🛒 Amazon Reviews Scraper — Ratings & Sentiment

Amazon Reviews Scraper is an Apify Actor for extracting Amazon product reviews from a product URL or ASIN. Each dataset item represents one review and includes the ASIN, product title, product URL, review ID, reviewer name and profile URL, star rating, review title, review body, verified purchase status, helpful votes, images, variant, country, top-review flag, and scrape timestamp. The Actor is usable through Apify MCP and through the Apify API, and it returns one record per review for analysis, monitoring, enrichment, and AI workflows.

Best fit and connected workflows

This Actor fits workflows where the review text on an Amazon product page is the source record.

Use it when you want to:

• collect the top customer reviews shown on an Amazon product page,
• combine verified purchase status with star ratings and review text,
• analyze review body text for sentiment, themes, or product feedback,
• start from Amazon product discovery and move into review-level analysis,
• connect product lookup or price tracking with downstream review monitoring.

Related workflows that connect naturally:

• Use Amazon Product Search Scraper when you first need to discover Amazon records, then pass a verified public product URL or ID into this Actor for review extraction.
• Use Amazon Search Scraper when search results are the entry point and you want to continue into review data collection.
• Use Amazon Competitor Price Tracker when you begin with the parent Amazon product record and then collect related engagement data here.

Practical scenario

Maya is a product manager reviewing a new headset listing. She already has the Amazon product URL and sends it to this Actor with a review count limit. The dataset returns the ASIN, product title, review title, review body, rating, verified purchase flag, helpful votes, review date, country, variant, images, and source URL. Maya uses the review body and helpful votes to spot repeated feedback, then checks the verified purchase status and review date to decide which comments go into her weekly product report.

Input fields

Field	Type	Description
productUrls	array	One or more Amazon product page URLs. /dp/ASIN and /gp/product/ASIN forms are accepted. The ASIN is extracted from the URL.
asins	array	One or more 10-character Amazon product IDs when you already have the ASINs.
maxReviews	integer	Maximum reviews to collect per product. The schema range is 1 to 10.
reviewsCutoffDate	string	Date cutoff in YYYY-MM-DD format. Reviews older than this date are filtered after scraping.
proxyConfiguration	object	Proxy settings. The default uses Apify Residential proxies.

Input example

{
  "productUrls": [
    {
      "url": "https://www.amazon.com/dp/B0CWXNS552"
    }
  ],
  "maxReviews": 10,
  "reviewsCutoffDate": "2024-01-01",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}

Output fields

Field	Type	Description
asin	string	Amazon Standard Identification Number.
product_title	string | null	Product title shown on the Amazon page.
product_url	string	Direct Amazon product URL.
review_id	string	Amazon review identifier used for deduplication.
reviewer_name	string | null	Reviewer display name.
reviewer_profile_url	string | null	Reviewer public profile URL.
is_verified_purchase	boolean	Verified purchase status.
rating	number | null	Star rating from 1.0 to 5.0.
review_title	string | null	Review headline.
review_body	string | null	Full review text.
review_date	string | null	Raw Amazon date string.
review_date_iso	string | null	ISO 8601 parsed date for filtering and sorting.
helpful_votes	number	Helpful vote count.
images	array	Image URLs attached to the review.
variant	string | null	Product variant reviewed, such as color or size.
country	string | null	Country extracted from the review date string.
is_top_review	boolean	Amazon top-review flag.
scraped_at	string	ISO 8601 scrape timestamp.
source_url	string	Amazon product page URL used as the source.

Output example

{
  "asin": "B0CWXNS552",
  "product_title": "Apple AirTag (1st Generation)",
  "product_url": "https://www.amazon.com/dp/B0CWXNS552",
  "review_id": "R2STPNKIY4BPVY",
  "reviewer_name": "John D.",
  "reviewer_profile_url": "https://www.amazon.com/gp/profile/amzn1.account.XXXX",
  "is_verified_purchase": true,
  "rating": 4,
  "review_title": "Great product, fast shipping",
  "review_body": "I've had this for 3 months. Setup was instant and tracking is accurate...",
  "review_date": "Reviewed in the United States on January 5, 2024",
  "review_date_iso": "2024-01-05T00:00:00.000Z",
  "helpful_votes": 42,
  "images": [
    "https://m.media-amazon.com/images/I/XXX._SL1500_.jpg"
  ],
  "variant": "Color: Black | Size: 256GB",
  "country": "the United States",
  "is_top_review": false,
  "scraped_at": "2026-04-09T14:30:00.000Z",
  "source_url": "https://www.amazon.com/dp/B0CWXNS552"
}

How it works

The Actor accepts Amazon product URLs or ASINs and resolves them to product-page review scraping. The input schema supports collecting up to 10 inline reviews per product, which matches the top customer reviews shown on the product page. Reviews can be filtered after scraping by reviewsCutoffDate. The default proxy configuration uses Apify Residential proxies, which are selected in the input schema. The dataset view surfaces rating, verification status, dates, helpful votes, images, and product links for review workflows.

Pricing

This Actor uses Pay per event pricing. The primary charged event is Review scraped, and each event is charged once for each Amazon product review successfully extracted and saved to the dataset. Apify platform usage is charged separately, so your total run cost includes both event charges and platform usage.

For the current pricing details, open the live Pricing tab on the Actor page.

Example in words: if a run saves ten reviews, the review event is charged ten times, plus the Apify platform usage for that run.

Use with AI agents (MCP)

This Actor is usable through Apify MCP and exposes a review extraction tool for Amazon product pages and ASIN-based inputs. The exact Actor identity is khadinakbar/amazon-reviews-scraper.

Extract Amazon product reviews for this ASIN and return review text, rating, verified purchase status, helpful votes, reviewer info, images, and variant.

Output interpretation:

• review_body is the primary text field for sentiment analysis, theme extraction, and summarization.
• rating and is_verified_purchase support separating review sentiment from purchase signals.
• helpful_votes, is_top_review, and review_date_iso support prioritization and time-based analysis.
• source_url and product_url provide provenance for each record.
• results points to the dataset items endpoint, while runOutput and runSummary support automation and agent-side status checks.

Scope, pagination, and cost guidance:

• The input schema is designed for Amazon product-page reviews and accepts product URLs or ASINs.
• maxReviews controls how many inline reviews are collected per product, within the schema range from 1 to 10.
• reviewsCutoffDate applies after scraping, which makes it useful for recent-review workflows.
• Each saved review triggers the Review scraped charged event, and Apify platform usage applies in addition to the event charges.
• Check the live Pricing tab for the current pricing view.

Apify API example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor('khadinakbar/amazon-reviews-scraper').call({
  productUrls: [
    { url: 'https://www.amazon.com/dp/B0CWXNS552' }
  ],
  maxReviews: 10,
  reviewsCutoffDate: '2024-01-01',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Fetched ${items.length} review records`);
console.log(items[0]);

Best results and outcome guidance

Use a product URL or ASIN that points to the exact Amazon product page you want reviewed. If you already have the product ID, asins is a direct path into the same review dataset. Set maxReviews to the number of inline reviews you want to collect for that product, within the schema range. When you are tracking newer customer feedback, pair the product input with reviewsCutoffDate so the returned dataset reflects the date window you need. The returned review_body, rating, is_verified_purchase, helpful_votes, and review_date_iso fields form the most useful baseline for review analysis.

Continue the workflow

• Then use Amazon Search Scraper to extend 🛒 Amazon Reviews Scraper — Ratings & Sentiment research with a complementary discovery contract.
• Then use Amazon Seller Email Scraper to extend 🛒 Amazon Reviews Scraper — Ratings & Sentiment research with a complementary enrichment contract.

Design note

I found that the dataset contract marks asin, product_url, review_id, is_verified_purchase, helpful_votes, images, is_top_review, scraped_at, and source_url as required, which keeps provenance and review identity present on every saved item.

FAQ

How should I choose between product URLs and ASINs?

Use product URLs when you are starting from a product page, and use ASINs when you already have the Amazon product ID. Both paths lead to the same review dataset.

When does Amazon Product Search Scraper fit into the workflow?

Use Amazon Product Search Scraper when you need to discover Amazon products first, then pass the verified public URL or ID into this Actor for review extraction.

When does Amazon Search Scraper fit into the workflow?

Use Amazon Search Scraper when search results are the starting point and you want to continue into review data collection.

When does Amazon Competitor Price Tracker fit into the workflow?

Use Amazon Competitor Price Tracker when you want to begin with a product record and then collect related review data from that product page.

Which fields are most useful for sentiment analysis?

review_body, rating, is_verified_purchase, helpful_votes, review_title, and review_date_iso are the core fields for review analytics and downstream AI workflows.

Where is the resulting data stored?

Review records are written to the default dataset, and the output contract also exposes run summary and run output records in the key-value store for automation workflows.

Responsible use

Use this Actor in line with applicable laws, Amazon's terms, and your internal data handling practices. Review records may include reviewer names, profile links, and other public review metadata, so handle the data with appropriate privacy and governance controls.