Amazon Reviews Extractor
Pay $0.30 for 1,000 reviews
Amazon Reviews Extractor
Pay $0.30 for 1,000 reviews
Scrape up to 500 Amazon reviews per product! Filter by stars, keywords, media & verified purchases. Extract global reviews (20+ regions). Export JSON/CSV. Perfect for market research & SEO.
You can access the Amazon Reviews Extractor programmatically from your own Python applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9 "products": [
10 "https://www.amazon.com/Logitech-LIGHTSPEED-Wireless-Gaming-Mouse/product-reviews/B07CMS5Q6P/ref=cm_cr_getr_mb_paging_btm_2?ie=UTF8&reviewerType=all_reviews&pageNumber=2&formatType=current_format",
11 "https://www.amazon.fr/-/en/dp/B000GAWSDG/ref=cm_cr_arp_mb_bdcrb_top?ie=UTF8&th=1&psc=1",
12 "B07MVJZQTC",
13 ],
14 "proxySettings": { "useApifyProxy": True },
15}
16
17# Run the Actor and wait for it to finish
18run = client.actor("web_wanderer/amazon-reviews-extractor").call(run_input=run_input)
19
20# Fetch and print Actor results from the run's dataset (if there are any)
21print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
22for item in client.dataset(run["defaultDatasetId"]).iterate_items():
23 print(item)
24
25# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-startAmazon Review Scraper - Fast & Reliable API in Python
The Apify API client for Python is the official library that allows you to use Amazon Reviews Extractor API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
pip install apify-clientOther API clients include:
WebWanderActor Metrics
11 monthly users
-
1 bookmark
>99% runs succeeded
Created in Feb 2025
Modified 4 days ago
