LAGIC
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Walmart Reviews Scraper — Walmart | Lagic

Built For

Extract customer reviews and ratings for any Walmart product.

Curated by Lagic·Verified working

Configure Agent

Provide urls of Walmart category, search or product pages.

Enter the maximum number of products you want to scrape per each star URL.

Enter the maximum number of reviews you want to scrape per each product.

Choose how to order the reviews. Note: 'Review Date From' filter only works with 'Newest to Oldest' sort type.

Search all reviews with date from this limit, for example 2024-01-30 will return reviews with date >= 2024-01-30. Only works when sort type is set to 'Newest to Oldest' (submission-desc).

Results to deliver

400 credits

This agent actively searches live listings — results may vary. You are only charged for what is delivered, up to this number.

Lagic Proxy

Country auto-rotated. Need a specific region? Contact support.

Pricing

4 credits per result
✓ 30 free credits on signup✓ Refund if 0 results✓ No card required

Sample Data Preview

Product NameProduct URLUnique Review IDStar Rating (1-5)Review TitleFull Review Text
Sample Text...https://...10098Value...Sample Text...Value...
Sample Text...https://...10090Value...Sample Text...Value...
..................
Exports as:CSVXLSXJSON

Overview

Scrape customer reviews from any Walmart product, category, or search URL. Get review text, ratings, submission dates, and helpfulness scores to analyze customer sentiment and product feedback.

This tool extracts customer reviews directly from Walmart.com, turning anecdotal feedback into structured data. It's designed for anyone who needs to understand what real customers are saying about products, from brand managers tracking a new launch to market researchers analyzing category trends. ### How it works You can start with specific product page URLs or go broader with links to category or search result pages. The tool will find the products and then extract the reviews associated with them. You have control over the scale, setting limits on how many products to check and how many reviews to pull from each one. This allows for both targeted analysis of a single item and broad sweeps of a market segment. ### What you get For each review, you receive the core data: the star rating, review title, full text, and the reviewer's nickname. Crucially, you also get metadata like the submission date, the number of 'helpful' and 'not helpful' votes, and whether the customer would recommend the product. This data can be sorted by relevance, helpfulness, date, or rating, and you can even filter to get only reviews submitted after a specific date, which is ideal for tracking sentiment over time.

Key Capabilities

  • Product Name
  • Product URL
  • Unique Review ID
  • Star Rating (1-5)
  • Review Title
  • Full Review Text
  • Reviewer's Nickname
  • Date and Time of Submission
  • "Helpful" Vote Count
  • "Not Helpful" Vote Count
  • Whether the reviewer recommends the product
  • Attached photos
  • Reviewer badges (e.g., "Verified Purchase")
  • Product Launch Monitoring: Track initial customer feedback and sentiment for a newly launched product by scraping its reviews daily.
  • Competitive Analysis: Analyze the strengths and weaknesses of competitor products by extracting and comparing their customer reviews.
  • Quality Control: Identify recurring product defects, shipping issues, or customer complaints by filtering for 1- and 2-star reviews.
  • Voice of Customer (VoC) Analysis: Aggregate thousands of reviews to identify common themes, feature requests, and points of praise for product development.
  • Marketing Content Curation: Find authentic, positive review quotes and user-submitted photos to use in marketing campaigns and on social media.
  • Market Trend Spotting: Scrape reviews from an entire product category to understand emerging consumer needs and preferences.
  • Sentiment Tracking Over Time: Use the date filter to analyze how customer opinions change after a product update, price change, or marketing campaign.

Field Dictionary

How To Run This Extractor

1

Paste one or more Walmart URLs into the 'Start urls' field. These can be links to products, categories, or search results.

2

Set the maximum number of products you want to scrape from each starting URL.

3

Define the maximum number of reviews you want to extract from each individual product.

4

Choose how to sort the reviews, such as by date, helpfulness, or rating.

5

Optionally, add a start date to only collect reviews published recently. This requires sorting by 'Newest to Oldest'.

6

Run the tool and download your collected review data in CSV, JSON, or Excel format.

Frequently Asked Questions

Do I need to be a developer to use this?
No, this tool is designed for non-technical users. You just need to provide Walmart URLs and set your limits.
What formats can I export the data in?
Is it legal to scrape reviews from Walmart?
How many reviews can I scrape at once?
Can I use this for client work?
How is this different from just reading reviews on the site?
How reliable is the data extraction?
Can I schedule the scraper to run automatically?
How do I get only the most recent reviews?
Can I get reviews for just one product or a whole category?
What do the 'positiveFeedback' and 'negativeFeedback' fields represent?