Walmart Reviews Scraper

Walmart buries thousands of reviews behind paginated pages and a layout that fights copy-paste. Reading them by hand is a non-starter once you go past one product. Point this scraper at a Walmart product URL or item ID and it walks the review pages for you, pulling each review's text, star rating, reviewer name, verified-buyer flag, and helpful-vote count into one clean row. Scrape a single product or feed it a whole list, then export to JSON, CSV, or Excel.

What you get

Every review becomes one tidy row with the same shape, so your columns line up whether you load the results into a spreadsheet or a database. Each row carries three groups of data:

• The review — feedbackId, feedbackTitle, feedbackText, starRating, authorName, helpfulVotes, verifiedBuyer
• The product — itemId, itemName, itemUrl, averageRating, reviewsTotal
• Run metadata — collectedAt, errorMessage

You control the volume with a per-product cap, filter down to a single star band, and sort by newest, most helpful, or rating.

Quick start

1. Click Try for free and open the input form.
2. Paste a Walmart product link or item ID into Single product link or item ID, or drop a batch into Multiple product links or item IDs.
3. Optionally pick a Star-rating filter, a Review order, and a Reviews per product cap.
4. Press Start, then export the results as JSON, CSV, Excel, or XML once the run finishes.

Use cases

• Product research — read what buyers actually say before adding an item to your catalog
• Brand monitoring — track the rating and complaint trends on your own listings over time
• Competitor analysis — pull rival products' reviews to spot recurring praise and pain points
• Sentiment and NLP projects — feed clean review text into your own classifier or summarizer
• Quality control — surface the 1- and 2-star reviews fast to flag defects and shipping issues
• Market trends — compare average ratings and review volume across a category

Input

Field	Type	Required	Description
itemUrl	string	One of itemUrl or itemUrls	A single Walmart product URL or numeric item ID. Example: https://www.walmart.com/ip/.../331530972.
itemUrls	array of strings	One of itemUrl or itemUrls	A batch of Walmart product URLs or item IDs, one per line. Can be combined with itemUrl.
ratingFilter	string	No	Limit to one star band or keep every rating. One of all, 5, 4, 3, 2, 1. Default all.
sortOrder	string	No	Order reviews come back in: relevancy, submission_desc, submission_asc, helpfulness, rating_desc, rating_asc. Default relevancy.
reviewsLimit	integer	No	Cap on reviews gathered per product. Default 50.
timeoutSeconds	integer	No	Seconds to wait per request before timing out. Default 45.

Example input

{
    "itemUrls": [
        "https://www.walmart.com/ip/Keurig-K-Mini-Single-Serve-Coffee-Maker/331530972",
        "https://www.walmart.com/ip/onn-50-Class-4K-UHD-Roku-Smart-TV/580942227",
        "331530972"
    ],
    "ratingFilter": "all",
    "sortOrder": "relevancy",
    "reviewsLimit": 50,
    "timeoutSeconds": 45
}

Output

Each review produces one row, and every field is always present — anything Walmart doesn't expose comes back as null so your dataset stays rectangular.

Example output

{
    "feedbackId": "187234509",
    "feedbackTitle": "Perfect for a small kitchen",
    "feedbackText": "Brews a single cup fast and takes up almost no counter space. The water tank is a little small but that's the trade-off for the size.",
    "starRating": 5,
    "authorName": "CoffeeLover22",
    "helpfulVotes": 14,
    "verifiedBuyer": true,
    "itemId": "331530972",
    "itemName": "Keurig K-Mini Single Serve Coffee Maker",
    "itemUrl": "https://www.walmart.com/reviews/product/331530972",
    "averageRating": 4.6,
    "reviewsTotal": 2841,
    "collectedAt": "2026-06-30T12:00:00.000000+00:00",
    "errorMessage": null
}

Output fields

Field	Type	Description
feedbackId	string	Unique identifier Walmart assigns to the review
feedbackTitle	string	Short headline the shopper gave their review
feedbackText	string	Full written text of the review
starRating	number	Score the reviewer gave, 1 through 5
authorName	string	Public nickname or display name of the reviewer
helpfulVotes	integer	How many shoppers flagged the review as useful
verifiedBuyer	boolean	True when Walmart marks the reviewer as a confirmed purchaser
itemId	string	Numeric Walmart item ID of the product
itemName	string	Title of the product under review
itemUrl	string	Link to the Walmart review page for the product
averageRating	number	Mean star score across all reviews for the product
reviewsTotal	integer	Total review count for the product
collectedAt	string	ISO 8601 timestamp of when the row was captured
errorMessage	string	Why a record failed; null on success

Tips for best results

• Start with one product. Run a single URL first to confirm the output fits your pipeline before queuing a long list.
• Use the star filter to cut noise. Set ratingFilter to 1 or 2 when you only want the complaints, or 5 when you only want the praise.
• Sort to match the job. helpfulness surfaces the reviews other shoppers trust; submission_desc keeps you on the latest feedback.
• Keep reviewsLimit modest while testing. A cap of 20–50 confirms the shape quickly, then raise it once you're happy.
• Raise timeoutSeconds on slow runs. If you see timeout errors, push it toward 60–90 seconds.
• A blank review list usually means the product has no reviews for that filter, not an error. Switch ratingFilter to all to confirm.

How can I use Walmart reviews data?

How can I use the Walmart Reviews Scraper to track my product's reputation? Paste your product's URL, set sortOrder to submission_desc so the newest reviews land first, and run it on a schedule. Each row gives you the star rating, review text, and verified-buyer flag, so you can watch your average rating move and catch new complaints as they show up.

How can I collect Walmart customer reviews for sentiment analysis? Feed a list of product URLs into itemUrls and raise reviewsLimit to gather a deep sample. The feedbackText and starRating fields drop straight into an NLP pipeline, so you can train a classifier, summarize themes, or score sentiment without any manual copying.

How can I compare competitor products on Walmart by their reviews? Add several competing item URLs to itemUrls in one run. Every row tags the itemName and itemId, so you can group the results by product and line up average ratings, review volume, and common gripes side by side.

How do I export Walmart review data to CSV or Excel? Run the scraper, then use Apify's export options to download the dataset as CSV, Excel, JSON, or XML. Because each review is one flat row with consistent fields, it opens cleanly in any spreadsheet or BI tool for filtering and charts.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.