Walmart Reviews Scraper

Scrape Walmart product reviews from $0.60 / 1,000 results, and pull thousands of reviews per product in one run. Get reviewer name, star rating, review title, text, date, helpful votes and verified-purchase status as clean rows ready for CSV, Excel, JSON or the API. No HTML parsing, no maintenance - you pay only per review returned.

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Why this Actor?

Capability	Data Forge Walmart Reviews Scraper	Other review scrapers
Star-rating filter (1-5)	Yes - isolate any single 1 to 5 star tier	No filter, mixed dump
Verified-purchase-only filter	Yes - keep confirmed buyers only	Not available
Sort control	5 modes: newest first, oldest first, highest rating, lowest rating, most relevant	Unsorted dump
Fields per review row	9 fields: text, date, verified flag, rating, title, author, helpful votes, negative votes, review ID	Rating and text only
Depth per product	Up to 1,000 reviews, paginated 20 per page	No depth control
Pricing model	Pay per review returned, $0.60 / 1,000	Per-run or subscription pricing

What does the Walmart Reviews Scraper do?

The Walmart Reviews Scraper turns any product on Walmart.com into a structured review dataset. Feed it one or more Walmart product IDs (catalog id or numeric item id) or product URLs, and it returns each review as a flat row: who wrote it, the star rating, the review headline and body, the date, how many shoppers found it helpful, and whether it was a Verified Purchase. Sort by newest, oldest, rating or relevance, narrow to a single star rating, keep only verified buyers, and paginate to thousands of reviews per product. The first paragraph says it best - this is the fast, low-cost way to pull Walmart reviews at scale without touching a browser.

What data can you get from a Walmart review?

Each review row carries the fields buyers ask for:

• 👤 Author - the reviewer's display name
• ⭐ Rating - star rating from 1 to 5
• 📛 Title - the review headline
• 📝 Text - the written review body
• 📅 Date - when the review was submitted
• ✅ Verified purchase - true when the reviewer is a confirmed buyer
• 👍 Helpful count - how many shoppers marked it helpful
• 👎 Negative feedback - how many marked it unhelpful
• 🆔 Review ID - a stable identifier for deduping

The complete raw review object is also attached under data, so no field is ever lost.

Input modes

Five ready-to-run recipes. Each maps 1:1 to a published example task - swap in your own Walmart product IDs and press Start. Every mode below stays at 100 reviews, so a full test run costs about $0.06.

1. Build a sentiment dataset

Data teams: pull a broad review sample for one product to train or score a sentiment model.

{
  "productIds": ["18533160127"],
  "maxReviewsPerProduct": 100
}

2. Mine 1-star complaints

Product and CX teams: isolate the angriest reviews to surface defects, sizing issues and return drivers.

{
  "productIds": ["18533160127"],
  "rating": "1",
  "maxReviewsPerProduct": 100
}

3. Keep verified buyers only

Researchers: drop unverified noise and keep only reviews carrying the Verified Purchase badge.

{
  "productIds": ["18533160127"],
  "verifiedOnly": true,
  "maxReviewsPerProduct": 100
}

4. Monitor newest reviews first

Brand monitoring: pull the freshest reviews first to catch new complaints the day they land.

{
  "productIds": ["18533160127"],
  "sort": "submission-desc",
  "maxReviewsPerProduct": 100
}

5. Collect 5-star social proof

Marketing: gather top-rated reviews for landing pages, ads and social-proof widgets.

{
  "productIds": ["18533160127"],
  "rating": "5",
  "maxReviewsPerProduct": 100
}

How to scrape Walmart reviews

1. Open the Walmart Reviews Scraper and click Try for free. The input form is prefilled with a working example product, so a first run returns real reviews with zero setup.
2. In Products, paste one or more product IDs (19075520026) or product/reviews URLs. Mix both freely.
3. Pick a Sort order - newest, oldest, highest rating, lowest rating or most relevant - and optionally set a Star rating filter to keep only 1 to 5 star reviews.
4. Set Max reviews per product to control depth and cost, and toggle Verified purchases only when you want confirmed buyers.
5. Click Start, then export the results to CSV, Excel, JSON or the API.

Input example

{
  "productIds": ["19075520026", "https://www.walmart.com/ip/967557625"],
  "sort": "submission-desc",
  "maxReviewsPerProduct": 500,
  "rating": "5",
  "verifiedOnly": true
}

Output example

{
  "query": "19075520026",
  "row_type": "review",
  "review_id": "a1b2c3d4e5f6",
  "author": "Jamie R.",
  "rating": 5,
  "title": "Works great, crisps fast",
  "text": "Crispy results, heats fast, and cleanup takes a minute. Best kitchen buy this year.",
  "date": "2026-05-22",
  "verified_purchase": true,
  "helpful_count": 47,
  "negative_feedback": 2,
  "data": { "...": "complete review object" }
}

Each run also writes a summary record to the OUTPUT key with the review count, error count and estimated cost. Error rows carry an error_code and are free - you pay only for real reviews.

How much does it cost to scrape Walmart reviews?

Reviews are billed at $0.60 per 1,000 results ($0.0006 each). The math is simple:

• $5 in free Apify trial credits = roughly 8,300 reviews ($5 / $0.0006)
• 1,000 reviews = $0.60
• 8,300 reviews = $5.00
• 10,000 reviews = $6.00

You are charged only for review rows actually returned. Errors and empty results cost nothing, so a misconfigured run never burns your budget. Live per-event pricing is shown on this actor's Apify Store page.

What can you use Walmart reviews for?

• Sentiment analysis - track how shoppers feel about a product over time.
• Product feedback mining - surface defects, sizing issues and feature requests at scale.
• Competitor CX intelligence - read how rival products handle complaints and returns.
• Quality monitoring - watch ratings and themes shift right after a restock or relaunch.
• Review summarization - feed reviews into an LLM to auto-tag issues and route them to teams.
• Assortment and merchandising - compare review volume and scores across a category.
• Brand monitoring - get a heads-up when negative reviews spike on your listing.
• Churn and returns research - read 1 and 2 star reviews to learn why buyers send items back.
• Pre-purchase research - benchmark a product against substitutes before you stock it.

Is it legal to scrape Walmart?

Scraping publicly available data, including public product reviews on Walmart.com, is broadly legal in the US and EU and has been upheld in cases such as hiQ v. LinkedIn. This actor collects only public review data that any visitor can see, never private accounts or hidden content. You are responsible for using the output in line with Walmart's terms, GDPR/CCPA and your local laws - for compliance-sensitive projects, consult your legal team.

Related actors

Part of the Data Forge fleet:

• Walmart Product Scraper - pull rich product data: title, brand, price, rollback flag, rating, specs, images and seller.
• Walmart Intelligence Scraper - the umbrella actor for product search, category, bestsellers, deals, product detail and reviews in one place.
• Amazon Reviews Scraper - the same review workflow for Amazon: rating, title, text, date and verified-purchase per row.

Need TripAdvisor, Booking, Google Maps or Google Play data too? The broader Data Forge fleet covers those.

FAQ

How many reviews can I pull per product? Up to 1,000 per product via pagination - set Max reviews per product to control depth, and run many products in a single job.

Which products are supported? Any product on Walmart.com that has public reviews. Pass a catalog id, a numeric item id, or a product/reviews URL - the input is normalized for you.

Is it legal to scrape Walmart reviews? Public product reviews are public data, and scraping public data is broadly legal in the US and EU, as upheld in hiQ v. LinkedIn. This actor reads only reviews any visitor can see, never private accounts. See the legality section above for the full note, and confirm your specific use with your legal team.

How fresh are the reviews? Every run pulls reviews live from Walmart.com with no cached middle layer, so a scheduled daily run always reflects the current review set. Use the Newest first sort (submission-desc) to surface reviews posted since your last run.

What if a product has only a few reviews? You get exactly what the product has - if there are 12 public reviews, you receive 12 rows and pay for 12. Max reviews per product is a ceiling, not a target. If a product or filter set returns no reviews at all, the run pushes a free NO_REVIEWS row and you are charged nothing.

Can I schedule this to monitor reviews over time? Yes. Every Data Forge actor ships an Apify scheduler and webhooks. Run it daily or hourly with sort submission-desc, then pipe new rows into your warehouse, a Google Sheet or a Slack alert to catch negative reviews as they land.

Can I call this from the API or an integration? Yes. Each Apify actor ships a REST API, scheduler, webhooks and an MCP server, so you can run the Walmart Reviews Scraper from your own code, Make, Zapier or an AI agent.

Can I filter by star rating or verified buyers? Yes. Set the Star rating filter to keep only 1 to 5 star reviews, toggle Verified purchases only, and Sort by newest, oldest, rating or relevance.

Do I pay for failed runs? No. Error and empty rows are free - you are billed only for real review results.

Support

I personally support customers directly - reach out on any channel.