Unlock comprehensive insights with our Flipkart Reviews Scraper, engineered to extract authentic product reviews, star ratings, buyer opinions, and sentiment data. Utilizing Flipkart Product Review Data Extraction and Flipkart Rating and Review Scraping, this solution empowers eCommerce businesses to monitor genuine customer feedback, evaluate product performance, and make data-driven decisions. Combined with Indian Ecommerce Review Data Scraping, it ensures structured, actionable insights for optimizing offerings, enhancing customer satisfaction, and staying competitive in India’s online marketplace.
Instantly gather live product reviews, star ratings, and buyer opinions using Flipkart Customer Review Scraping for accurate and timely insights.
Focus on genuine user feedback, distinguishing verified and authentic reviews to enhance decision-making and trust in collected e-commerce data.
Organize and extract complete review information using Flipkart Product Review Data Extraction for seamless analytics and actionable business strategies.
Analyze positive, negative, and neutral feedback across products to understand customer sentiment and refine marketing or assortment strategies effectively.
Leverage Flipkart Review Scraping for Sentiment Analysis to capture star ratings and textual feedback, providing comprehensive understanding of product performance.
Track top-reviewed products and emerging trends in specific categories to optimize listings, promotional strategies, and inventory allocation efficiently.
import requests
from bs4 import BeautifulSoup
HEADERS = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
"Accept-Language": "en-US,en;q=0.9",
}
def scrape_flipkart_reviews(product_url):
response = requests.get(product_url, headers=HEADERS)
if response.status_code != 200:
return None
soup = BeautifulSoup(response.text, "lxml")
reviews = []
review_blocks = soup.find_all("div", {"class": "_16PBlm"}) # Flipkart review container class
for review in review_blocks:
title = review.find("p", {"class": "_2xg6Ul"})
rating = review.find("div", {"class": "_3LWZlK"})
comment = review.find("div", {"class": "t-ZTKy"})
reviews.append({
"Review_Title": title.text.strip() if title else "N/A",
"Star_Rating": rating.text.strip() if rating else "N/A",
"Review_Comment": comment.text.strip() if comment else "N/A"
})
return reviews
# Example Flipkart product URL
flipkart_product_url = "https://www.flipkart.com/product-example/p/itmexample123"
review_data = scrape_flipkart_reviews(flipkart_product_url)
for idx, review in enumerate(review_data, 1):
print(f"Review {idx}: {review}")
Utilize Flipkart Product Feedback Extraction to identify recurring customer preferences and complaints, improving product quality and enhancing buyer satisfaction consistently.
Leverage Flipkart Review Scraping for Sentiment Analysis to quantify positive, negative, and neutral opinions for data-driven marketing decisions effectively.
Apply Indian Ecommerce Review Data Scraping to track brand perception across Flipkart’s marketplace, enabling proactive reputation management and competitive benchmarking.
Use E-Commerce Review Data Scraper India to detect top-performing products and emerging trends, guiding assortment and promotional strategies efficiently.
Schedule recurring crawls using Flipkart Product Data Scraping to fetch live review and rating information efficiently across multiple products.
Apply advanced filters through E-Commerce Review Data Scraper India to extract only relevant reviews for focused and actionable insights.
Leverage Indian Ecommerce Review Data Scraping to continuously track product reviews, ratings, and emerging trends for competitive insights effectively.
Use Flipkart Product Review Data Extraction to compile and consolidate all review information, enabling actionable insights and informed e-commerce strategies.
When using Flipkart Reviews Scraper, ensure adherence to Flipkart’s terms of service and data usage policies, maintaining ethical and legal scraping practices.
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