-08-20, 50 selected products achieved coverage of **312 / 312 expected review pages**, producing **8,613 unique reviews** after downstream deduplication. This is validation evidence for that run, not a guarantee that Rakuten page structure or availability will remain unchanged.

### Limits and responsible use

- Collects public Rakuten Ichiba product review pages only.
- Does not search for products, rank products, or choose a market sample.
- Does not use residential proxies or bypass authenticated access controls.
- Rakuten page-structure changes can require parser maintenance.
- A run can finish while individual pages are listed in `run_audit.failures`; always inspect the audit record.
- Use a reasonable concurrency and follow applicable laws, Rakuten terms, and your organization's data-handling requirements.

# Actor input Schema

## `products` (type: `array`):

Products to collect. Each item needs review\_url, or both shop\_id and item\_id. Research identifiers are preserved in the output.

## `concurrency` (type: `integer`):

Parallel page requests. Reduce this if requests become unstable.

## `max_pages_per_product` (type: `integer`):

Use 0 for every detected review page. Use a positive cap for a pilot or quick sample.

## Actor input object example

```json
{
  "products": [],
  "concurrency": 5,
  "max_pages_per_product": 0
}
```

# Actor output Schema

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

Default dataset containing review, page\_audit, and run\_audit records. Use record\_type to distinguish them.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("research_master/rakuten-review-voc").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("research_master/rakuten-review-voc").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 '{}' |
apify call research_master/rakuten-review-voc --silent --output-dataset

```

## MCP server setup

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

```

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/rlfDBron8tnRLxUNn/builds/O2qNNozsoZfp7mkOm/openapi.json