# Patagonia Reviews Scraper (`getdataforme/patagonia-reviews-scraper`) Actor Extract high-quality product reviews effortlessly with Project Cheerio Crawler Typescript. Features fast Cheerio-based scraping, structured JSON/CSV/Excel output, and customizable inputs for e-commerce sites. Includes sentiment analysis, topic extraction, and error-resilient crawling.... - **URL**: https://apify.com/getdataforme/patagonia-reviews-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** E-commerce, Lead generation, 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 ## Project Cheerio Crawler Typescript ### Introduction The Project Cheerio Crawler Typescript is a powerful Apify Actor designed to efficiently crawl and extract structured data from websites using Cheerio, a fast and lightweight library for parsing HTML. It specializes in scraping product reviews and related metadata, enabling users to gather valuable insights from e-commerce platforms like Patagonia. This Actor provides reliable, high-performance data extraction with minimal setup, making it ideal for businesses and researchers needing clean, actionable data. ### Features - **Fast and Efficient Crawling**: Utilizes CheerioCrawler for rapid HTML parsing and data extraction, handling up to hundreds of pages per minute. - **Structured Data Output**: Extracts detailed review information including ratings, comments, and custom fields in a consistent JSON format. - **Customizable Inputs**: Supports multiple start URLs and configurable request limits to tailor crawls to specific needs. - **Error-Resilient**: Built-in handling for common web scraping challenges like rate limits and dynamic content. - **Scalable and Reliable**: Optimized for Apify's cloud infrastructure, ensuring consistent performance across large-scale extractions. - **Data Quality Assurance**: Filters and structures raw HTML into clean, usable datasets with sentiment analysis and topic extraction. - **Easy Integration**: Outputs data in JSON, CSV, or Excel formats for seamless integration with analytics tools or databases. ### Input Parameters | Parameter | Type | Required | Description | Example | |------------|---------|----------|-----------------------------------------------------------------------------|---------| | startUrls | array | Yes | An array of URLs to start the crawl from. Supports multiple entry points. | `[{"url": "https://www.patagonia.com/product/mens-lightweight-synchilla-snap-t-fleece-pullover/25551.html?dwvar_25551_color=KALB"}]` | | maxItems | integer | No | Maximum number of requests the crawler can make. Default is 100. | `100` | ### Example Usage To run the Actor, provide input parameters in JSON format: ```json { "startUrls": [ { "url": "https://www.patagonia.com/product/mens-lightweight-synchilla-snap-t-fleece-pullover/25551.html?dwvar_25551_color=KALB" } ], "maxItems": 100 } ```` Example output in JSON format: ```json [ { "reviewId": 793942293, "productId": "23075", "productName": "W's Classic Retro-X® Jacket", "productImageUrl": "", "productPageUrl": "https://yotpo.com/go/335xOpWP", "isSyndicated": false, "syndicationSource": null, "rating": 4, "title": "Amanita is fab! Hoping for an XXL", "reviewBody": "This jacket in Amanita is great. Festive and Eye catching. However, the jacket runs very short. It would help a lot if you could spend the extra money to put in a two-way zipper. Then at least one could open it up at the bottom where it pooches out. It would also be far more flattering in a longer cut with side slits. If not for the great print, I would not have kept this in the XL jacket", "isRecommended": false, "isIncentivized": false, "verifiedBuyer": false, "submissionTime": "2025-12-26T08:28:42.000Z", "reviewerName": "Alida L.", "country": "US", "sentiment": 0.1531145, "votesUp": 19, "votesDown": 1, "customFields": { "Likelihood to Recommend": { "title": "Likelihood to Recommend", "fieldType": "SingleChoice", "value": "No" }, "Fit": { "title": "Fit", "fieldType": "Rating", "value": 1 }, "Height": { "title": "Height", "fieldType": "SingleChoice", "value": "5'10 - 6'0" }, "Activity": { "title": "Activity", "fieldType": "MultipleChoice", "value": "Casual Wear" }, "Size": { "title": "Size", "fieldType": "SingleChoice", "value": "XL" } }, "topics": { "print": "If not for the great print, I would not have kept this in the XL jacket", "jacket": "This jacket in Amanita is great.", "amanita": "Amanita is fab! Hoping for an XXL" }, "photos": [ { "id": "798324310", "url": "", "thumbUrl": "https://cdn-yotpo-images-production.yotpo.com/Review/793942293/798324310/square.jpg?1766738046" }, { "id": "798324314", "url": "", "thumbUrl": "https://cdn-yotpo-images-production.yotpo.com/Review/793942293/798324314/square.jpg?1766738049" } ], "comment": null, "language": "en" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather customer reviews to analyze trends, sentiment, and product performance. - **Competitive Intelligence**: Monitor competitor products and reviews to inform pricing and marketing strategies. - **Price Monitoring**: Extract review data alongside product details for comprehensive market insights. - **Content Aggregation**: Collect user-generated content for blogs, reports, or social media campaigns. - **Academic Research**: Study consumer behavior through large-scale review datasets. - **Business Automation**: Automate data collection for dashboards, alerting systems, or CRM integrations. ### Installation and Usage 1. Search for "Project Cheerio Crawler Typescript" 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 Actor outputs an array of JSON objects, each representing a product review. Key fields include: - `reviewId`: Unique identifier for the review. - `productId`, `productName`: Details about the reviewed product. - `rating`, `title`, `reviewBody`: Core review content and score. - `customFields`: Additional metadata like fit, size, and recommendations. - `topics`, `photos`: Extracted themes and image URLs. - `sentiment`: Numerical sentiment score. - `submissionTime`, `reviewerName`: Timestamp and author info. Data is structured for easy parsing and analysis. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** Extract high-quality product reviews effortlessly with Project Cheerio Crawler Typescript. Features fast Cheerio-based scraping, structured JSON output, and customizable inputs for e-commerce sites. Ideal for market research, competitive analysis, and content aggregation. Start extracting valuable insights today—try it free on Apify! (248 characters) # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## `maxItems` (type: `integer`): Maximum number of requests that can be made by this crawler. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.patagonia.com/product/mens-lightweight-synchilla-snap-t-fleece-pullover/25551.html?dwvar_25551_color=KALB" } ], "maxItems": 100 } ``` # Actor output Schema ## `results` (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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://www.patagonia.com/product/mens-lightweight-synchilla-snap-t-fleece-pullover/25551.html?dwvar_25551_color=KALB" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/patagonia-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [{ "url": "https://www.patagonia.com/product/mens-lightweight-synchilla-snap-t-fleece-pullover/25551.html?dwvar_25551_color=KALB" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/patagonia-reviews-scraper").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 '{ "startUrls": [ { "url": "https://www.patagonia.com/product/mens-lightweight-synchilla-snap-t-fleece-pullover/25551.html?dwvar_25551_color=KALB" } ] }' | apify call getdataforme/patagonia-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/patagonia-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Patagonia Reviews Scraper", "description": "Extract high-quality product reviews effortlessly with Project Cheerio Crawler Typescript. Features fast Cheerio-based scraping, structured JSON/CSV/Excel output, and customizable inputs for e-commerce sites. Includes sentiment analysis, topic extraction, and error-resilient crawling....", "version": "0.0", "x-build-id": "PqhzUnGmhob8DGj6Y" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~patagonia-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-patagonia-reviews-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~patagonia-reviews-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-patagonia-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~patagonia-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-patagonia-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "startUrls": { "title": "Start URLs", "type": "array", "description": "URLs to start with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItems": { "title": "Max Requests per Crawl", "type": "integer", "description": "Maximum number of requests that can be made by this crawler.", "default": 100 } } }, "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 } } } } } } } } } } ```