# Japanese Review Classifier — sentiment & complaints · $0.5/1k (`leoworks/japanese-review-classifier`) Actor

Classify Japanese customer reviews — Rakuten (楽天レビュー), Amazon.co.jp and any review scraper's dataset — into complaint types, sentiment and purchase motive with probabilities. Japanese review sentiment analysis (レビュー分析), no prompts, no LLM key.

- **URL**: https://apify.com/leoworks/japanese-review-classifier.md
- **Developed by:** [Leoworks](https://apify.com/leoworks) (community)
- **Categories:** AI, E-commerce, Agents
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.43 / 1,000 review classifications

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?

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

## Japanese Review Classifier — sentiment & complaints

**For sellers, brands and AI agents that have Japanese reviews** — from a Rakuten (楽天) review scraper, any other Apify dataset, or pasted as text — and need every review labelled with complaint type, sentiment and purchase motive, for $0.50 per 1,000 reviews, with no scraping, prompt writing or LLM key.

- **Complaint types** — delivery, quality/defect, size/fit, price/value, shop response, packaging, no effect, skin/body reaction, other (multi-label, each with a probability)
- **Sentiment** — positive / neutral / negative
- **Purchase motive** — price/discount, reviews & reputation, brand, gift, repurchase, unknown
- **Your own labels** — up to 10 yes/no criteria in plain English or Japanese (e.g. "mentions charging speed", "配達員の対応が悪い")

Labels come with English keys **and Japanese names** (e.g. `quality_defect` / `品質・不良`, field `labelJa`).

**Use it to:** find why Rakuten buyers leave 1-star reviews (楽天レビュー分析) · compare complaint mix against competitors · separate shipping and packaging problems from product defects · tag Japanese reviews for a dashboard · Japanese review sentiment analysis (レビュー分析) at scale.

### Output sample

Real rows from run `GrEqVhVam3XUpbStj` (2026-10-08), reviews of power banks on Rakuten, with one custom label ("mentions charging speed"). English translations are added here for readers; the Actor returns the original text.

| text (Japanese) | English (added) | complaint (probability) | sentiment | motive | custom: mentions charging speed |
|---|---|---|---|---|---|
| 早くに発送してくださり ありがとうございます。 予想より重たかったです。 色はかわいいです。 本体の充電 時間がかかりすぎて そこが難点… | Shipped fast, thanks. Heavier than expected. Cute colour. Charging the unit takes far too long — that's the downside. | other (0.70) | neutral (0.73) | unknown | **true** |
| すぐに届きましたが、バッテリーを充電しても 39%から永遠に上がりません。 不良品ですかね。 困ります。交換してもらいたいです。 | Arrived quickly, but the battery never charges past 39%. Defective? I want an exchange. | quality_defect (0.97) | negative (1.00) | unknown | false |
| 箱が潰れて中身もどうなってるかわかりませんて言われました どういう扱いしてるんですかね？  新しく商品を送り返してくれましたが、再発送の… | Told the box was crushed and the contents might be damaged… resent without notice, no apology. Never again. | packaging (0.96), customer_service (0.91), delivery (0.60) | negative (1.00) | unknown | false |
| 安定の商品でした。有難うございます。 リピートの際には、また購入させていただきます。 | Reliable product, thank you. I'll buy again. | none (0.98) | positive (0.99) | unknown | false |

Each row also has `labelJa` names, the rating and the ID fields you choose (`reviewId`, `productId`, …), and in full mode `complaintScores` for every complaint type.

### Input example

The form default — two pasted reviews, no dataset needed (about $0.001, 2 seconds):

```json
{
  "texts": [
    "ダンボールが潰れて届きました。中身は無事でしたが、梱包をもう少し丁寧にしてほしいです。",
    "すぐ壊れました。充電できません。返品したいです。"
  ]
}
```

To classify a Rakuten review scraper's output, pass its dataset instead — the text, rating and ID fields are detected automatically (checked on run `b2QEJvgEe4N9TURc1`: 15 reviews with `reviewId`, `productId` and `rating` kept):

```json
{
  "datasetId": "YOUR_RAKUTEN_REVIEW_DATASET_ID",
  "customLabels": ["mentions charging speed"]
}
```

### Pricing

Pay only for classified reviews — no subscription.

| Event | Price | When |
|---|---|---|
| `review-judged` | $0.0005 | One review classified (complaint types, sentiment, purchase motive and any custom labels). |

That is **$0.50 per 1,000 reviews**. **First run with the form defaults: about $0.001** (2 reviews, 2 seconds).

**Cost examples**

| Reviews | Cost |
|---|---|
| 100 | $0.05 |
| 1,000 | $0.50 |
| 10,000 | $5.00 |
| 100,000 | $50.00 |

With the free $5 monthly Apify credit you can classify about **10,000 reviews**.

Items without review text are skipped and **not charged**. Reviews that fail after retries are reported with an `error` field and **not charged**. If you set a maximum cost per run, the Actor stops cleanly when it is reached.

### Works with

**Rakuten review scrapers on Apify Store** — run one, then pass its dataset to this Actor. Field detection was checked against real output of both.

| Source | Scraper on Apify Store | Text field | Rating | IDs kept |
|---|---|---|---|---|
| Rakuten reviews | [Rakuten Japan Reviews Scraper (piotrv1001)](https://apify.com/piotrv1001/rakuten-japan-reviews-scraper) | `text` | `rating` | `reviewId`, `productId` |
| Rakuten reviews | [Rakuten Ichiba Reviews Scraper (axlymxp)](https://apify.com/axlymxp/rakuten-ichiba-reviews-scraper) | `body` | `rating` | `item_id`, `shop_id` |

**Also:** the Apify API and JavaScript/Python clients · Apify Schedules · Claude, Cursor and Claude Code through the Apify MCP server (next section) · for Korean reviews, our [Korean Review Classifier](https://apify.com/leoworks/korean-review-classifier) uses the same labels.

### Use with Claude, Cursor or Claude Code (MCP)

Add the Apify MCP server with this Actor as a tool and ask your agent in plain language — for example *"Classify these Japanese reviews and tell me the top complaint types: …"* or *"Classify dataset abc123 from my review scraper run and summarize the complaints."* The agent calls the tool `leoworks--japanese-review-classifier` and reads the labels with `get-dataset-items`.

Claude Desktop or Cursor (`mcp.json`):

```json
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=leoworks/japanese-review-classifier",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}
```

Claude Code: `claude mcp add --transport http apify "https://mcp.apify.com?tools=leoworks/japanese-review-classifier" --header "Authorization: Bearer YOUR_APIFY_TOKEN"`. Leave out the header to sign in with OAuth in the browser instead. Your Apify token is in Console → Settings → API & Integrations. We verified this setup with the Apify MCP server (v0.17.3) on 2026-10-08: the agent classified a pasted review in 2.5 seconds (run `GUnh4xa8RtCXm76yZ`).

### Output (one row per review)

```json
{
  "index": 1,
  "text": "箱が潰れて中身もどうなってるかわかりませんて言われました\nどういう扱いしてるんですかね？\n\n新しく商品を送り返してくれましたが、再発送の連絡もなくいつのまにか届いてました\n謝罪の言葉もこちらから言うまでなく2度と買いません",
  "labels": {
    "complaint": [
      {
        "label": "packaging",
        "labelJa": "梱包",
        "probability": 0.96
      },
      {
        "label": "customer_service",
        "labelJa": "ショップ対応",
        "probability": 0.91
      },
      {
        "label": "delivery",
        "labelJa": "配送",
        "probability": 0.6
      }
    ],
    "sentiment": {
      "label": "negative",
      "labelJa": "否定",
      "probability": 1,
      "confidence": 1
    },
    "motive": {
      "label": "unknown",
      "labelJa": "不明",
      "probability": 0.9,
      "confidence": 0.88
    }
  }
}
```

When no complaint type passes the threshold, `complaint` is `[{ "label": "none", "labelJa": "不満なし" }]`. **Minimal mode** returns label keys only.

#### Summary by product (REPORT)

Each run also saves a **REPORT** record (Output tab → *Summary by product*) at no extra charge: for every product, the complaint rate and complaint mix, sentiment shares, purchase motives, average rating and the 3 strongest complaint reviews — plus the same for all reviews together. Products are grouped by `summaryGroupField` (detected automatically from fields such as `productId`, `productName` or `placeId` when empty).

```json
{
  "groupField": "productId",
  "groups": [{
    "group": "A",
    "reviews": 3,
    "averageRating": 2.67,
    "complaintRate": 0.667,
    "complaints": [{ "label": "delivery", "count": 1, "share": 0.333 }, { "label": "quality_defect", "count": 1, "share": 0.333 }],
    "sentiment": { "negative": 0.667, "positive": 0.333 },
    "motive": { "unknown": 0.667, "price": 0.333 },
    "exampleComplaints": [{ "complaint": "delivery", "rating": 2, "text": "配送が1週間もかかりました。遅すぎます" }]
  }]
}
```

### Accuracy

Measured on hand-labelled Japanese Rakuten reviews (2026-10-07/08). The questions were adjusted on the first set (a crushed outer box counts as packaging, not a product defect), then checked on a new set of different products.

| Set | Products | Reviews (1–2 stars) | Complaint type | Sentiment |
|---|---|---|---|---|
| **New check set — not used for adjusting** | Power banks | 47 (32) | **91%** | **96%** |
| First set | Bottled water | 50 (30) | 96% | 100% |
| Second set | Mugs, power banks | 40 (5) | 100% | 100% |

Probabilities are calibrated — raise **Complaint threshold** for fewer, surer labels. Automated labels can be wrong; check samples before making big decisions.

### Limits

| Item | Limit |
|---|---|
| Review length | First 4,000 characters are used |
| Custom labels | Up to 10, each up to 200 characters |
| Dataset size | Any — datasets are read in pages of 1,000 |
| Language | Japanese (measured). Other languages: see our Korean and Spanish classifiers |
| Speed | About 100 reviews in 5 seconds |
| Data | Only the text, rating and the ID fields you choose are sent for classification; reviewer names are not output |

### FAQ

**Which AI makes the judgments?** Jev, TypeSafe's decision model (version `jev-1.13.0`, pinned). Jev answers each label with a calibrated probability instead of generated text, so the same input gets the same answer from run to run. Only the review text, its rating and your custom labels are sent to Jev; reviewer names and other fields are not.

**Does it scrape Rakuten?** No. It classifies reviews you already have — run a Rakuten review scraper first (see **Works with**) or paste texts.

**Does it generate text or summaries?** No. It only assigns labels with probabilities — fast, cheap and consistent.

### Disclaimer

> Independent tool — not affiliated with, endorsed by or sponsored by Rakuten Group, or by the authors of the scrapers listed above. Names are used only to describe compatible data sources.

### Reviews and support

If this Actor saved you time, a short review on Apify Store helps others find it. Questions or a dataset whose fields are not detected? Open an issue in the **Issues** tab — we answer within a day.

### Changelog

See the Changelog tab.

# Changelog

This Actor's version history is a separate document: https://apify.com/leoworks/japanese-review-classifier/changelog.md

# Actor input Schema

## `datasetId` (type: `string`):

An Apify dataset containing reviews — for example the output of a Rakuten review scraper. Use this OR “Review texts”.

## `texts` (type: `array`):

Paste review texts directly (one per line). Use this OR “Reviews dataset”.

## `textField` (type: `string`):

Field holding the review text. Leave empty to auto-detect (content, text, review, body, reviewText, …).

## `extraTextFields` (type: `array`):

Fields to prepend to the text (up to 5), e.g. `title` for Coupang review headlines. Leave empty if unsure.

## `idFields` (type: `array`):

Fields copied unchanged from each input item to the output so you can join results back (e.g. reviewId, productId). Leave empty to auto-pick reviewId/id/url.

## `customLabels` (type: `array`):

Up to 10 extra yes/no labels (each up to 200 characters) written in plain language (English or Japanese), e.g. “mentions the smell”, “配達員の対応が悪い”. Each gets a probability.

## `complaintThreshold` (type: `number`):

Minimum probability (0–1) for a complaint type or custom label to be reported. Raise it for fewer, surer labels.

## `outputMode` (type: `string`):

Minimal mode returns only label keys — smaller and easier to aggregate.

## `summaryGroupField` (type: `string`):

Field in your dataset that identifies the product (e.g. `productId`, `productName`, `placeId`). The run then saves a REPORT record with, per product: complaint rate and complaint mix, sentiment shares, purchase motives, average rating and 3 example complaints. Leave empty to detect it automatically (overall summary only if none is found). No extra charge.

## `maxItems` (type: `integer`):

Classify at most this many reviews (0 = all).

## `maxConcurrency` (type: `integer`):

Reviews classified in parallel.

## `healthCheck` (type: `boolean`):

Internal: fail the run when results look degraded (used by the developer's scheduled checks).

## Actor input object example

```json
{
  "texts": [
    "ダンボールが潰れて届きました。中身は無事でしたが、梱包をもう少し丁寧にしてほしいです。",
    "すぐ壊れました。充電できません。返品したいです。"
  ],
  "extraTextFields": [],
  "idFields": [],
  "customLabels": [],
  "complaintThreshold": 0.5,
  "outputMode": "full",
  "maxItems": 0,
  "maxConcurrency": 10,
  "healthCheck": false
}
```

# Actor output Schema

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

No description

## `summary` (type: `string`):

No description

## `report` (type: `string`):

No description

# 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 = {
    "texts": [
        "ダンボールが潰れて届きました。中身は無事でしたが、梱包をもう少し丁寧にしてほしいです。",
        "すぐ壊れました。充電できません。返品したいです。"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("leoworks/japanese-review-classifier").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 = { "texts": [
        "ダンボールが潰れて届きました。中身は無事でしたが、梱包をもう少し丁寧にしてほしいです。",
        "すぐ壊れました。充電できません。返品したいです。",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("leoworks/japanese-review-classifier").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 '{
  "texts": [
    "ダンボールが潰れて届きました。中身は無事でしたが、梱包をもう少し丁寧にしてほしいです。",
    "すぐ壊れました。充電できません。返品したいです。"
  ]
}' |
apify call leoworks/japanese-review-classifier --silent --output-dataset

```

## MCP server setup

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

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/GQyJVM9xKDyM8XQ2l/builds/nG6KlhdewqVYvUs2a/openapi.json