Paste a walmart.com product URL and get that listing's customer reviews back as rows: the reviewer, the headline, the full comment, the date, the star rating, whether Walmart marked it a verified purchase, and how many shoppers found it helpful. Pick the order they arrive in - newest first, highest rated first, or the site's own relevance ranking.
The input is the listing, not the review - you name the products you want feedback on and the job collects what their buyers wrote.
One row per review, each tagged with the query it came from - so a run across fifty listings still reconciles back to your input list.
Walmart product URLs carry a descriptive slug that changes when the listing is retitled, and the same item can reach you under more than one of them. Every row also carries item_id, parsed from the URL itself - so two differently-worded links to the same product join cleanly on one column.
Walmart marks reviews it can tie to an actual order, and that mark arrives as its own column rather than as a filter applied for you. Weight it, split on it or ignore it - the decision stays in your analysis instead of being baked into the export.
Most relevant, top reviews, newest or oldest first, highest or lowest rating first. Combined with a per-query limit, that is what decides which slice of a long review list you spend credits on - a hundred newest reads very differently from a hundred most relevant.
Each row carries the review as Walmart displays it: who wrote it, the headline, the comment, the date, the star rating, the verified-purchase mark, and the helpful counts in both directions.
Comment text comes back verbatim, with no preprocessing applied, so downstream sentiment work reads the shopper's own wording rather than ours. The column list below is the header row of real run exports rather than a specification - and it is worth reading the note under it before you write a parser, because several of these fields are not the type they look like.
Taken from the header row of real run exports, identical across every one of them and identical to the column list the platform ships. The note underneath covers the types, which are the part that catches people out.
item_id rather than from this column - that is the field derived from the URL you submitted.5/20/2026.Yes where Walmart marks the review a verified purchase. It is a flag on the row, not a filter applied to the export.Two things to know before you write code against this. First, the types are not what they look like: rating, helpful_positive and helpful_negative all come back as text, while position comes back as a number - so a naive sum over the ratings column concatenates instead of adding. Second, join on item_id, not on query or product_name: the same product reaches the scraper under more than one URL slug, and query faithfully preserves whichever one you sent. Pull a free run and read the first rows before you assume anything else.
A per-query limit and a sort order together decide which slice of a long review list you pay for. Both are chosen before the job starts, alongside how you hand over the list of products.
A few examples of how teams use Walmart review data to answer a question they actually have.
Sort a listing lowest-rated first and read the comments as a queue rather than a score. Recurring language - arrived dented, missing part, broke in a week - names the failure long before it shows up as a return rate.
Pull a competitor's product URL and take the verified-purchase rows. What buyers praise and what they complain about is the clearest brief you will get on what your own listing has to answer.
Feed a list of product URLs, join on item_id and watch ratings and helpful counts move over repeated runs. One row per review means the raw material stays intact instead of arriving pre-averaged.
No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.
Every new account, one-time. No credit card required. Per-query limits, every sort order, file upload and every export format included.
Roughly $2 per 1,000 reviews. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for continuous monitoring or very large historical pulls. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.The same product, the other marketplace. Running both on an item you sell is the quickest way to see whether a complaint is about the product or about the channel.
Reviews aggregated across retailers rather than tied to one listing - useful for a view of a product that no single storefront gives you.
Short answer: yes for the public review content - and this export carries nothing about the order behind it.
Reviews on Walmart are published to be read. The star rating, the headline, the comment, the date, the verified-purchase mark and the helpful counts are shown to anyone who opens the product page, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login, a cart or a paywall.
The reviewer field carries what the site itself publishes, which is a display name rather than a full identity - there is no profile link, no email and no address in this output. Nothing about the order behind a verified purchase is collected either: not what was paid, not where it shipped, not when. If you are processing the comment text in the EU, the usual rules still apply to what you do with it downstream.
Walmart's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the retailer, check it. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's Walmart Reviews Scraper turns a list of product listings into review data. You submit walmart.com product URLs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - choose a sort order, cap the rows per query if you want to, and download what shoppers wrote as a clean CSV, Excel or JSON file.
Each row carries the review as Walmart displays it: the reviewer's display name, the headline, the full comment, the posting date, the star rating, whether Walmart marked the review a verified purchase, and how many shoppers found it helpful or unhelpful. Every row also carries the Walmart item ID parsed from the URL, which is what lets a run across many listings join cleanly even when the same product was submitted under more than one link.
Product teams sort a listing lowest-rated first and read the complaints as a queue, where recurring language names a defect long before it shows up as a return rate. Strategy teams pull a competitor's listing and take the verified-purchase rows as a brief on what their own product page has to answer. Researchers feed a whole catalogue, join on the item ID and watch ratings and helpful counts move across repeated runs.
One practical note up front: the rating and the helpful counts come back as text while the position comes back as a number, so cast before you average. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.