Meesho Reviews Scraper — Ratings, Comments, Photos & Authors

🛍️ Meesho Reviews Scraper — Product Ratings, Comments, Buyer Photos & Star Distribution

Scrape Meesho product reviews and ratings as clean, structured data — no login, no coding. This Meesho scraper searches Meesho by keyword or takes a list of product URLs and returns every matching product's full review list plus its rating summary: star rating, review text, reviewer name, review photos, helpful votes, review date, and the product's average rating, total ratings and complete 1–5 star distribution.

Export every result to JSON, CSV, Excel, XML, or API for e-commerce intelligence, competitor benchmarking, voice-of-customer (VoC) sentiment analysis, and D2C product research.

Built for Meesho sellers, private-label brands, e-commerce agencies, product researchers, data analysts and NLP / sentiment-analysis teams who need review and rating data in bulk instead of scrolling through product pages by hand.

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⚡ At a glance

Feature	Details
Target Platform	Meesho (meesho.com — India's leading value e-commerce marketplace)
Input Modes	Free-text Search Keywords (searchQueries), direct Product URLs / IDs (productUrls), or both
Pagination & Scale	Automatic cursor-based review pagination + multi-page search discovery — supports 1,000+ to 100,000+ reviews per run
Product Rating Summary	Denormalized on every row: averageRating, ratingCount, reviewCount, ratingScale, ratingDistribution (1–5 stars), totalMediaCount
Review Granularity	reviewId, rating (1–5), comment (full text + emojis), reviewDate, helpfulCount, authorName, images[], imageCount, videoCount, hasMedia
Product Context	productId, productName, productUrl, productPrice (INR), productImage
Deduplication	100% deduplicated by productId + reviewId across all pages and keywords
Export Formats	Bulk export to JSON, CSV, Excel, HTML, XML, or programmatic access via Apify REST API

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🔎 Why scrape Meesho?

Meesho is one of India's largest e-commerce marketplaces, with hundreds of millions of shoppers buying fashion, ethnic wear, electronics, home decor, kitchenware and beauty products — and leaving millions of ratings, written reviews and buyer photos along the way. That review stream is one of the richest, most authentic sources of real voice-of-customer data in Indian e-commerce.

The challenge: Meesho displays reviews a few at a time on each product page behind infinite scroll, with no bulk export option. Copying them by hand is slow, error-prone and impossible at category scale. To analyze hundreds of products — your own catalog or a competitor's — you need the data in a structured, de-duplicated feed.

This Meesho reviews scraper automates the entire workflow. Give it keywords or product URLs and it returns one tidy row per review, each carrying the product's complete rating summary, ready for a spreadsheet, BI dashboard or NLP pipeline.

Common reasons people scrape Meesho:

• 🛡️ Monitor product quality & reduce returns — catch recurring complaints about sizing, stitching, fabric quality, battery life or packaging before they hurt your seller rating.
• 📊 Benchmark competitors — pull rival listings' ratings, review volume, pricing and negative feedback across entire categories.
• 💬 Voice-of-customer (VoC) & NLP research — feed review comments (including Hindi/Hinglish text and emojis) into LLM and sentiment models to quantify buyer satisfaction.
• 📦 Pre-sourcing due diligence — read verified buyer reviews and inspect real customer photos on comparable products before committing to manufacturing or wholesale inventory.
• 📈 Track ratings over time — schedule weekly or daily runs and watch averageRating, ratingCount and the 1–5 star distribution evolve.
• 📸 Customer photo auditing — filter by hasMedia: true to compare real delivered items (images) against studio catalog photos (productImage).
• 🎯 Category gap analysis — compare rating distributions across dozens of top-selling products to discover high-demand niches with poor existing product quality.

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✨ What this Meesho scraper does

• ✅ Two ways to target products — enter free-text keyword searches (kurti, bluetooth earbuds, kids shoes) or paste direct product URLs / IDs (https://www.meesho.com/.../p/fw48iu).
• ✅ Unlocked 1,000+ review pagination — automatically paginates both product search results and each product's full review feed until your maxItems target is reached.
• ✅ Complete rating summary on every row — includes averageRating, ratingCount (total ratings), reviewCount (written reviews), ratingScale and the full ratingDistribution (1 to 5 star counts).
• ✅ Rich review detail — captures rating, full comment text (preserving emojis and regional scripts), reviewDate, helpfulCount, and authorName.
• ✅ Buyer media extraction — extracts all customer-uploaded review photo URLs into images, alongside imageCount, videoCount and a boolean hasMedia flag.
• ✅ Full product context — attaches productId, productName, productUrl, productPrice (INR) and productImage to every review record so multi-product exports can be pivoted immediately.
• ✅ Zero-config empty run support — running with empty inputs automatically samples popular Meesho product categories so you can preview the schema immediately.
• ✅ Export anywhere — download as JSON, CSV, Excel, XML or HTML, or connect directly to Apify API, Google Sheets, Make, Zapier, or webhooks.

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🎯 Use cases

1. Meesho Sellers & Marketplace Managers

Monitor customer feedback across your entire SKU catalog and top category competitors. Identify why specific listings receive 1-star or 2-star ratings (rating <= 2) and fix product descriptions, sizing charts, or packaging to cut return rates.

2. D2C & Private-Label Product Development

Before launching a new kurti line, TWS earbud model, or kitchen organizer, scrape 1,000+ reviews from the top 20 competing products on Meesho. Extract the top buyer pain points and feature requests to build a superior product.

3. E-Commerce Agencies & Marketplace Consultants

Deliver automated competitor benchmarking and category health reports to brand clients, complete with star-distribution breakdowns (ratingDistribution), price positioning (productPrice), and visual review audits.

4. Sentiment Analysis, LLM & AI Training Pipelines

Pipe structured review records (comment + rating + productName) into NLP pipelines, vector databases, or LLM clustering workflows to analyze Indian consumer sentiment across English, Hinglish, and regional languages.

5. Dropshippers, Resellers & Wholesale Buyers

Vet Meesho suppliers and catalogs using real buyer photo evidence (images) and negative-rating ratios ((ratingDistribution["1"] + ratingDistribution["2"]) / ratingCount) before listing items in your store.

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📥 Input parameters

Provide search keywords, product URLs, or both, then set your desired volume. All fields are optional.

Field	Type	Default	Description
searchQueries	array	[]	Product keywords to search on Meesho (prefilled with ["kurti"]). Example: ["kurti", "bluetooth earbuds", "kids shoes"].
productUrls	array	[]	Meesho product URLs (or raw encoded product IDs from /p/{id}) to scrape reviews from directly.
maxProductsPerQuery	integer	0	Maximum products to scrape per keyword. 0 = automatic pagination governed by maxItems.
maxReviewsPerProduct	integer	0	Maximum reviews to collect per product (prefilled to 25 for fast trial runs). 0 = collect all available reviews up to maxItems.
maxItems	integer	1000	Overall cap on the total number of review records saved across all products (prefilled to 25).
maxConcurrency	integer	4	Number of products to process in parallel (1–8). Recommended 2–5.
proxyConfiguration	object	IN Residential	Proxy settings. Indian residential proxies (IN) are pre-configured by default for reliable access.

Example 1 — 1,000+ reviews for a category keyword

{
  "searchQueries": ["kurti"],
  "maxProductsPerQuery": 0,
  "maxReviewsPerProduct": 0,
  "maxItems": 1000,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "IN"
  }
}

Example 2 — Multi-category competitor benchmark

{
  "searchQueries": ["bluetooth earbuds", "smart watch", "neckband"],
  "maxProductsPerQuery": 15,
  "maxReviewsPerProduct": 100,
  "maxItems": 3000,
  "maxConcurrency": 4
}

Example 3 — Deep audit of specific Meesho product URLs

{
  "productUrls": [
    "https://www.meesho.com/green-print-star-shot/p/fw48iu",
    "https://www.meesho.com/gown-for-women/p/88k0ir"
  ],
  "maxReviewsPerProduct": 500,
  "maxItems": 1000
}

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📤 Output & field reference

Each review is saved as one clean dataset item carrying both the individual review details and the parent product's overall rating summary.

Sample JSON output

{
  "productId": "fw48iu",
  "productName": "Abhisarika Graceful Kurtis",
  "productUrl": "https://www.meesho.com/green-print-star-shot/p/fw48iu",
  "productPrice": 181,
  "productImage": "https://images.meesho.com/images/catalogs/497800263/cover/1/2/04ba26b6c8bd77d30df6ae6128638205f9fc8b8b1d206cab4798bdfa1450508a_512.jpg",
  "averageRating": 4.2,
  "ratingCount": 1095,
  "reviewCount": 289,
  "ratingScale": 5,
  "ratingDistribution": {
    "1": 112,
    "2": 40,
    "3": 109,
    "4": 219,
    "5": 615
  },
  "totalMediaCount": 0,
  "reviewId": 1707255551,
  "rating": 5,
  "comment": "The product is awesome, i love this product... Go for it gurlliess. Unexpected 💟🧿😭",
  "reviewDate": "2026-06-11 18:05:14",
  "helpfulCount": 13,
  "authorName": "Soni Agrahari",
  "authorLevel": null,
  "authorFollowers": null,
  "images": [
    "https://images.meesho.com/images/ratings_reviews/8323546047/8364774342/8323546047_8364774342_9d80ca0f84f4c.jpeg"
  ],
  "imageCount": 1,
  "videoCount": 0,
  "hasMedia": true,
  "scrapedAt": "2026-11-02T00:50:16.302Z"
}

🛍️ Product Context Fields

Field	Type	Coverage	Description
productId	string	🟢 High (100%)	Encoded Meesho product identifier (from the /p/{id} URL segment).
productName	string	🟢 High (100%)	Product catalog title.
productUrl	string	🟢 High (100%)	Canonical Meesho product page URL.
productPrice	number	🟢 High (~98%)	Product price in Indian Rupees (INR) when discovered via search.
productImage	string	🟢 High (~98%)	Primary catalog cover image URL when discovered via search.

⭐ Product Rating Summary Fields

Field	Type	Coverage	Description
averageRating	number	🟢 High (100%)	Product overall star rating (decimal, 0–5, e.g. 4.2).
ratingCount	number	🟢 High (~98%)	Total number of star ratings submitted for the product.
reviewCount	number	🟢 High (~98%)	Total number of written reviews for the product.
ratingScale	number	🟢 High (100%)	Maximum rating scale (typically 5).
ratingDistribution	object	🟢 High (~98%)	Star breakdown object with keys "1" through "5" and integer rating counts.
totalMediaCount	number	🟢 High (100%)	Total buyer photos/videos count reported in the summary.

💬 Individual Review & Media Fields

Field	Type	Coverage	Description
reviewId	number	🟢 High (100%)	Unique review identifier (used for strict deduplication).
rating	number	🟢 High (100%)	Star rating given by the buyer on this review (1–5).
comment	string	🟢 High (~95%)	Written review text (null only on rating-only submissions).
reviewDate	string	🟢 High (100%)	Timestamp when the review was posted (YYYY-MM-DD HH:MM:SS).
helpfulCount	number	🟢 High (100%)	Number of helpful upvotes received by the review.
authorName	string	🟢 High (100%)	Reviewer display name (or "Meesho User" when anonymous).
authorLevel / authorFollowers	string / number	🟡 Conditional	Legacy reviewer profile fields (null when not exposed).
images	array	🟡 Conditional (~60%)	Array of high-resolution buyer review photo URLs.
imageCount	number	🟢 High (100%)	Number of photos attached to this review.
videoCount	number	🟢 High (100%)	Number of videos attached to this review.
hasMedia	boolean	🟢 High (100%)	true when the review includes at least one photo or video.
scrapedAt	string	🟢 High (100%)	ISO 8601 extraction timestamp.

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🔁 Pagination & 1,000+ scaling

The Meesho reviews scraper is engineered for both deep single-product audits and broad category-wide extraction:

1. Deep cursor pagination per product — for every product, the scraper steps through Meesho's review pages (20 reviews per batch) until it reaches maxReviewsPerProduct or the end of the product's review stream.
2. Automatic multi-page product discovery — when maxProductsPerQuery: 0 (the default), the scraper automatically paginates through search result pages (offset: 0, 20, 40...) and queues new products until your total maxItems target (including 1,000+ reviews) is reached.
3. Strict deduplication — every review is deduplicated by productId + reviewId, guaranteeing zero duplicate rows across pages or overlapping keywords.

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⚙️ How to use

1. Open Meesho Reviews Scraper in Apify Console and click Try for free.
2. Enter one or more Search keywords (e.g. kurti, saree, bluetooth earbuds) and/or paste Product URLs.
3. Set Maximum reviews (maxItems) — leave maxProductsPerQuery: 0 and maxReviewsPerProduct: 0 to let the scraper automatically paginate until maxItems is satisfied.
4. Keep the pre-configured Indian residential proxy (IN).
5. Click Start, watch reviews stream into the dataset, and export to JSON, CSV, Excel, or API.

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💡 Pro tips for best results

• Breadth vs. Depth:
  • For a broad category sample (e.g., 50 products × 20 reviews each = 1,000 reviews), set maxReviewsPerProduct: 20 and maxItems: 1000.
  • For a deep product audit (every single review on 5 products), paste the URLs into productUrls and set maxReviewsPerProduct: 0, maxItems: 5000.
• Calculate Return-Risk Score: Use ratingDistribution to compute (ratingDistribution["1"] + ratingDistribution["2"]) / ratingCount. Products with >15% 1- and 2-star ratings often suffer from sizing or quality mismatches even if their overall averageRating looks acceptable.
• Filter for Photo Reviews: Filter exported data by hasMedia = true to inspect real customer photos (images) before sourcing a product.
• Keep Indian Residential Proxy Enabled: Meesho restricts non-Indian and datacenter traffic; the default RESIDENTIAL (IN) proxy configuration automatically rotates sessions on any blocked request.

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⚠️ Honest limitations

• Product price/image on direct URL runs: When scraping via searchQueries, productPrice and productImage are populated on ~100% of records from the search catalog. When pasting raw productUrls directly without search, productPrice and productImage are null because the review endpoint returns review and rating-summary data only.
• Anonymous reviewer names: Many Meesho shoppers do not set a public profile name and appear on the platform as "Meesho User".
• Proxy requirement: Indian residential proxies (apifyProxyCountry: "IN") are required for reliable extraction.

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❓ FAQ

How many Meesho reviews can I scrape in one run?

You can collect 1,000+ to 50,000+ reviews in a single run. With maxProductsPerQuery: 0 and maxReviewsPerProduct: 0, the actor automatically paginates across products and review pages until maxItems is reached.

Do I need a Meesho login or seller account?

No login, cookies or seller account are needed.

Does every row include the 1–5 star rating distribution?

Yes. Every review row includes the parent product's averageRating, ratingCount, reviewCount and ratingDistribution ({"1": ..., "2": ..., "3": ..., "4": ..., "5": ...}), making it easy to analyze product-level metrics and review-level text in a single flat table.

Can I scrape reviews for specific products only?

Yes — leave searchQueries empty and paste one or more Meesho product URLs (e.g. https://www.meesho.com/some-product/p/8bhiji) or encoded product IDs (8bhiji) into productUrls.

Why are authorLevel and authorFollowers null?

Those fields are retained in the output schema for backward compatibility; Meesho's current review payload no longer populates gamification levels or follower counts.

How do I export only negative reviews (1 and 2 stars) or photo reviews?

Run the scraper to collect the product's review feed, then filter the dataset in Excel, Google Sheets, SQL, or via the Apify Dataset API on rating <= 2 (for complaint and defect analysis) or hasMedia === true (for customer photo audits).

Can I schedule recurring runs to track rating trends?

Yes. Use Apify Schedules to run your keyword or productUrls list daily or weekly. Because every row is stamped with scrapedAt and carries both averageRating, ratingCount, reviewCount and ratingDistribution, you can easily track velocity and star-rating shifts over time.

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💻 API & programmatic integration

Trigger Meesho Reviews Scraper and pull structured review data directly into your BI tool, NLP pipeline, or e-commerce dashboard via the Apify REST API, Python, or Node.js.

cURL example

curl -X POST "https://api.apify.com/v2/acts/haketa~meesho-reviews-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQueries": ["kurti", "bluetooth earbuds"],
    "maxProductsPerQuery": 0,
    "maxReviewsPerProduct": 0,
    "maxItems": 1000,
    "proxyConfiguration": {
      "useApifyProxy": true,
      "apifyProxyGroups": ["RESIDENTIAL"],
      "apifyProxyCountry": "IN"
    }
  }'

Python (apify-client) example

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "searchQueries": ["kurti", "saree"],
    "maxProductsPerQuery": 0,
    "maxReviewsPerProduct": 100,
    "maxItems": 1000,
}

run = client.actor("haketa/meesho-reviews-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item.get('rating')}★] {item.get('productName')} — {item.get('comment')}")

Node.js (apify-client) example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

const run = await client.actor('haketa/meesho-reviews-scraper').call({
    productUrls: [
        'https://www.meesho.com/green-print-star-shot/p/fw48iu',
    ],
    maxReviewsPerProduct: 500,
    maxItems: 500,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Collected ${items.length} Meesho reviews.`);

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📊 Sample analytics workflows

Workflow	Key Fields to Use	Business Outcome
Defect & Return-Risk Audit	rating <= 2, comment, images[], ratingDistribution	Spot sizing, fabric or electronics defects early to reduce marketplace return rates.
Category Whitespace Discovery	searchQueries, productPrice, averageRating, ratingCount	Identify high-volume product categories (ratingCount > 1000) with mediocre ratings (averageRating < 3.9).
Visual Packaging Verification	hasMedia === true, images[], productImage	Compare real buyer delivery photos against supplier catalog renders before wholesale sourcing.
Weekly Brand Health Tracking	productId, averageRating, reviewCount, scrapedAt	Monitor SKU rating trajectory and review velocity across your catalog and top 10 competitors.

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🔗 Related scrapers

Building an Indian e-commerce or multi-platform review intelligence pipeline? Pair this Meesho scraper with:

• IndiaMART Scraper — Indian B2B suppliers, manufacturers, prices and GST data.
• TradeIndia Scraper — Indian wholesale suppliers, products, phones and emails.
• Trustpilot Reviews Scraper — Company ratings, verified reviews and business replies.
• G2 Reviews Scraper — B2B software reviews, pros/cons and rating breakdowns.
• Klook Reviews Scraper — Activity and tour reviews, ratings and traveler photos.

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📋 Changelog

• v1.2 (2026-11) — Unlocked 1,000+ multi-page product discovery and review pagination (maxProductsPerQuery: 0 and maxReviewsPerProduct: 0 defaults), added automatic category fallback for empty inputs, cleaned dataset_schema.json and output_schema.json, and expanded documentation.
• v1.1 (2026-11) — Added cursor-based review pagination, denormalized 1–5 star ratingDistribution on every row, and enhanced Indian residential session rotation.
• v1.0 (2026-06) — Initial release with keyword search, direct product URL input, and buyer media extraction.