# Amazon Product Reviews Spider (`getdataforme/amazon-product-reviews-spider`) Actor Extract comprehensive Amazon product reviews effortlessly with this Apify Actor. Gain valuable insights into customer sentiment, ratings, and feedback for market research, competitive analysis, and sentiment tracking.... - **URL**: https://apify.com/getdataforme/amazon-product-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 Extract comprehensive Amazon product reviews effortlessly with this Apify Actor. Gain valuable insights into customer sentiment, ratings, and feedback for market research, competitive analysis, and sentiment tracking.... --- ## Amazon Product Reviews Spider ### Introduction The Amazon Product Reviews Spider is a powerful Apify Actor designed to extract detailed customer reviews from Amazon product pages. It provides comprehensive data on user feedback, ratings, and profiles, enabling businesses and researchers to gain valuable insights into product performance and customer sentiment. This tool streamlines the process of gathering authentic reviews for analysis, competitive intelligence, or content aggregation. ### Features - **Comprehensive Review Extraction**: Captures full review text, ratings, dates, and reviewer profiles from Amazon product pages. - **High Reliability**: Built with robust error handling to ensure consistent data retrieval even from dynamic web pages. - **Fast and Scalable**: Optimized for speed, handling multiple URLs efficiently without compromising data quality. - **Structured JSON Output**: Delivers clean, machine-readable data ready for integration into databases or analytics tools. - **Flexible Input**: Accepts multiple Amazon product URLs for batch processing. - **No Coding Required**: User-friendly interface for easy configuration and execution on the Apify platform. - **Ethical Scraping**: Adheres to Amazon's terms of service and respects rate limits to avoid disruptions. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Urls | array | Yes | A list of Amazon product page URLs to scrape reviews from. Each URL must be a valid HTTP/HTTPS link to an Amazon product page. | ["https://www.amazon.com/Octa-Core-Capable-Processor-GeForce-ANV16-41-R5J0/dp/B0CXY573W9/ref=sr_1_1?crid=2DD2B6XRVQC89&dib=eyJ2IjoiMSJ9.FT9NoLUdDmhLCPw6GyazrVxXQeH0mIVNx1iEaATP8n4DxM4dogeI87GrX8BCUccz6hYTTqmz7dm8w5vRJsQqG2FS-yQuAN4lvvepEuzTzSHZ3nTVPexb1iWsLjTJYlG1zEadC3sW--DkR-ZIW5e6MNn9n1xCDUcVthyODSw928L9brgbN7B27Avjd77uqXiynFd3COBHZrMmO_gGMnO0yUSpdfyRnfoFkrG_QroK4uw.j9FPrLSf-4P0p8rSDuno6fiQ6VDfNlrp_0XiPKwWm3A&dib_tag=se&keywords=acer%2Bnitro%2Bv&qid=1732438203&sprefix=%2Caps%2C411&sr=8-1&th=1"] | ### Example Usage To run the Actor, provide input in JSON format. Here's an example input: ```json { "Urls": [ "https://www.amazon.com/Octa-Core-Capable-Processor-GeForce-ANV16-41-R5J0/dp/B0CXY573W9/ref=sr_1_1?crid=2DD2B6XRVQC89&dib=eyJ2IjoiMSJ9.FT9NoLUdDmhLCPw6GyazrVxXQeH0mIVNx1iEaATP8n4DxM4dogeI87GrX8BCUccz6hYTTqmz7dm8w5vRJsQqG2FS-yQuAN4lvvepEuzTzSHZ3nTVPexb1iWsLjTJYlG1zEadC3sW--DkR-ZIW5e6MNn9n1xCDUcVthyODSw928L9brgbN7B27Avjd77uqXiynFd3COBHZrMmO_gGMnO0yUSpdfyRnfoFkrG_QroK4uw.j9FPrLSf-4P0p8rSDuno6fiQ6VDfNlrp_0XiPKwWm3A&dib_tag=se&keywords=acer%2Bnitro%2Bv&qid=1732438203&sprefix=%2Caps%2C411&sr=8-1&th=1" ] } ```` The output will be an array of JSON objects, each representing a review. Example output: ```json [ { "profile_name": "Adrianna Powers", "profile_url": "/gp/profile/amzn1.account.AFJR34VBPJW7BGQV3Q5MV2QIGGXA", "review_text": "\"This gaming laptop is a beast! The graphics are incredible, and it runs all my favorite games smoothly. The display is vibrant, and the keyboard feels great. It\u2019s very easy to use. Sound quality is great. Plus, it's surprisingly portable. Definitely the best gaming laptop I've ever owned!\"Read more", "rating": "5.0 out of 5 stars", "review_date": "Reviewed in the United States on November 5, 2025", "actor_id": "xfNVuEo086y5eUKlB", "run_id": "A3IxL2Ytp5DjSVQe6" }, { "profile_name": "Brandon Keener", "profile_url": "/gp/profile/amzn1.account.AGUXWEU3XF24Z2DINT2YO3AVHBUQ", "review_text": "10/10 works great with school and my cybersecurity degree as well as gaming aspect GTA Modding, Call of Duty, and Fortnite 10/10Recommend Great specifications I absolutely love it works great for me.Read more", "rating": "5.0 out of 5 stars", "review_date": "Reviewed in the United States on February 18, 2026", "actor_id": "xfNVuEo086y5eUKlB", "run_id": "A3IxL2Ytp5DjSVQe6" }, { "profile_name": "Her Storied Ways", "profile_url": "/gp/profile/amzn1.account.AF7IZOQLKEREGQN4MSZIUYVXPEFA", "review_text": "\n The media could not be loaded.\n Not the biggest fan of laptops, but with this being my second one, it's impressive for what I use it for (writing and gaming). Battery life is optimal, and of course it depends on what you are doing, but it is decent compared to my old laptop. It does have some weight to it, but nothing I can't handle even while traveling with it. Starts up quickly and I like the way the keys light up when typing.Gaming- It handles my games just fine (Farmville2, SimCity4, Sims4), which, in my opinion, are very graphic intensive games. If you use the keyboard for your games (wasd), they are made to stand out among the other keys, which is nice.It does catch dust and finger prints easily BUT Wipes down nicely to dust and clean. Not disappointed in this product at all. Check out my short video so you can look at it in real time.Read more", "rating": "5.0 out of 5 stars", "review_date": "Reviewed in the United States on March 3, 2025", "actor_id": "xfNVuEo086y5eUKlB", "run_id": "A3IxL2Ytp5DjSVQe6" } ] ``` ### Use Cases - **Market Research**: Analyze customer feedback to identify trends, preferences, and pain points in product categories. - **Competitive Intelligence**: Compare reviews across similar products to benchmark performance and pricing strategies. - **Sentiment Analysis**: Feed review data into NLP tools to gauge overall customer satisfaction and brand perception. - **Content Aggregation**: Collect reviews for blogs, newsletters, or social media content to engage audiences. - **Academic Research**: Gather data for studies on consumer behavior, product reviews, and e-commerce trends. - **Business Automation**: Automate monitoring of product reviews for quality control or reputation management. ### Installation and Usage 1. Search for "Amazon Product 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 containing details from a single review. Key fields include: - `profile_name`: The reviewer's name. - `profile_url`: Link to the reviewer's Amazon profile. - `review_text`: The full text of the review. - `rating`: The star rating given by the reviewer. - `review_date`: The date and location of the review. - `actor_id` and `run_id`: Metadata for tracking the extraction run. This structured format ensures easy parsing and integration with other tools. ### 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: `array`): The urls for the spider. ## Actor input object example ```json { "Urls": [ "https://www.amazon.com/Octa-Core-Capable-Processor-GeForce-ANV16-41-R5J0/dp/B0CXY573W9/ref=sr_1_1?crid=2DD2B6XRVQC89&dib=eyJ2IjoiMSJ9.FT9NoLUdDmhLCPw6GyazrVxXQeH0mIVNx1iEaATP8n4DxM4dogeI87GrX8BCUccz6hYTTqmz7dm8w5vRJsQqG2FS-yQuAN4lvvepEuzTzSHZ3nTVPexb1iWsLjTJYlG1zEadC3sW--DkR-ZIW5e6MNn9n1xCDUcVthyODSw928L9brgbN7B27Avjd77uqXiynFd3COBHZrMmO_gGMnO0yUSpdfyRnfoFkrG_QroK4uw.j9FPrLSf-4P0p8rSDuno6fiQ6VDfNlrp_0XiPKwWm3A&dib_tag=se&keywords=acer%2Bnitro%2Bv&qid=1732438203&sprefix=%2Caps%2C411&sr=8-1&th=1" ] } ``` # 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/amazon-product-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/amazon-product-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/amazon-product-reviews-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/amazon-product-reviews-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Product Reviews Spider", "description": "Extract comprehensive Amazon product reviews effortlessly with this Apify Actor. Gain valuable insights into customer sentiment, ratings, and feedback for market research, competitive analysis, and sentiment tracking....", "version": "0.0", "x-build-id": "hbNMRnzTwET6NLBlE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~amazon-product-reviews-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-amazon-product-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~amazon-product-reviews-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-amazon-product-reviews-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~amazon-product-reviews-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-amazon-product-reviews-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://www.amazon.com/Octa-Core-Capable-Processor-GeForce-ANV16-41-R5J0/dp/B0CXY573W9/ref=sr_1_1?crid=2DD2B6XRVQC89&dib=eyJ2IjoiMSJ9.FT9NoLUdDmhLCPw6GyazrVxXQeH0mIVNx1iEaATP8n4DxM4dogeI87GrX8BCUccz6hYTTqmz7dm8w5vRJsQqG2FS-yQuAN4lvvepEuzTzSHZ3nTVPexb1iWsLjTJYlG1zEadC3sW--DkR-ZIW5e6MNn9n1xCDUcVthyODSw928L9brgbN7B27Avjd77uqXiynFd3COBHZrMmO_gGMnO0yUSpdfyRnfoFkrG_QroK4uw.j9FPrLSf-4P0p8rSDuno6fiQ6VDfNlrp_0XiPKwWm3A&dib_tag=se&keywords=acer%2Bnitro%2Bv&qid=1732438203&sprefix=%2Caps%2C411&sr=8-1&th=1" ], "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 } } } } } } } } } } ```