Walmart is one of the biggest e-commerce websites that contains millions of products along with the reviews of customers. Product reviews scraped from Walmart can give businesses, market researchers, and analysts insights into their activities. In this tutorial, we will guide you step-by-step on how to scrape Walmart product reviews using a Walmart scraper efficiently and ethically.
Why Scrape Walmart Product Reviews?
Before diving into the scraping process, let's understand why extracting Walmart reviews is beneficial:
Competitive Analysis: Monitor customer sentiment about competing products.
Product Research: Identify strengths and weaknesses of different products based on real customer feedback.
Sentiment Analysis: Understand consumer preferences and emerging trends.
Price and Quality Correlation: Analyze how price impacts customer satisfaction.
Is Scraping Walmart Product Reviews Legal?
Web scraping should be done ethically and according to Walmart's Terms of Service. Before scraping, check Walmart's robots.txt file and use publicly available APIs if possible. Avoid scraping private or sensitive user data to remain within legal boundaries.
Step-by-Step Guide to Scraping Walmart Product Reviews
1. Choose the Right Walmart Scraper Tool
Several tools can help you extract product reviews from Walmart, including:
Scrapy (Python-based scraping framework)
BeautifulSoup (Python library for parsing HTML)
Selenium (For dynamic content scraping)
Scraper APIs (Ready-made Walmart scrapers for ease of use)
2. Identify the Walmart Product Page URL
Each Walmart product has a unique URL that includes customer reviews. You need to extract this URL to fetch review data.
Example URL structure:
walmart.com/reviews/product{product_id}
3. Inspect the Walmart Reviews Section
Use your browser’s Inspect Element (Right-click > Inspect) tool to find the HTML structure of the review section. Identify key elements like:
Reviewer name
Review text
Star rating
Review date
4. Write Your Scraping Code
Here’s an example Python script using BeautifulSoup to extract Walmart reviews:
import requests
from bs4 import BeautifulSoup
# Define the Walmart product review page URL
url = "https://www.walmart.com/reviews/product/{product_id}"
headers = {"User-Agent": "Mozilla/5.0"}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')
reviews = soup.find_all('div', class_='review')
for review in reviews:
reviewer = review.find('span', class_='reviewer-name').text.strip()
rating = review.find('span', class_='rating').text.strip()
review_text = review.find('div', class_='review-text').text.strip()
print(f"Reviewer: {reviewer}\nRating: {rating}\nReview: {review_text}\n")
5. Handle Pagination
Walmart product reviews are often spread across multiple pages. Modify your script to handle pagination by identifying the Next Page button and iterating through all pages.
6. Store and Analyze the Data
Once extracted, save the data in a structured format such as CSV or JSON for further analysis:
import csv
with open('walmart_reviews.csv', 'w', newline='', encoding='utf-8') as file:
writer = csv.writer(file)
writer.writers(["Reviewer", "Rating", "Review Text"])
for review in reviews:
writer.writers([reviewer, rating, review_text])
7. Respect Walmart’s Policies
To avoid getting blocked:
Use delays between requests.
Rotate IP addresses with proxies.
Avoid excessive scraping in a short time.
Conclusion
Walmart product review scraping will fetch you insights into customer preferences for products, the effectiveness of products being sold, and market trends; however, use ethical web scrapping practices in terms of service applied by Walmart to its customers.
If you’re looking for an automated and hassle-free way to extract Walmart product reviews, consider using ready-made web scraping tools to simplify the process.
Do you have any questions or need further guidance? Let's discuss it in the comments below!
Know More >> https://scrapelead.io/blog/how-to-scrape-walmart-product-reviews-using-walmart-scraper/