# Sweatybetty Reviews Scraper (`getdataforme/sweatybetty-reviews-scraper`) Actor Effortlessly scrape detailed customer reviews from Sweaty Betty's activewear products with this Apify Actor. Extract ratings, comments, and metadata for market research and sentiment analysis. Features include reliable crawling, customizable limits, and structured JSON output.... - **URL**: https://apify.com/getdataforme/sweatybetty-reviews-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** E-commerce, Lead generation, Agents - **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 ## Sweatybetty Reviews Scraper ### Introduction The Sweatybetty Reviews Scraper is a powerful Apify Actor designed to extract customer reviews from Sweaty Betty's website, a popular retailer for activewear and sportswear. It automates the collection of detailed review data, including ratings, comments, and metadata, enabling users to gather insights into product performance and customer sentiment. This tool is ideal for businesses, researchers, and analysts seeking reliable data for market analysis without manual scraping. ### Features - **Comprehensive Review Extraction**: Scrapes full review details such as ratings, titles, bodies, reviewer info, and timestamps from product pages. - **High Reliability**: Built on Apify's robust CheerioCrawler, ensuring efficient and error-resistant data collection. - **Customizable Limits**: Allows setting maximum requests to control crawl depth and avoid overloading servers. - **Structured Output**: Delivers clean, JSON-formatted data ready for analysis or integration into databases. - **Fast Performance**: Processes multiple URLs concurrently for quick results on large product catalogs. - **No Coding Required**: User-friendly interface for easy setup and execution directly in the Apify platform. - **Scalable**: Handles varying loads, from single products to extensive inventories. ### Input Parameters | Parameter | Type | Required | Description | Example | |------------|---------|----------|-----------------------------------------------------------------------------|---------| | startUrls | array | Yes | List of URLs to start crawling from, typically product pages on Sweaty Betty. | [{"url": "https://www.sweatybetty.com/shop/bottoms/leggings/winter-power-7-8-leggings-SB10249_NoriGreen_78Length.html"}] | | maxItems | integer | No | Maximum number of requests the crawler can make during the run. Default is 100. | 50 | ### Example Usage To run the actor, provide input parameters in JSON format: ```json { "startUrls": [ { "url": "https://www.sweatybetty.com/shop/bottoms/leggings/winter-power-7-8-leggings-SB10249_NoriGreen_78Length.html?dwvar_SB10249_NoriGreen_78Length_color=norigreen&oldpid=SB10249_NoriGreen_78Length" } ], "maxItems": 100 } ```` Example output: ```json [ { "reviewId": "180661987", "productId": "195022910090", "productName": "Pro Run Zip Up", "rating": 4, "title": "Ultra soft", "reviewBody": "The fabric is really ultra soft. I love the snug! \nBut the hole for the smart watch is placed weirdly, also hood is too big. Overall an excellent sale catch but I wouldn't pay the full price. I'm also afraid the zipper won't last long at the bottom.", "contentLocale": "en_GB", "reviewerName": "", "userLocation": null, "isRecommended": null, "isSyndicated": false, "helpfulCount": 0, "unhelpfulCount": 0, "totalFeedbackCount": 0, "moderationStatus": "APPROVED", "submissionTime": "2026-03-29T09:12:50.000+00:00", "lastModifiedTime": "2026-03-29T09:30:11.000+00:00", "contextData": {}, "secondaryRatings": {}, "photos": [], "hasClientResponse": false } ] ``` ### Use Cases - **Market Research**: Analyze customer feedback to identify trends in product quality and preferences for activewear. - **Competitive Intelligence**: Compare reviews across similar products from Sweaty Betty and competitors to inform pricing and marketing strategies. - **Price Monitoring**: Track sentiment alongside pricing data to optimize sales and promotions. - **Content Aggregation**: Collect reviews for content creation, such as blog posts or social media insights on fitness gear. - **Academic Research**: Study consumer behavior in the sportswear industry using real-world review data. - **Business Automation**: Integrate scraped data into dashboards for automated reporting on product performance. ### Installation and Usage 1. Search for "Sweatybetty Reviews Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters (e.g., add start URLs and set maxItems). 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: - `reviewId`: Unique identifier for the review. - `productId` and `productName`: Details of the reviewed product. - `rating`: Numerical rating (e.g., 1-5). - `title` and `reviewBody`: Review content. - `reviewerName`, `userLocation`: Anonymized reviewer info. - `submissionTime`, `lastModifiedTime`: Timestamps. - Additional metadata like `helpfulCount`, `moderationStatus`, and `photos` for comprehensive 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! *** **Concise Description (148 words):**\ Unlock valuable customer insights with the Sweatybetty Reviews Scraper! This Apify Actor effortlessly extracts detailed reviews from Sweaty Betty's activewear products, including ratings, comments, and metadata. Key features: high-reliability crawling, customizable request limits, and structured JSON output. Perfect for market research, competitive analysis, and business automation. Get started today and transform raw data into actionable intelligence. Try it free on Apify! # 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.sweatybetty.com/shop/bottoms/leggings/winter-power-7-8-leggings-SB10249_NoriGreen_78Length.html?dwvar_SB10249_NoriGreen_78Length_color=norigreen&oldpid=SB10249_NoriGreen_78Length" } ], "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.sweatybetty.com/shop/bottoms/leggings/winter-power-7-8-leggings-SB10249_NoriGreen_78Length.html?dwvar_SB10249_NoriGreen_78Length_color=norigreen&oldpid=SB10249_NoriGreen_78Length" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/sweatybetty-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.sweatybetty.com/shop/bottoms/leggings/winter-power-7-8-leggings-SB10249_NoriGreen_78Length.html?dwvar_SB10249_NoriGreen_78Length_color=norigreen&oldpid=SB10249_NoriGreen_78Length" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/sweatybetty-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.sweatybetty.com/shop/bottoms/leggings/winter-power-7-8-leggings-SB10249_NoriGreen_78Length.html?dwvar_SB10249_NoriGreen_78Length_color=norigreen&oldpid=SB10249_NoriGreen_78Length" } ] }' | apify call getdataforme/sweatybetty-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/sweatybetty-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sweatybetty Reviews Scraper", "description": "Effortlessly scrape detailed customer reviews from Sweaty Betty's activewear products with this Apify Actor. Extract ratings, comments, and metadata for market research and sentiment analysis. Features include reliable crawling, customizable limits, and structured JSON output....", "version": "0.0", "x-build-id": "FrIfLM9fBvjF6sseM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~sweatybetty-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-sweatybetty-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~sweatybetty-reviews-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-sweatybetty-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~sweatybetty-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-sweatybetty-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```