Flipkart Product Reviews Scraper

Collect every written review for any Flipkart product — text, rating, author, date, helpful votes, certified-buyer flag and variant info — in the order you choose: most helpful, most recent, positive first or negative first. Paste product links or bare product IDs and get back a clean row for each review, ready for sentiment analysis, competitor monitoring or voice-of-customer research. Built for anyone who needs Flipkart's customer feedback as data: brands tracking what shoppers say, sellers watching rivals, analysts mining real shopper language and teams feeding review text into topic or sentiment models.

What you can do with it

• Mine customer language — pull the review text, titles and star rating for any product in one dataset, ready for analysis.
• Isolate complaints or praise — sort positive-first or negative-first to surface what delights or frustrates buyers first.
• Monitor a product over time — sort by most recent and schedule the actor to catch fresh feedback as it appears.
• Read Flipkart the way shoppers do — every row carries the review date, helpful votes, whether it was a certified purchase and the variant the reviewer bought.
• Reuse a link you already have — paste full Flipkart product addresses or just the product ID; mixed together in the same list is fine.
• Cap the volume per product — set the maximum reviews per product and the actor stops paging as soon as the cap is reached.

What you get

One row per review. Abridged sample of a single review:

{
  "reviewId": "fk-MOBGTAGQ-p1-i0",
  "productId": "MOBGTAGQ",
  "productTitle": "Apple iPhone 15 (Blue, 128 GB)",
  "rating": 5,
  "title": "Excellent phone",
  "text": "Superb camera and battery life. Delivery was quick and the product was sealed.",
  "author": "Rahul S.",
  "date": "2024-03-15T00:00:00.000Z",
  "helpfulCount": 42,
  "certifiedBuyer": true,
  "variantInfo": "Color: Blue, Storage: 128 GB",
  "images": ["https://rukminim1.flixcart.com/blobio/612/612/imr/blobio-imr_abc123.jpg?q=70"]
}

date is an ISO timestamp when the original wording can be parsed. Flipkart usually states review age relatively ("3 months ago"), which is converted against the run date and so is approximate to that granularity. When a date cannot be parsed, date is empty. Where a review omits a field the actor leaves it empty rather than guessing.

Input reference

Setting	Type	Default	What it does
Product links	list of text	—	Flipkart product links whose reviews you want (flipkart.com/.../p/itm...?pid=...). Mixed with bare IDs is fine.
Product IDs	list of text	—	Alias for the links above: bare product IDs or full links. Combined into one de-duplicated product list.
Maximum reviews per product	whole number	50	Stop after this many reviews per product. Products with fewer reviews simply return what exists. Maximum 2,000.
Review order	choice	helpful	helpful (most helpful first), recent (newest first), positive (praise first), or negative (complaints first).

Provide at least one product link or ID; the actor reads each product's review listing in the order you chose.

Output fields

Field	Type	Description
reviewId	text	The review's stable identifier.
productId	text	Flipkart's product ID the review belongs to.
productTitle	text	Product title as published, when available on the page.
rating	number	Stars given, 1 to 5.
title	text	Review headline / title.
text	text	Full review body.
author	text	Who wrote it, as Flipkart publishes it.
date	text	ISO timestamp parsed from Flipkart's date; empty when the wording cannot be parsed.
helpfulCount	number	How many shoppers found the review helpful.
certifiedBuyer	true/false	Whether Flipkart marks it as a certified purchase.
variantInfo	text	Variant the reviewer bought (color, size, storage), when shown.
images	list of text	Customer-uploaded review photos, when the review has any; empty otherwise.

Example use cases

• Brand listening — pull recent reviews for your products weekly and track what buyers praise or complain about.
• Complaint triage — sort negative-first to surface one-star problems before they spread.
• Launch watch — schedule most-recent reads after a launch and catch fresh feedback daily.
• Competitor comparison — collect helpful-first reviews for rival products and compare recurring themes.
• Review corpus — export up to 2,000 reviews per product with ratings and dates for sentiment or topic models.

Pricing

You pay per result, with no monthly minimum.

What you are charged for	Price
Review — one review added to your dataset	$2.50 per 1,000

Worked example. A run over 5 products collecting 200 reviews each is 1,000 review rows: 1,000 × $0.0025 = $1.00. A quick check collecting 100 reviews costs about $0.25. A daily watch collecting 50 reviews is about $0.13 per run, roughly $3.75 a month.

Limits & what this actor cannot do

• Ratings, review ordering and availability are a snapshot at the moment of collection and keep changing afterwards.
• Only written reviews Flipkart publishes openly are returned. Star-only ratings without review text do not produce a review row.
• There is a practical ceiling per product imposed by what Flipkart will actually serve; the actor stops when the reviews run out rather than inventing more.
• Where a review omits a field the actor leaves it empty rather than guessing.
• Dead or removed products produce no rows and are reported in the run summary rather than charged.
• Speed depends on the size of the job and on Flipkart's own response times; no fixed throughput is promised.
• Flipkart's terms govern automated access. You are responsible for using the data lawfully and in line with the source site's terms, and for handling any personal data in line with applicable privacy law.

FAQ

Do I need a Flipkart account? No. The actor reads only what Flipkart shows the public on its product and review pages.

Does it need my login or password? No. There is nothing to connect and nothing to configure — enter a product link or ID and press start.

Can I paste a product link I already have? Yes. Paste any Flipkart link that contains /p/ and a product id, or a pid= value. A bare product ID works too.

How many reviews can I get per product? Up to 2,000 per product in one run. Flipkart stops serving sooner when the product has fewer, so the actor stops when the reviews run out.

How does sorting work? Most helpful is Flipkart's default ranking. Most recent surfaces fresh feedback first. Positive and negative surface praise or complaints. The option sets the order sent to each review page.

What if a product has no reviews? It is reported as an empty product in the run summary and costs nothing — no review rows are added.

Can I schedule it? Yes. Schedule it hourly, daily or weekly from the Apify console, and each run writes a fresh dataset you can compare against the last — ideal for tracking new feedback over time.

Is the data complete? It is complete for what Flipkart publishes openly on its review pages. Where a page omits a field the actor leaves it empty rather than guessing, and it reports exactly the review rows Flipkart actually serves rather than an estimated total.