How to Scrape More than 2M+ Reviews from Walmart?

Consumer reviews are what ‘breaks or makes it’—they can easily swing your customer’s decision to buy or skip your product. Imagine having the power to gather and analyze millions of such reviews from a retail giant like Walmart Review Data Scraping is what you need to get insights about customer perception. That's worth finding treasure in the retail sphere.

This article delves deep into a fine and profoundly affordable strategy to scrape over 2 million reviews from Walmart ethically and effectively. Let’s dig in!

Why Review Data Scraping?

Customer Review Data Scraping is the most efficient solution that allows your business to understand consumer sentiments and product performance. Walmart having one of the biggest retail chains globally, is a treasure trove of data in the review section.

Ethical Considerations in Web Scraping Walmart Reviews

Why do Ethical Practices Matter?

Before you dive into the scraping game, you must know some ethical standards. This ensures your business respects the terms of service at Walmart. Such policies are created for obvious reasons; to prevent breach of boundaries. Additionally, data privacy regulations are significant considerations if you don't want a legal problem in your hands and scar your brand reputation.

Retailers and businesses are bound to follow responsible scraping methods. This includes:

• Adhering to the robots.txt file, which tells you what pages you are allowed or not allowed to scrape
• Being considerate of the time and way of requesting data to not overwhelm Walmart's servers.

Pre-Scraping Steps

Define Your Goals

Initially, take a minute to define what you're after. Are you looking for product ratings, user comments, or reviewers' details? Every data source serves a particular purpose competitive analysis, for example, product development or market research. Knowing your goal can streamline the scarping procedure, making it more targeted and efficient.

Read Walmart's Terms of Service

Go through and make yourself familiar with Walmart's policy about scraping as every website has distinct rules regarding data scraping. So, by remaining compliant with terms of service, you'll avoid all possible legal issues and match your methods to Walmart's policies regarding everything so nothing falls there either.

Tools and Technologies You'll Need

Now that you have determined the goals and ethics for scraping Walmart, it’s time to pick the right tools to scrape. The best tools will depend on whether or not you are dealing with dynamic content or if you need to parse large data sets efficiently. Here are a few of the popular web scraping tools and frameworks:

• ReviewGators API: Built to scale reviews, ReviewGators' powerful API allows you to extract reviews efficiently even when dealing with dynamic pages and large datasets.
• Scrapy: Powerful and flexible web scraping framework, focusing particularly on structured data across many websites—supports scalable scraping and even database integration.
• Beautiful Soup: A Python library suitable for "smaller" datasets or more specific targeted extraction of elements from an HTML or XML source, pretty easy to use and works great for quick, targeted data extraction.
• Selenium: For scraping websites that contain sufficient dynamic content or those with plenty of JS-heavy pages. In other words, it will simulate a browser to enable you to interact with elements, like logins or page scrolls.

All these technologies, combined with ReviewGators' API solutions, ensure that the gathering process is smooth, efficient, and scalable.

Structuring Your Scraper

Coding Best Practices

As you start building your scraper, a smart move would be to follow best practices. You need to write modular code that is easy to maintain and scale. Break your code into small, self-contained functions each representing a specific task. It helps in adding comments and documentation for easy understanding of your codes and logic by whoever looks at it.

Implementation of Web Scraping Techniques

Now that you have your tools set up and your code structured, let’s get started with scraping! Start by gathering valuable data from product pages, specifically scooping from the review section. Employ filters for smooth data-gathering—for example, reviewing filters based on star ratings helps handle the large review chunks effectively.

Executing the Productive Strategy for Walmart Review Scraping

For retailers selling products, customer reviews on platforms like Walmart provide invaluable insights into customer feedback, preferences, and potential improvements. Scraping these reviews in large quantities can help you analyze your customer sentiment at scale.

Here's how you can use a robust scraping API tool to scrape over 2M+ reviews from any product on Walmart.

For our demonstration, we are scraping reviews for the Apple Watch Series 10:

Step 1: Understand Walmart's Review Structure

Before scraping, you should know the structure of reviews at Walmart. For instance, if the product is an Apple Watch Series 10, reviews about it on Walmart would be structured in the format below:

Review Title (for example, "Best smartwatch ever!")
Review Text (the full review text)
Reviewer Name (for example, "John D.")
Rating (for example; 4.5)
Review Date

Step 2: Get API Access via the API tool—ReviewGators

You first need to register at ReviewGators to fetch their API for scraping reviews for Walmart. When you have successfully created your account here, retrieve your API key. Your access to their Walmart review scraping service is authenticated through this API key.

API Key: Needed for all requests.

Endpoint: Walmart-specific review scraping endpoint offered by ReviewGators.

Demonstration:

import requests

api_key = "YOUR_API_KEY"
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

# Test the API key
response = requests.get("https://api.reviewgators.com/walmart/reviews", headers=headers)
print(response.status_code)

This step checks if a status code of 200 is returned. If so, your API key is perfect, and you are good to go.

Step 3: Fetch the Walmart Product ID for Apple Watch Series 10

To scrape reviews for a specific product like the Apple Watch Series 10, you will require the Walmart Product ID. This is obtained from the Walmart product URL.

Example of Walmart URL for Apple Watch Series 10:

https://www.walmart.com/ip/Apple-Watch-Series-10/987654321

The product ID here is; 987654321

Step 4: Review Scraping for Apple Watch Series 10 Using ReviewGators API

With the product ID and API key by your side, you can now start scraping reviews using ReviewGators' API. The following Python script demonstrates how you might scrape reviews for the Apple Watch Series 10 product:

import json
import requests

# Walmart Product ID for Apple Watch Series 10
product_id = "987654321"

# ReviewGators Walmart Review API endpoint
url = f"https://api.reviewgators.com/walmart/reviews/{product_id}"

# Parameters for scraping reviews
params = {
    "limit": 1000,  # Number of reviews to scrape in one request
    "offset": 0     # Start from the first review
}

# Function to scrape reviews
def scrape_reviews(product_id, limit, offset):
    response = requests.get(
        f"https://api.reviewgators.com/walmart/reviews/{product_id}",
        headers=headers,
        params={"limit": limit, "offset": offset}
    )
    reviews = response.json()  # Convert response to JSON
    return reviews

# Example: Scrape the first 1000 reviews for Apple Watch Series 10
reviews_data = scrape_reviews(product_id, 1000, 0)

# Print out the first review as a sample
print(json.dumps(reviews_data['reviews'][0], indent=4))

This script scrapes the first 1,000 reviews of Apple Watch Series 10. Wanting to scrape over 2M reviews would require paginating your requests to grab all the reviews.

Step 5: Paginate Large Review Datasets

To scrape a high number of reviews (2M+), paginate through the reviews by adjusting the offset parameter. In this way, you can collect reviews in batches.

# Pagination loop to scrape 2M+ reviews for Apple Watch Series 10
total_reviews = 2000000  # Target review count
batch_size = 1000        # Reviews per batch

all_reviews = []

for offset in range(0, total_reviews, batch_size):
    batch_reviews = scrape_reviews(product_id, batch_size, offset)
    all_reviews.extend(batch_reviews['reviews'])
    
    # Pause to avoid overloading the server (optional)
    time.sleep(2)  # Use this to avoid rate limits

print(f"Scraped {len(all_reviews)} reviews in total.")

This loop would scrape 1,000 reviews per request until you assemble 2M+ reviews on Apple Watch Series 10.

Step 6: Strore the Align the Scraped Data

At this point, you'll probably have millions of reviews scraped out. Make sure to save them for further analysis. For an unstructured dataset (like given above), MongoDB is decent. For structured, relational data, MySQL or PostgreSQL are good.

Step 7: Keep Track of Your API Quotas and Scale Scraping

Consider the following while scraping millions of reviews:

• API Usage Monitoring: Avoid hitting rate limits on ReviewGators
• Infrastructure Scaling: Use cloud resources, e.g., AWS or Google Cloud, when operating with huge datasets
• Error Handling: Make retries for failed requests and manage timeouts.

Conclusion

Scraping 2M+ reviews from Walmart is an excellent means of really tapping into customer interests and product performance. By this far, you've learned the importance of ethical practices, structuring your scraper, and eventually, strategical steps for Walmart scraping. Implementing the best measures and tools offers a seamless retail journey, unveiling the full potential of the insights present in customer reviews.

If you are seeking smooth and effective review scraping for your products, trying the ReviewGators API tools is worth your investment shot. With us guiding you through all your retail needs, you can have data-driven decision-making at your disposal!