# Thenorthface Reviews Spider (`getdataforme/thenorthface-reviews-spider`) Actor This Apify Actor scrapes customer reviews from The North Face product pages, extracting structured data on ratings, sentiments, and user details.... - **URL**: https://apify.com/getdataforme/thenorthface-reviews-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **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 ### Description This Apify Actor scrapes customer reviews from The North Face product pages, extracting structured data on ratings, sentiments, and user details.... --- ## Thenorthface Reviews Spider ### Introduction The Thenorthface Reviews Spider is a powerful Apify Actor designed to scrape and extract customer reviews from The North Face product pages. It provides structured data on product feedback, ratings, and user sentiments, enabling businesses and researchers to gain valuable insights into consumer opinions. This tool streamlines the process of gathering authentic review data, saving time and effort compared to manual collection. ### Features - **Comprehensive Review Extraction**: Captures detailed review information including headlines, comments, ratings, and user details from The North Face website. - **High Data Accuracy**: Ensures reliable extraction of verified buyer status, helpful votes, and timestamps for trustworthy analysis. - **Flexible URL Input**: Accepts multiple product URLs for batch processing, allowing efficient scraping of various items. - **Structured JSON Output**: Delivers clean, machine-readable data perfect for integration into databases or analytics tools. - **Performance Optimized**: Built for speed and efficiency, handling large volumes of reviews without compromising on quality. - **Error-Resilient**: Includes built-in handling for common scraping challenges like rate limits and page variations. - **Apify Integration**: Seamlessly runs on the Apify platform with easy monitoring and export options. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | Urls | string | No | The URLs of The North Face product pages to scrape reviews from. Supports multiple URLs separated by newlines or commas. | https://www.thenorthface.com/en-us/p/womens/womens-footwear/womens-winter-boots-213481/womens-back-to-berkeley-iv-leather-waterproof-boots-NF0A8A9E?color=DQI | ### Example Usage #### Input Configuration ```json { "Urls": "https://www.thenorthface.com/en-us/p/womens/womens-footwear/womens-winter-boots-213481/womens-back-to-berkeley-iv-leather-waterproof-boots-NF0A8A9E?color=DQI" } ```` #### Output Example ```json [ { "product_url": "https://www.thenorthface.com/en-us/p/womens/womens-footwear/womens-winter-boots-213481/womens-back-to-berkeley-iv-leather-waterproof-boots-NF0A8A9E?color=DQI", "style_code": "NF0A8A9E", "review_id": 576643172, "headline": "Comfortable and warm", "comments": "Very comfortable and stylish. Keep my feet warm and dry.", "nickname": "4321", "location": "Undisclosed", "rating": 5, "helpful_votes": 0, "not_helpful_votes": 0, "is_verified_buyer": true, "is_verified_reviewer": true, "is_staff_reviewer": false, "bottom_line": "Yes", "created_date": 1774088646126, "updated_date": 1774088720990, "actor_id": "fO6gSJEn6SwQoPgO4", "run_id": "aWT0EawGJITkYPpvC" }, { "product_url": "https://www.thenorthface.com/en-us/p/womens/womens-footwear/womens-winter-boots-213481/womens-back-to-berkeley-iv-leather-waterproof-boots-NF0A8A9E?color=DQI", "style_code": "NF0A8A9E", "review_id": 575005360, "headline": "Runs VERY small", "comments": "Cute boots, but very small. My normal shoe size is an 8, and I usually size up to a 8.5 for hiking boots with thick socks. A 9 in these with thin socks felt more like a 7.5. Returning :(", "nickname": "LP", "location": "NY, NY", "rating": 4, "helpful_votes": 1, "not_helpful_votes": 0, "is_verified_buyer": false, "is_verified_reviewer": false, "is_staff_reviewer": false, "bottom_line": null, "created_date": 1772113961198, "updated_date": 1773952163122, "actor_id": "fO6gSJEn6SwQoPgO4", "run_id": "aWT0EawGJITkYPpvC" }, { "product_url": "https://www.thenorthface.com/en-us/p/womens/womens-footwear/womens-winter-boots-213481/womens-back-to-berkeley-iv-leather-waterproof-boots-NF0A8A9E?color=DQI", "style_code": "NF0A8A9E", "review_id": 571809940, "headline": "beautiful boots", "comments": "awesome boots, great style", "nickname": "4017", "location": "Undisclosed", "rating": 5, "helpful_votes": 0, "not_helpful_votes": 0, "is_verified_buyer": true, "is_verified_reviewer": true, "is_staff_reviewer": false, "bottom_line": "Yes", "created_date": 1767577051715, "updated_date": 1767577063574, "actor_id": "fO6gSJEn6SwQoPgO4", "run_id": "aWT0EawGJITkYPpvC" } ] ``` ### Use Cases - **Market Research**: Analyze customer sentiment and trends for The North Face products to inform marketing strategies. - **Competitive Intelligence**: Compare review data across similar outdoor gear brands to identify strengths and weaknesses. - **Price Monitoring**: Track how product ratings correlate with pricing and promotions. - **Content Aggregation**: Build databases of user-generated content for social media or blog posts. - **Academic Research**: Study consumer behavior in outdoor apparel through authentic review analysis. - **Business Automation**: Automate review monitoring for e-commerce platforms to respond to feedback quickly. ### Installation and Usage 1. Search for "Thenorthface 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 Actor outputs an array of JSON objects, each representing a single review. Key fields include: - `product_url`: The URL of the reviewed product. - `style_code`: Unique product identifier. - `review_id`: Unique review identifier. - `headline`: Review title. - `comments`: Full review text. - `nickname`: Reviewer's username. - `location`: Reviewer's location (may be "Undisclosed"). - `rating`: Numerical rating (1-5). - `helpful_votes` and `not_helpful_votes`: Community feedback counts. - `is_verified_buyer`, `is_verified_reviewer`, `is_staff_reviewer`: Boolean flags for reviewer status. - `bottom_line`: Overall recommendation. - `created_date` and `updated_date`: Timestamps in milliseconds. - `actor_id` and `run_id`: Apify-specific identifiers. ### Error Handling The Actor includes robust error handling for common issues like invalid URLs, network timeouts, or website changes. If a URL fails, it logs the error and continues with other URLs. Check the run logs for details on any failures. ### Rate Limiting and Best Practices Respect The North Face's terms of service and avoid overloading their servers. The Actor implements built-in delays to prevent rate limiting. For best results, limit to 10-20 URLs per run and space out executions. Use the data ethically and comply with data privacy laws. ### Limitations - Only scrapes reviews from The North Face product pages; does not extract other site content. - May not capture reviews behind login walls or those requiring additional interactions. - Output is limited to available review data; some fields may be null if not provided by the site. ### 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! # Actor input Schema ## `Urls` (type: `string`): The urls for the spider. ## Actor input object example ```json {} ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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/thenorthface-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/thenorthface-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/thenorthface-reviews-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/thenorthface-reviews-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Thenorthface Reviews Spider", "description": "This Apify Actor scrapes customer reviews from The North Face product pages, extracting structured data on ratings, sentiments, and user details....", "version": "0.0", "x-build-id": "YPXyW4ZGJclbJso5s" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~thenorthface-reviews-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-thenorthface-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~thenorthface-reviews-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-thenorthface-reviews-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~thenorthface-reviews-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-thenorthface-reviews-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Urls": { "title": "Urls", "pattern": "^https?://.+", "type": "string", "description": "The urls for the spider." } } }, "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 } } } } } } } } } } ```