Shopsy Reviews Scraper: Extract Product Reviews & Ratings Effortlessly

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What Is Shopsy.in?

Shopsy.in is an Indian e-commerce platform featuring a wide range of products including cosmetics, fashion, electronics, and more. Each product listing includes a dedicated reviews section where customers share detailed feedback, ratings, and experiences. Manually collecting and analyzing hundreds of reviews is labor-intensive — the Shopsy Reviews Scraper automates this process, delivering structured review data ready for analysis.

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Overview

The Shopsy Reviews Scraper extracts comprehensive product review data from Shopsy.in pages, transforming unstructured customer feedback into clean, structured datasets. It is designed for:

• E-commerce analysts monitoring product sentiment and ratings trends
• Market researchers conducting competitive product analysis
• Quality assurance teams tracking customer feedback patterns
• Data scientists building sentiment analysis and NLP models
• Brands tracking competitor product perception

The scraper handles multiple reviews per URL, supports error resilience via ignore_url_failures, and configurable collection limits to match your research scope and budget.

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Input Format

The scraper accepts a JSON configuration object with three main parameters:

{
  "urls": [
    "https://www.shopsy.in/onfroi-lipstick-combo-pack-8-liquid-matte-long-lasting-smudge-proof/product-reviews/itmab6007e15a0be?pid=XLPHE92VVRNRSJFZ&lid=LSTXLPHE92VVRNRSJFZLVB69H&mid=FLIPKART&cat=ShopsyMakeupFragrances&vert=ShopsyLipstick&page=3"
  ],
  "ignore_url_failures": true,
  "max_items_per_url": 200
}

Parameter	Description	Example
urls	Array of Shopsy product review page URLs to scrape. Include full URLs with query parameters.	Review page URLs with product-reviews path
max_items_per_url	Maximum number of reviews to extract per URL (1–200). Controls volume and execution time.	200 for comprehensive collection; 20 for quick samples
ignore_url_failures	Boolean flag. If true, the scraper continues if individual URLs fail; if false, it stops on first failure.	true for reliability in bulk runs

Tip: Use complete review page URLs including pagination parameters. Each URL typically contains 15–30 reviews per page; adjust max_items_per_url accordingly.

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Output Format

Example output record:

{
  "author": "Ruchi Verma",
  "certified_buyer": true,
  "created": "6 months ago",
  "downvote": {
    "type": "VoteValue",
    "count": 21,
    "is_selected": false
  },
  "review_property_map": {
    "v_e_r_i_f_i_e_d__p_u_r_c_h_a_s_e": true
  },
  "review_type_display_text": null,
  "text": "Very nice product 😀 \nI am very happy with this product 🙂",
  "title": "Excellent",
  "total_count": 64,
  "upvote": {
    "type": "VoteValue",
    "count": 43,
    "is_selected": false
  },
  "url": "/reviews/XLPHE92VVRNRSJFZ:2?reviewId=ce2be012-0934-4237-9178-cba7ecf1fe11",
  "user_badge": null,
  "from_url": "https://www.shopsy.in/onfroi-lipstick-combo-pack-8-liquid-matte-long-lasting-smudge-proof/product-reviews/itmab6007e15a0be?pid=XLPHE92VVRNRSJFZ&lid=LSTXLPHE92VVRNRSJFZLVB69H&mid=FLIPKART&cat=ShopsyMakeupFragrances&vert=ShopsyLipstick"
}

Each scraped review returns a structured record with 12+ fields:

Review Content & Author Information

Field	Meaning
Author	Username or display name of the reviewer
Text	Full review text written by the customer
Title	Review headline or summary title
Certified Buyer	Boolean indicating whether the reviewer purchased the product (verified badge)
User Badge	Special badges or labels assigned to the reviewer (e.g., "Trusted Reviewer," "Top Contributor")

Rating & Engagement

Field	Meaning
Upvote	Number of "helpful" votes the review received
Downvote	Number of "not helpful" votes the review received
Total Count	Total engagement count (sum of upvotes and downvotes)
Created	Timestamp when the review was posted

Review Metadata

Field	Meaning
URL	Direct link to the individual review on Shopsy
Review Type Display Text	Classification of the review (e.g., "Positive," "Negative," "Neutral") or star rating displayed
Review Property Map	Additional metadata object containing structured review properties (rating scale, category flags, etc.)

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How to Use

1. Locate review URLs — Navigate to a Shopsy product page and scroll to the "Customer Reviews" section. Click on a review page or filter; copy the full URL from your browser.
2. Prepare configuration — Paste URLs into the urls array. Set max_items_per_url based on how many reviews you need (20 for quick tests, 100–200 for comprehensive analysis).
3. Handle errors — Enable ignore_url_failures: true for bulk runs. This ensures the scraper continues if one URL fails due to network or structure changes.
4. Run the scraper — Submit the configuration and monitor the execution log for status updates.
5. Process results — Export as JSON, CSV, or Excel. Clean and standardize the Created dates and Review Property Map for downstream analysis.

Common best practices:

• Test with 1–2 URLs first to verify output structure.
• Use max_items_per_url: 50–100 to balance completeness and speed.
• Reviews from "Certified Buyer" accounts typically carry more weight in sentiment analysis.

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Use Cases & Business Value

• Sentiment analysis: Train models to classify positive/negative reviews at scale
• Competitor benchmarking: Compare product ratings and customer feedback across competitors
• Quality insights: Identify common product issues or praised features from customer feedback
• Marketing research: Understand customer pain points and messaging opportunities
• Reputation monitoring: Track brand perception changes over time

By automating review collection, you save days of manual work and unlock insights that guide product development, marketing strategy, and customer service improvements.

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Conclusion

The Shopsy Reviews Scraper is an essential tool for anyone serious about e-commerce intelligence. Whether you're conducting market research, analyzing sentiment, or tracking competitor products, this scraper delivers structured review data in minutes. Leverage customer feedback to make data-driven business decisions and stay ahead in the competitive Indian e-commerce landscape.