A drop-shipper bets on a $5 LED ring light. The supplier's listing shows 4.7 stars and a few thousand reviews. Looks fine. Then they scroll to the 1- and 2-star rows, and three buyers in three countries are saying the same thing in three different languages: the box smells like solvents on arrival. That detail isn't on the listing summary or in the auto-translated snippet. It's in the original-language review text, in Russian. This is the kind of thing AliExpress review data tells you — but only if you have it offline, in a spreadsheet, with the language column intact. This guide walks you through using the ExportComments AliExpress Reviews exporter to pull the full review history for any product into Excel, CSV, or JSON.

Why export AliExpress reviews

AliExpress reviews are messier and more honest than most e-commerce review sources. Buyers from forty countries leave reviews in their native language. AliExpress shows a machine translation by default — and machine translation cheerfully smooths over the words that actually matter (smell, leak, bent, fake). On top of that, AliExpress collects three sub-ratings buyers can score independently: logistics, communication, and quality. Once the data is offline, the analysis you couldn't do on the page becomes obvious:

  • Dropshipper supplier validation — pull every review for the same SKU across the three or four sellers offering it, filter to rating <= 2, and compare complaint patterns. The supplier whose 1-stars all mention shipping is a different problem than the one whose 1-stars all mention build quality.
  • Original-language vs machine-translated text — keep both columns and check the originals before you trust the translation. AliExpress's auto-translation softens specific complaints into vague ones — "not as expected" in English was often "packet was open and items broken" in the original.
  • Logistics / communication / quality split — sort the sub-rating columns separately. A product can be 4.6 stars overall and still have a 2.9 logistics sub-rating that explains your refund rate.
  • Country-of-buyer pattern — pivot on the buyer-country column to spot regional issues. The same dropshipping product can land fine in Spain and arrive damaged consistently in Brazil.
  • Photo-evidence audit — buyer photos are the highest-trust signal on AliExpress. Pull the photo URL column, sample the 1-star rows, and you'll know in five minutes whether the supplier is shipping the product on the listing.
  • Variation-level quality — pivot on the variation column. The seller's overall 4.6 average can hide a 3.1 average for the "black, USB-C" variant that's actually the one your store sells.

How to export AliExpress reviews — step by step

Step 1: Grab the AliExpress product URL

Open the product page on AliExpress — for example https://www.aliexpress.com/item/1005006012345678.html. Any canonical product URL works. The numeric item ID at the end of the URL is what the exporter uses to address the listing.

Step 2: Paste the URL into the exporter

Head to the AliExpress Reviews exporter and paste the URL into the input field. Validating multiple suppliers selling the same SKU? Switch to bulk mode and paste one URL per line — the run returns one Excel file per URL, bundled in a single ZIP at the end of the job. Each supplier stays in its own sheet, which is exactly what you want when you're about to compare complaint patterns between them.

Step 3: Pick a format

Excel (.xlsx), CSV, or JSON. Excel is the easiest to sort and filter — and you'll be doing both a lot when you sift the 1-star rows. CSV is the safest pick if the export is heading into a BI tool. JSON is the right pick when the data goes into a notebook for clustering or an LLM for theme summarization.

Step 4: Start the export

Click Export. The job runs server-side and paginates through the AliExpress review feed for that item, capturing the original-language text, the machine translation, the language code, the country flag, and the three sub-ratings when the buyer scored them. Larger items with thousands of reviews take a couple of minutes. The file lands in your dashboard and your inbox when the run finishes.

Step 5: Open the file

Open the .xlsx in Excel, Numbers, or Google Sheets. Each row is one review, with the columns described next.

Inside the export — what fields you get

Each row is a single AliExpress review. You get columns for:

  • buyer_name — the display name on the review (often partial or masked by AliExpress).
  • country — the buyer's country (AliExpress shows a flag — we extract the ISO code).
  • rating — the 1–5 star score.
  • review_text — the buyer's original review text (often Russian, Spanish, Portuguese, French, etc.).
  • review_text_en — AliExpress's machine translation when the original isn't in English.
  • language — the original review language code.
  • variation — the variation the buyer chose (color / size / spec).
  • logistics_rating — sub-rating for shipping experience, when present.
  • communication_rating — sub-rating for seller communication, when present.
  • quality_rating — sub-rating for product quality, when present.
  • photos — list of buyer-submitted photo URLs.
  • permalink — direct link to the individual review.
  • created_at — review date.

Common workflows

  • Supplier validation for dropshipping — paste every supplier offering the same SKU into bulk mode. Filter each file to rating <= 2 and compare. The supplier whose 1-stars cluster around "arrived smelling of solvents" or "packaging open" is the one to drop, and you only see that pattern in the original-language column.
  • Original-language audit — sort by language and skim the 1- and 2-star rows in their native script. The auto-translation will say "product was not as expected"; the original will say "the cable melted on the second use." You can't make a sourcing call on the first version.
  • Sub-rating pivot — pivot the three sub-rating columns separately. A 4.7 overall with a 3.0 logistics sub-rating points to a fulfillment problem, not a product problem — completely different fix.
  • Country pattern detection — pivot on country. If 1-stars cluster in Brazil and Mexico while Spain stays clean, the issue is the LATAM logistics chain, not the product.
  • Variation-level quality — pivot on variation. Sellers happily list ten variants with one shared review pool; the variant your store actually sells might be the one dragging the score down.
  • Recurring monitoring — schedule a weekly export on your top SKUs, fire a webhook when a new wave of 1-stars lands, and catch a supplier quality slip before it shows up as a refund spike.

Plan limits and API access

The Free tier returns up to 100 reviews per export — enough to spot-check a supplier or evaluate the format. Personal scales to 5,000 results per export, Premium to 50,000, and Business to 250,000 — enough to capture the full review history of even the deepest AliExpress listings. The same job is available through the REST API and via webhooks for scheduled or pipeline-triggered runs. See pricing for the full breakdown.

FAQ

  • Do I get the original-language review text or only the translation?
    Both. The review_text column is the buyer's original (often Russian, Spanish, Portuguese, French, etc.) and review_text_en is AliExpress's machine translation. Keep both — the translation softens specifics that the original calls out clearly.
  • Are the three sub-ratings always populated?
    Only when the buyer scored them. logistics_rating, communication_rating, and quality_rating appear as separate columns and stay empty for buyers who didn't rate that dimension.
  • Can I see which variant the buyer ordered?
    Yes. The variation column carries the color, size, or spec the buyer picked, so you can pivot reviews by variant and isolate the bad ones.
  • Do I get buyer-submitted photo URLs?
    Yes. The photos column is a list of photo URLs. The exporter doesn't download the image files — keeps the export fast and the sheet small. Open the URLs when you need to verify a complaint.
  • Can I export reviews for many suppliers at once?
    Yes — that's exactly what bulk mode is for. Paste one URL per supplier and the run returns one file per URL in a single ZIP. Compare complaint patterns side by side, drop the bad supplier, keep the good one.
  • Can I schedule a weekly export?
    Yes, on Premium and Business. Pair it with webhook delivery to push new reviews into Slack or BigQuery on a recurring cadence — useful for spotting a supplier quality drop before it shows up as a refund wave.