Extract, analyze, and monitor Amazon customer reviews at scale. Our Amazon Reviews API returns structured JSON with full review text, star ratings, verified purchase status, helpful vote counts, and reviewer metadata — with a single HTTP request.
Whether you need to scrape Amazon reviews for sentiment analysis, build a competitive intelligence tool, feed a product analytics dashboard, or train an LLM on real customer feedback — DataCloud's Amazon review scraper API handles anti-bot protection, rotating proxies, and CAPTCHA bypassing automatically. You get clean data, every time.
200 free credits — no credit card required.
The amazon customer reviews API designed for developers. No selenium, no CAPTCHA solver, no proxy management — just clean, structured data.
Rating, title, body, date, verified purchase status, reviewer ID, helpful votes — every field in one JSON call.
Filter by star rating (1–5 stars), sort by recency or helpfulness, and restrict to verified purchases only.
Scrape Amazon customer reviews from 15+ international marketplaces: US, UK, DE, JP, IN, CA, AU and more.
Get full star-rating distribution and aggregate average rating — essential for review analysis and sentiment tracking.
Structured JSON output ready to export to CSV, Excel, Google Sheets, or any database with zero transformation.
Our infrastructure handles anti-scraping measures, rotating proxies, and CAPTCHA solving so your requests always succeed.
Access image URLs attached to customer reviews — crucial for product quality research and visual sentiment analysis.
Paginate through thousands of reviews with the page parameter. Extract the entire review history for any product.
Feed thousands of real Amazon customer reviews into LLMs, sentiment classifiers, and NLP pipelines. Analyze product satisfaction, identify recurring complaints, and extract feature-level sentiment at scale with our amazon review extractor.
Monitor competitor product reviews in real-time. Detect rating changes, spot emerging negative trends, and benchmark your product's average rating against alternatives — all via the amazon product reviews API.
Scrape Amazon reviews in bulk to understand consumer preferences, identify underserved needs, and validate product ideas. Export to CSV, Google Sheets, or any BI tool using our structured JSON output.
Build smarter repricing and listing optimization tools that factor in review velocity and rating trends. Access scraping amazon customer reviews data to determine product quality signals.
Get alerts when new negative reviews appear. Track review volume over time. Filter by verified purchases only to focus on authentic buyer feedback. Our amazon reviews scraper api supports real-time monitoring use cases.
Collect large-scale labeled Amazon review datasets for training recommendation systems, star-rating predictors, and review quality classifiers. The API returns all fields needed to build high-quality supervised learning datasets.
Our amazon review scraping API is as simple as a single GET request. No headless browser, no selenium, no amazon captcha solver needed — we handle all of that infrastructure for you.
import requests, json
token = "<YOUR-DATACLOUD-TOKEN>"
asin = "B0C7BKZ883" # Amazon ASIN
page = 1
star_rating = "all" # "one_star" … "five_star"
sort_by = "recent" # "helpful" | "recent"
verified = True
url = (
"https://api.datacloud.sh/amazon/reviews"
f"?token={token}&asin={asin}&page={page}"
f"&star_rating={star_rating}&sort_by={sort_by}"
f"&verified_purchases={str(verified).lower()}"
)
res = requests.get(url)
print(json.dumps(res.json(), indent=2))Our amazon reviews extractor returns every available field from the Amazon reviews page — no manual parsing required.
Everything about scraping Amazon reviews, the amazon customer reviews API, and our data extraction capabilities.
Join thousands of developers using DataCloud's amazon reviews scraper api to power sentiment analysis, competitive intelligence, and market research tools.
DataCloud's Amazon Scraper API covers every data extraction use case. Explore specialized endpoints below.