# Emporioitaliano Reviews Spider (`getdataforme/emporioitaliano-reviews-spider`) Actor
The Emporioitaliano Reviews Spider efficiently extracts product reviews from the Emporioitaliano website, offering automated, high-quality data collection for businesses and researchers....
- **URL**: https://apify.com/getdataforme/emporioitaliano-reviews-spider.md
- **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community)
- **Categories:** AI, E-commerce, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $9.00 / 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
## Emporioitaliano Reviews Spider
The Emporioitaliano Reviews Spider is a powerful tool designed to extract detailed product reviews from the Emporioitaliano website. This actor enables businesses and researchers to gather valuable customer feedback, analyze market trends, and enhance competitive intelligence by automating the data collection process.
### Features
- **Automated Review Extraction**: Seamlessly scrape product reviews from specified URLs.
- **High Data Quality**: Ensures accurate and reliable data collection.
- **Efficient Performance**: Optimized for speed and minimal resource usage.
- **Scalable**: Handle multiple URLs simultaneously for large-scale data needs.
- **User-Friendly Configuration**: Simple setup with minimal input requirements.
- **Comprehensive Output**: Detailed review data including ratings, timestamps, and more.
- **Versatile Export Options**: Export data in JSON, CSV, or Excel formats.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|--------------|--------|----------|------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|
| ProductUrls | Array | No | List of product URLs to scrape reviews from. | ["https://emporioitaliano.com/collections/tre-marie/products/tre-marie-panettone-milanese-750-grams-1-7-lb-the-original-milanese"] |
### Example Usage
#### Example Input JSON
```json
{
"ProductUrls": [
"https://emporioitaliano.com/collections/tre-marie/products/tre-marie-panettone-milanese-750-grams-1-7-lb-the-original-milanese"
]
}
````
#### Example Output JSON
```json
[
{
"Review_ID": "e2dc38ae-f771-4605-8da4-329954ae8b27",
"Product_ID": 6712822005956,
"Product_URL": "https://emporioitaliano.com/collections/tre-marie/products/tre-marie-panettone-milanese-750-grams-1-7-lb-the-original-milanese",
"Product_Title": "Tre Marie Traditional Pandoro 1000 gr",
"Verified_Buyer": "true",
"Rating": "5",
"Author": " ",
"Title": "Great Service!",
"Body": "Great product and excellent service.",
"Thumbs_Up": "0",
"Thumbs_Down": "0",
"Timestamp": "2026-01-05 15:29:13 UTC",
"actor_id": "kqPc2e7YT8fTu53DP",
"run_id": "wBCpnRDlRATiJit61"
}
]
```
### Use Cases
- **Market Research and Analysis**: Gain insights into customer preferences and market trends.
- **Competitive Intelligence**: Monitor competitor products and customer feedback.
- **Price Monitoring**: Track product pricing and promotional strategies.
- **Content Aggregation**: Collect user-generated content for analysis.
- **Academic Research**: Study consumer behavior and sentiment.
- **Business Automation**: Automate data collection for streamlined operations.
### Installation and Usage
1. Search for "Emporioitaliano Reviews Spider" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters.
4. Click "Start" to begin extraction.
5. Monitor progress in the log.
6. Export results in your preferred format (JSON, CSV, Excel).
### Output Format
The output is a JSON array where each object contains detailed review information:
- **Review\_ID**: Unique identifier for the review.
- **Product\_ID**: Identifier for the product.
- **Product\_URL**: URL of the product page.
- **Product\_Title**: Title of the product.
- **Verified\_Buyer**: Indicates if the review is from a verified buyer.
- **Rating**: Customer rating of the product.
- **Author**: Name of the review author.
- **Title**: Title of the review.
- **Body**: Content of the review.
- **Thumbs\_Up/Down**: Count of helpfulness votes.
- **Timestamp**: Date and time of the review.
### Support
For custom/simplified outputs or bug reports, please contact:
- Email: support@getdataforme.com
- Subject line: "custom support"
- Contact form:
We're here to help you get the most out of this Actor!
# Actor input Schema
## `ProductUrls` (type: `array`):
The product urls for the spider.
## Actor input object example
```json
{
"ProductUrls": [
"https://emporioitaliano.com/collections/tre-marie/products/tre-marie-panettone-milanese-750-grams-1-7-lb-the-original-milanese"
]
}
```
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/emporioitaliano-reviews-spider").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("getdataforme/emporioitaliano-reviews-spider").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 '{}' |
apify call getdataforme/emporioitaliano-reviews-spider --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/emporioitaliano-reviews-spider",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Emporioitaliano Reviews Spider",
"description": "The Emporioitaliano Reviews Spider efficiently extracts product reviews from the Emporioitaliano website, offering automated, high-quality data collection for businesses and researchers....",
"version": "0.0",
"x-build-id": "WWblRk7GLd8WpDhNb"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~emporioitaliano-reviews-spider/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-emporioitaliano-reviews-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/getdataforme~emporioitaliano-reviews-spider/runs": {
"post": {
"operationId": "runs-sync-getdataforme-emporioitaliano-reviews-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/getdataforme~emporioitaliano-reviews-spider/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-emporioitaliano-reviews-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"ProductUrls": {
"title": "Product Urls",
"minItems": 1,
"type": "array",
"description": "The product urls for the spider.",
"default": [
"https://emporioitaliano.com/collections/tre-marie/products/tre-marie-panettone-milanese-750-grams-1-7-lb-the-original-milanese"
],
"items": {
"type": "string"
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```