Walmart Reviews Scraper

Walmart Reviews Scraper

Walmart Reviews Scraper is a fast, reliable Walmart reviews crawler that lets you scrape Walmart product reviews at scale and turn them into a clean Walmart reviews dataset. It solves the repetitive, manual work of collecting ratings and customer feedback by automating extraction from Walmart product and search pages. Built for marketers, developers, data analysts, and researchers, this Walmart reviews scraping tool helps you download Walmart product reviews, power sentiment analysis, and export Walmart product reviews in structured formats for downstream use.

What data / output can you get?

Below are the exact JSON fields the Walmart Reviews Scraper saves to the Apify dataset. These are pushed during the run and can be exported to JSON, CSV, or Excel.

Data field	Description	Example value
itemType	Record type identifier	"Review"
productUrl	Canonical Walmart product URL for the review	"https://www.walmart.com/ip/Free-Assembly-Men-s-Everyday-Cotton-Tee-with-Short-Sleeves-Sizes-S-3XL/888475689?classType=VARIANT&from=/search"
rating	Star rating as a number	5
title	Review title (nullable)	"Solid shirt"
text	Full review text	"Good shirt, I bought it to run to the store or around town. Neckline is good."

Notes:

• title may be null when the reviewer didn’t provide a headline.
• You can export the dataset to JSON, CSV, or Excel from Apify for analysis and reporting.

Key features

• 🚀 Powerful review extraction Extracts ratings, titles, and text from Walmart review pages and saves each record as a structured JSON object for data analysis and reporting.

• 🔍 Search-to-product crawling Start from keywords or search URLs to auto-discover product pages, then scrape reviews for each product found.

• 🧭 Review sorting control Choose how to sort reviews via sortOrder with options: relevancy, submission-desc, helpful, rating-desc, rating-asc.

• 📄 Automatic pagination Iterates through review pages to collect up to your max per product (controlled by maxComments).

• 🧰 Live dataset output Saves results to the Apify dataset as they’re found, enabling near real-time data consumption and exports.

• 🧪 Conservative rate limiting & retries Built-in delays, retries, and robust error handling help keep runs stable when scraping Walmart product reviews at scale.

• 🛡️ Residential proxy with fallback Uses Apify residential proxy by default with smart fallback/rotation logic to reduce blocks and keep your Walmart reviews scraper reliable.

• 📦 Batch processing from search pages Control how many products to process from each search page with maxProductsPerStartUrl for efficient, targeted runs.

How to use Walmart Reviews Scraper - step by step

1. Create or log in to your Apify account.
2. Open the Walmart Reviews Scraper in the Apify Store.
3. Add input:
   • Paste Walmart product URLs, search URLs, or plain keywords into startUrls (supports bulk).
   • Examples: "https://www.walmart.com/ip/…", "https://walmart.com/search?q=tshirt", or "tshirt".
4. Configure options:
   • sortOrder: Choose one of ["relevancy", "submission-desc", "helpful", "rating-desc", "rating-asc"].
   • maxComments: Set how many reviews to collect per product (1–1000).
   • maxProductsPerStartUrl: Limit the number of products discovered from each search URL.
   • skipProductsWithoutReviews: Toggle whether to skip products with no reviews.
   • proxyConfiguration: Leave default or adjust if needed.
5. Start the run and monitor logs as the Walmart reviews crawler discovers products and scrapes their reviews.
6. Review output in the Dataset tab during the run (records are pushed live).
7. Download your Walmart reviews dataset as JSON, CSV, or Excel, or consume it programmatically from the dataset.

Pro tip: To quickly download Walmart product reviews for many items, use a search URL with a focused query and tune maxProductsPerStartUrl and maxComments for speed and volume.

Use cases

Use case	Description
E-commerce optimization	Aggregate ratings, titles, and review text to pinpoint product strengths/weaknesses and prioritize improvements.
SEO content strategy	Mine customer language from review text to discover phrases and topics customers actually use.
Competitive analysis	Collect comparable Walmart reviews datasets across products to track sentiment and feature feedback trends.
Customer research	Analyze recurring themes in review text to understand user needs and objections pre-purchase.
QA and support	Surface common issues in review text to inform quality assurance and knowledge base updates.
Data science & NLP	Build a labeled Walmart reviews dataset for sentiment analysis, topic modeling, and text classification.
Automation pipelines	Feed clean JSON review records into BI tools, dashboards, or ETL workflows via the Apify dataset.

Why choose Walmart Reviews Scraper?

This Walmart reviews scraper is built for precision, scale, and reliability on real Walmart product pages.

• 🎯 Accurate, structured output: Consistently captures rating, title, and text as clean JSON fields.
• 📈 Scalable controls: maxComments (1–1000) and maxProductsPerStartUrl give you tight control over volume and scope.
• 🔄 Search-to-review workflow: Start from keywords/search URLs to discover products automatically before scraping reviews.
• 🧩 Developer friendly: JSON dataset output fits seamlessly into analytics stacks and ETL jobs.
• 🛡️ Production-ready proxies: Residential proxy with fallback and rotation logic reduces blocks for dependable runs.
• 💼 Flexible exports: Export Walmart reviews data in JSON, CSV, or Excel for easy sharing and analysis.

Bottom line: If you need a Walmart reviews scraping tool that reliably produces a structured Walmart reviews dataset, this is a robust, automation-ready choice.

Is it legal / ethical to use Walmart Reviews Scraper?

Yes—when done responsibly. This scraper targets publicly available Walmart product and review pages and does not access private or login-protected content.

Guidelines:

• Only extract public data and respect Walmart’s Terms of Service.
• Comply with data protection laws (e.g., GDPR, CCPA) applicable to your use.
• Avoid scraping personal or sensitive information.
• Validate your specific use case with your legal team if in doubt.

Input parameters & output format

Example JSON input

{
  "proxyConfiguration": {
    "useApifyProxy": false
  },
  "skipProductsWithoutReviews": true,
  "startUrls": [
    "https://walmart.com/search?q=tshirt"
  ],
  "sortOrder": "relevancy",
  "maxComments": 10,
  "maxProductsPerStartUrl": 20
}

Parameters

• startUrls (array, required): List of Walmart product URLs, search URLs, or keywords. Supports bulk input.
  • Default: none (required)
• sortOrder (string): How to sort reviews. Options: relevancy, submission-desc, helpful, rating-desc, rating-asc.
  • Default: "relevancy"
• maxComments (integer): Maximum number of reviews to extract per product. Set to 0 for unlimited (not recommended). Range: 1–1000.
  • Default: 10
• maxProductsPerStartUrl (integer): Maximum number of products to process from each start URL (for search URLs). Set to 0 for unlimited.
  • Default: 20
• skipProductsWithoutReviews (boolean): If enabled, products with no reviews will be skipped to speed up processing.
  • Default: true
• proxyConfiguration (object): Proxy settings. Prefill sets useApifyProxy to false.
  • Default: { "useApifyProxy": false }

Example JSON output

[
  {
    "itemType": "Review",
    "productUrl": "https://walmart.com/ip/Free-Assembly-Men-s-Everyday-Cotton-Tee-with-Short-Sleeves-Sizes-S-3XL/888475689?classType=VARIANT&from=/search",
    "rating": 5,
    "title": "Solid shirt",
    "text": "Good shirt, I bought it to run to the store or around town. Neckline is good."
  },
  {
    "itemType": "Review",
    "productUrl": "https://walmart.com/ip/Free-Assembly-Men-s-Everyday-Cotton-Tee-with-Short-Sleeves-Sizes-S-3XL/888475689?classType=VARIANT&from=/search",
    "rating": 4,
    "title": "Good basic tee",
    "text": "Nice basic tee for the price. Bought to wear under sweaters and jackets..."
  }
]

Notes:

• title can be null if a reviewer didn’t include a headline.

FAQ

Can I start from keywords, search URLs, or product URLs?

Yes. Provide any mix of Walmart product URLs, search URLs, or plain keywords in startUrls. The scraper normalizes keywords to Walmart search URLs and will discover product pages automatically.

Which review fields are included in the output?

The dataset contains itemType, productUrl, rating, title, and text for each review. These are pushed during the run and visible in the default dataset view.

How many Walmart reviews can I collect per product?

You control this with maxComments, which accepts values from 1 to 1000 per product. Set an appropriate limit based on your needs and runtime.

Does it sort reviews by recency or helpfulness?

Yes. Use sortOrder to choose among relevancy, submission-desc, helpful, rating-desc, or rating-asc so you can prioritize the reviews you need.

Can it crawl multiple products from a search page?

Yes. When you start from a search URL or keyword, the Walmart reviews crawler discovers products and processes up to maxProductsPerStartUrl per start URL.

Do I need to log in or use cookies?

No. The Walmart Reviews Scraper works with publicly available pages and includes proxy handling to keep requests reliable.

Can I export Walmart product reviews to CSV or Excel?

Yes. After the run, open the dataset and export Walmart product reviews in JSON, CSV, or Excel for analysis and sharing.

Does it work on Walmart.ca?

This scraper targets walmart.com URLs. Behavior on other regional domains like walmart.ca isn’t guaranteed.

Final thoughts

Walmart Reviews Scraper is built to extract structured Walmart customer feedback at scale. With keyword/search-to-product discovery, review sorting, proxy resilience, and live JSON outputs, it helps marketers, developers, data analysts, and researchers build a dependable Walmart reviews dataset fast. Use Apify datasets to export Walmart product reviews or plug the results into your automation and analytics workflows. Start extracting smarter, cleaner insights from Walmart product reviews today.