Flipkart Reviews Scraper

Extract authentic customer reviews from Flipkart products with ease. This powerful tool collects product ratings, review text, author information, dates, and verified purchase status – everything you need for market research, sentiment analysis, or product insights.

What does this scraper do?

This scraper retrieves customer reviews from any Flipkart product page. Simply provide the product review URL, and the scraper will automatically:

• Extract all review details including ratings, titles, and full review text
• Identify verified purchases (Certified Buyer status)
• Capture reviewer names and review dates
• Handle pagination automatically to collect the desired number of reviews
• Deduplicate reviews to ensure clean, unique data
• Save all data in a structured format ready for analysis

Why scrape Flipkart reviews?

Flipkart reviews contain valuable insights for:

• Product Research – Understand what customers love or dislike about specific products
• Competitive Analysis – Monitor competitor products and customer sentiment
• Market Intelligence – Identify trends, common complaints, and feature requests
• Sentiment Analysis – Analyze customer satisfaction at scale
• E-commerce Strategy – Make data-driven decisions about product listings and pricing

Features

✅ Fast & Efficient – Optimized for speed using advanced API and HTML extraction
✅ Reliable – Handles errors gracefully with automatic retries
✅ Accurate – Extracts complete review data including ratings, text, authors, and dates
✅ Scalable – Process single products or multiple products in one run
✅ Clean Data – Automatic deduplication ensures no duplicate reviews
✅ Pagination – Automatically navigates through all review pages

Input Configuration

The scraper accepts the following input parameters:

Required

• Product Review URL (startUrl) – The Flipkart product review page URL
  • Example: https://www.flipkart.com/product-name/product-reviews/itmXXXXXXXXXX

Optional

• Maximum Reviews (results_wanted) – Maximum number of reviews to collect (default: 20)
• Maximum Pages (max_pages) – Safety limit on review pages to visit (default: 20)
• Multiple URLs (startUrls) – Array of review URLs to scrape multiple products
• Proxy Configuration (proxyConfiguration) – Custom proxy settings (residential proxies recommended)

Example Input

{
  "startUrl": "https://www.flipkart.com/adidas-ampligy-m-running-shoes-men/product-reviews/itmab79cd4ce225d",
  "results_wanted": 50,
  "max_pages": 10
}

Output Format

Each review is saved with the following structured data:

Field	Type	Description
product_name	String	Name of the product being reviewed
product_id	String	Unique Flipkart product identifier
review_id	String	Unique review identifier
rating	Number	Star rating (1-5)
title	String	Review headline/title
review_text	String	Full review content
author	String	Name of the reviewer
date	String	Review submission date
verified_purchase	Boolean	Whether the reviewer is a certified buyer
helpful_count	Number	Number of helpful votes
review_images	Array	URLs of review images (if any)
url	String	Source URL of the review

Example Output

{
  "product_name": "adidas ampligy m running shoes men",
  "product_id": "itmab79cd4ce225d",
  "review_id": "abc123xyz",
  "rating": 5,
  "title": "Excellent product!",
  "review_text": "Very comfortable shoes, great for running. Highly recommended!",
  "author": "John Doe",
  "date": "Apr, 2023",
  "verified_purchase": true,
  "helpful_count": 15,
  "review_images": [],
  "url": "https://www.flipkart.com/adidas-ampligy-m-running-shoes-men/product-reviews/itmab79cd4ce225d?page=1"
}

How to Use

On Apify Platform

1. Navigate to the Flipkart Reviews Scraper on Apify
2. Click Try for free
3. Enter the Flipkart product review URL in the Product Review URL field
4. Configure Maximum Reviews and other settings as needed
5. Click Start to begin scraping
6. Download your data in JSON, CSV, Excel, or other formats

Locally via Apify CLI

$apify run

Ensure you have configured your input in INPUT.json before running.

Use Cases

Market Research

Analyze customer feedback to understand product strengths and weaknesses across different categories.

Competitor Monitoring

Track competitor product reviews to identify market gaps and opportunities.

Product Development

Gather customer pain points and feature requests to inform product improvements.

Sentiment Analysis

Build datasets for machine learning models to classify customer sentiment.

Quality Assurance

Monitor your own products for recurring issues or quality concerns.

Performance & Cost

• Speed: Scrapes 20 reviews in approximately 30-60 seconds
• Cost: Minimal – optimized for efficient resource usage
• Proxies: Residential proxies recommended for best reliability

Important Notes

⚠️ Respect Rate Limits – Use reasonable results_wanted values to avoid overloading Flipkart's servers
⚠️ Use Proxies – Residential proxies are recommended for consistent scraping
⚠️ Review URL Format – Ensure you're using the product review page URL, not the product page URL

Troubleshooting

No results returned

• Verify the URL is a Flipkart product review page (should contain /product-reviews/ in the URL)
• Check that the product has reviews available
• Ensure proxy configuration is correct

Scraper times out

• Reduce results_wanted to a smaller number
• Decrease max_pages to limit pagination
• Verify your proxy configuration is working

Missing data fields

• Some reviews may not have all fields (e.g., no title, no images)
• Older reviews might have different data structures

Data Privacy & Ethics

This scraper is designed to collect publicly available data from Flipkart. Please ensure your use case complies with:

• Flipkart's Terms of Service
• Applicable data protection regulations (GDPR, CCPA, etc.)
• Ethical web scraping practices

Support

Need help or have questions?

• 📧 Contact: [your-email@example.com]
• 🐛 Report issues on GitHub
• 💬 Join our community forum

About

This scraper is maintained by Shahid Irfan. It's optimized for reliability, speed, and data accuracy to help businesses make informed decisions based on customer feedback.

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Ready to extract valuable insights from Flipkart reviews? Start scraping now!