Naver Shopping Review Scraper (네이버쇼핑 구매평) | Korean Product Reviews, Photos & Ratings (2026)

Korean products carry a median of 495 reviews. This Actor takes all of them.

⚡ 1,000 reviews in under 30 seconds.

Zen Studio Korea Marketplace Scrapers   •  Product, price and review data from Korea's two biggest storefronts
Naver Shopping Keyword & category search	Naver Detail Specs, sellers, history	Naver Review ➤ You are here
Coupang Search Keyword product search	Coupang Detail Specs, options, sellers	Coupang Review Reviews, photos, ratings

Copy to your AI assistant

zen-studio/naver-review-scraper on Apify. Takes Naver Shopping product URLs or bare IDs and returns one row per customer review in the DEFAULT dataset: reviewText, reviewRating, reviewDate, reviewerId, purchased option, photos and videos. Call ApifyClient("TOKEN").actor("zen-studio/naver-review-scraper").call(run_input={"productUrls": ["https://search.shopping.naver.com/catalog/52631236642"], "maxReviewsPerProduct": 200}), then client.dataset(run["defaultDatasetId"]).list_items().items. Leave maxReviewsPerProduct unset for all reviews. reviewSource says which listing served each row. Full spec: GET https://api.apify.com/v2/acts/zen-studio~naver-review-scraper/builds/default (Bearer TOKEN) → inputSchema, actorDefinition.storages.dataset, readme. Token: https://console.apify.com/account/integrations

Key Features

• Fast enough for bulk work: around 1,000 reviews in under 30 seconds, so a shortlist of products is a coffee break rather than an afternoon.
• Reviews at real depth. Korean marketplace listings are review-heavy in a way most sources are not: a median product carries around 500 reviews, one in ten carries more than 11,000, and the deepest we measured had 572,422. Clear the per-product limit and the Actor takes them all.
• Both kinds of link. A price-comparison link returns the reviews gathered against that listing; a store link returns that seller's own. Whichever you paste is what you get, and reviewSource records it.
• One table, whichever listing answered. reviewText, reviewRating, reviewDate and reviewerId read the same on every row, so a mixed dataset sorts and exports as one sheet. The source's own field names are kept alongside them, so nothing is lost.
• Photos and videos, with hasImages and hasVideos flags and the full attachment URLs.
• The purchased option, which is what makes a review usable for product analysis: which colour, size or variant the reviewer actually bought.
• Filter by kind: all reviews, photo reviews, video reviews, or the ones the source itself scores highest for usefulness.

How to Scrape Naver Shopping Reviews

1. Open the Actor and paste your product links into Product links or IDs.
2. Adjust Max reviews per product. It starts at 30, one page. Set it to 0 to collect every review.
3. Press Start.
4. Download the results as JSON, CSV or Excel, or pull them from the API.

Basic: the top reviews for a product

{
  "productUrls": ["https://search.shopping.naver.com/catalog/52631236642"],
  "maxReviewsPerProduct": 100
}

Every review a product has

{
  "productUrls": ["https://smartstore.naver.com/nestlestore/products/10503912668"],
  "maxReviewsPerProduct": null
}

Photo reviews only, newest first

{
  "productUrls": ["https://brand.naver.com/dunkin/products/10011140914"],
  "reviewType": "PHOTO",
  "reviewSort": "RECENT",
  "maxReviewsPerProduct": 500
}

A shortlist of competing products

{
  "productUrls": [
    "https://search.shopping.naver.com/catalog/52631236642",
    "https://search.shopping.naver.com/catalog/58509823828",
    "https://smartstore.naver.com/nestlestore/products/10503912668"
  ],
  "maxReviewsPerProduct": 300
}

Input Parameters

Field	Type	Default	Description
productUrls	array	required	Naver Shopping product links or bare ID numbers. Price-comparison and store links both work. Max 1,000 per run; duplicates are removed.
maxReviewsPerProduct	integer	30	How many reviews per product, one page by default. Clear the field for every review available, up to 50,000 per product.
reviewSort	string	RANKING	RANKING (top ranked) or RECENT (newest first). On price-comparison listings the two orders reach different reviews.
reviewType	string	ALL	ALL, PHOTO, VIDEO or QUALITY.

What Data Can You Extract from a Naver Shopping Review?

Every review row includes:

• The same four columns whichever listing answered: reviewText, reviewRating, reviewDate, reviewerId.
• Identity and joins: reviewId, productId, catalogId, reviewSource, and sourceUrl, the exact link you supplied, so a multi-product dataset groups without a lookup.
• The purchased option: buyOption on price-comparison rows, productOptionContent on store rows.
• Media: hasImages, hasVideos, and the attachment URLs: images and videos on price-comparison rows, reviewAttaches on store rows.
• Signals: qualityScore and topic tags with sentiment on price-comparison rows, helpCount and the reviewer's badge on store rows.
• Everything else the source publishes, under its own field names.

reviewSource is catalog for reviews served by a price-comparison listing and store for a seller's own. The two surfaces publish different extras, so a row from one carries fields the other does not; the four unified columns above are always present on both.

Output Example: a price-comparison review

This is a complete row: every key and every value a real result carries, at every level of nesting. Only two things are shortened. Arrays are cut to their first item or two, since a 181-point price history or a 97-field seller teaches nothing after the first. And single values longer than 200 characters are middle-ellipsised, which in practice only affects click-tracking URLs. No key is omitted anywhere.

{
  "id": "4703712429-hanloven-10098450194",
  "buyOption": "모델 선택: 실버+SSD256더블업",
  "content": "하나하나 다 따지고 봤는데 이게 가격이 제일 싸고 가성비 있어 보여서 선택하게 되었어요.<br><br>성능도 나쁘지 않았고 세일도 많이 하다보니 눈길이 갔구요.<br><br>주…기다니기에도 편할 것 같습니다.<br><br>삼성의 노예라서 모든 기기를 삼성으로 쓰고 있는 사람이라서.. 본인 핸드폰이 삼성이다 ?? 그러면 100퍼센트 추천드리는 바입니다.",
  "imageCount": 5,
  "isImage": true,
  "images": [
    "https://phinf.pstatic.net/checkout/20250416_271/1744730042745Rew7r_JPEG/1000024798.jpg",
    "https://phinf.pstatic.net/checkout/20250416_137/1744730042500Gvd16_JPEG/1000024799.jpg",
    "https://phinf.pstatic.net/checkout/20250416_67/1744730042479Jt13I_JPEG/1000024801.jpg"
  ],
  "mallName": "삼성mall 한사랑씨앤씨",
  "mallId": "hanloven",
  "mallProductId": "10098450194",
  "mallReviewId": "4703712429",
  "mallSequence": "376289",
  "mallLogoUrl": "https://shopping-phinf.pstatic.net/20241001_23/1c579383-f040-40aa-9b69-9143c78cfa8d.jpg",
  "matchNvMid": "52631236642",
  "modifyDate": "2025-04-16",
  "nvMid": "87642952896",
  "pageUrl": "https://smartstore.naver.com/main/products/10098450194",
  "qualityScore": 0.881631,
  "registerDate": "2025-04-16",
  "starScore": 5,
  "title": "하나하나 다 따지고 봤는데 이게 가격이 제일 싸고 가성비 있어 보여서 선택하게 되었어요.성능도 나쁘지 않았고 세일도 많이 하다보니 눈길이 갔구요.주말에 ssd 더블업 이벤트하길래 ",
  "topicCount": 10,
  "isTopic": true,
  "topics": [
    {
      "topicName": "가격",
      "topicCode": "price",
      "reputationScore": 550,
      "startPosition": 37,
      "endPosition": 66,
      "isPositive": true
    }
  ],
  "userId": "rmld****",
  "videoCount": 1,
  "isVideo": true,
  "videos": [
    {
      "videoId": "B43F2D1B59016C0190EF1ACB85409E055D8A",
      "videoThumbnailUrl": "https://phinf.pstatic.net/image.nmv/shopnbuyer_2025_04_16_917/qfzVQbj1ae_03.jpg"
    }
  ],
  "catalogId": "52631236642",
  "productId": null,
  "reviewSource": "catalog",
  "reviewId": "4703712429-hanloven-10098450194",
  "reviewText": "하나하나 다 따지고 봤는데 이게 가격이 제일 싸고 가성비 있어 보여서 선택하게 되었어요.<br><br>성능도 나쁘지 않았고 세일도 많이 하다보니 눈길이 갔구요.<br><br>주…기다니기에도 편할 것 같습니다.<br><br>삼성의 노예라서 모든 기기를 삼성으로 쓰고 있는 사람이라서.. 본인 핸드폰이 삼성이다 ?? 그러면 100퍼센트 추천드리는 바입니다.",
  "reviewRating": 5,
  "reviewDate": "2025-04-16",
  "reviewerId": "rmld****",
  "hasImages": true,
  "hasVideos": true,
  "scrapedAt": "2026-08-20T12:12:51.181787+00:00",
  "sourceUrl": "https://search.shopping.naver.com/catalog/52631236642"
}

Output Example: a store review

This is a complete row: every key and every value a real result carries, at every level of nesting. Only two things are shortened. Arrays are cut to their first item or two, since a 181-point price history or a 97-field seller teaches nothing after the first. And single values longer than 200 characters are middle-ellipsised, which in practice only affects click-tracking URLs. No key is omitted anywhere.

{
  "id": 5042808714,
  "reviewType": "NORMAL",
  "reviewServiceType": "SELLBLOG",
  "reviewContentClassType": "PHOTO",
  "reviewScore": 1,
  "reviewContent": "스타벅스 브랜드를 믿었는데 정말 정말 정말 실망스럽네요. 유통 기한이 4개월도 남지않은 제품을 라이브 행사하듯이 판매하다니..... 캡슐 구매한지 20년도 넘었지만 이렇게 임박…!!!! 사은품이라는 샘플도 1개는 품질유지기한 2달도 안남은 드립백.... 이건 사은품이 아니라 처분 아닌가요? 차라리 임박상품이라고 할인해서 판매를 하던지 어이가 없습니다.",
  "contentsStatusType": "NORMAL",
  "createDate": "2026-08-13T13:58:47.453+00:00",
  "freeTrial": false,
  "repurchase": false,
  "helpCount": 5,
  "writerId": "snow****",
  "maskedWriterId": "snow****",
  "writerIdNo": "",
  "writerMemberNo": 0,
  "writerProfileImageUrl": "https://profile-phinf.pstatic.net/404/default.png",
  "storeType": "STOREFARM",
  "storeNo": 100586493,
  "checkoutMerchantId": "",
  "checkoutMerchantNo": 0,
  "orderNo": "",
  "productOrderNo": "2026081122629351",
  "productNo": "10503912668",
  "productName": "[슈퍼적립]스타벅스 네스프레소 캡슐 대표5종2세트(하우스 리스트레토 파이크 에스프 헤이즐)",
  "productUrl": "https://smartstore.naver.com/main/products/10503912668",
  "largeCategorizeCategoryId": "50000006",
  "middleCategorizeCategoryId": "50000148",
  "smallCategorizeCategoryId": "50001081",
  "detailCategorizeCategoryId": "50009600",
  "productOptionContent": "▼ 구성 선택: 10개입x10박스 / ▼ 제품 선택: 리스트레토샷 10",
  "knowledgeShoppingMallProductId": "10503912668",
  "originProductNo": 10453374171,
  "reviewAttaches": [
    {
      "id": 3677974611,
      "reviewAttachmentType": "I",
      "attachUrl": "https://phinf.pstatic.net/checkout.phinf/20260813_269/1786629381967DVcsR_JPEG/1000062722.jpg",
      "attachWidth": 3024,
      "attachHeight": 4032,
      "attachPath": "https://phinf.pstatic.net/checkout.phinf/20260813_269/1786629381967DVcsR_JPEG/1000062722.jpg",
      "attachSize": 6778174,
      "attachDescription": "",
      "sortOrder": 0,
      "attachName": "1000062722.jpg",
      "attachDirectoryName": "/20260813_269/1786629381967DVcsR_JPEG"
    }
  ],
  "reviewCommentIds": [
    3177665108
  ],
  "reviewComments": [
    {
      "reviewId": 5042808714,
      "commentId": 3177665108,
      "commentContent": "안녕하세요 고객님, 네슬레 공식몰입니다.\n먼저 소중한 시간 할애하시어 저희 제품 구매해주셨으나 소비기한에 있어 불편을 드려 죄송합니다.\n다만, 소비기한의 경우 구매 전 상세 페이지를 통해 확인이 가능하오니 구매 전 참고 부탁드립니다.\n늘 최선을 다해 만족 드릴 수 있는 서비스 제공하도록 노력하겠습니다.\n저희 네슬레 공식몰샵을 이용해 주셔서 감사드립니다.",
      "createDate": "2026-08-14T00:19:53.057+00:00",
      "writerMerchantId": "",
      "writerMerchantNo": 0,
      "contentsStatusType": "NORMAL",
      "unescapeCommentContent": "안녕하세요 고객님, 네슬레 공식몰입니다.\n먼저 소중한 시간 할애하시어 저희 제품 구매해주셨으나 소비기한에 있어 불편을 드려 죄송합니다.\n다만, 소비기한의 경우 구매 전 상세 페이지를 통해 확인이 가능하오니 구매 전 참고 부탁드립니다.\n늘 최선을 다해 만족 드릴 수 있는 서비스 제공하도록 노력하겠습니다.\n저희 네슬레 공식몰샵을 이용해 주셔서 감사드립니다."
    }
  ],
  "reviewEvaluationValueIds": [
    1000222,
    1000234,
    1000244
  ],
  "subReviewEvaluationValueIds": [],
  "reviewUserInfoValues": [
    {
      "itemId": 104002,
      "itemOptionId": 1,
      "itemValue": "다이어트"
    },
    {
      "itemId": 104002,
      "itemOptionId": 3,
      "itemValue": "저당"
    },
    {
      "itemId": 104002,
      "itemOptionId": 4,
      "itemValue": "저속노화"
    }
  ],
  "reviewTopics": [
    {
      "topicCode": "total",
      "topicCodeName": "만족도",
      "patternStartNo": 24,
      "patternEndNo": 29
    }
  ],
  "reviewContentAnalysisSummaryTags": [
    {
      "tagGroupNo": 101559,
      "representTagNo": 124591,
      "tagPatternStartNo": 0,
      "tagPatternEndNo": 29
    }
  ],
  "eventTitle": "",
  "profileImageSourceType": "DEFAULT",
  "repThumbnailAttach": {
    "id": 3677974611,
    "reviewAttachmentType": "I",
    "attachUrl": "https://phinf.pstatic.net/checkout.phinf/20260813_269/1786629381967DVcsR_JPEG/1000062722.jpg",
    "attachWidth": 3024,
    "attachHeight": 4032,
    "attachPath": "https://phinf.pstatic.net/checkout.phinf/20260813_269/1786629381967DVcsR_JPEG/1000062722.jpg",
    "attachSize": 6778174,
    "attachDescription": "",
    "sortOrder": 0,
    "attachName": "1000062722.jpg",
    "attachDirectoryName": "/20260813_269/1786629381967DVcsR_JPEG"
  },
  "repThumbnailTagNameDescription": {},
  "isMyReview": false,
  "catalogId": null,
  "productId": "10503912668",
  "reviewSource": "store",
  "reviewId": 5042808714,
  "reviewText": "스타벅스 브랜드를 믿었는데 정말 정말 정말 실망스럽네요. 유통 기한이 4개월도 남지않은 제품을 라이브 행사하듯이 판매하다니..... 캡슐 구매한지 20년도 넘었지만 이렇게 임박…!!!! 사은품이라는 샘플도 1개는 품질유지기한 2달도 안남은 드립백.... 이건 사은품이 아니라 처분 아닌가요? 차라리 임박상품이라고 할인해서 판매를 하던지 어이가 없습니다.",
  "reviewRating": 1,
  "reviewDate": "2026-08-13T13:58:47.453+00:00",
  "reviewerId": "snow****",
  "hasImages": true,
  "hasVideos": false,
  "scrapedAt": "2026-08-20T12:12:51.181854+00:00",
  "sourceUrl": "https://smartstore.naver.com/nestlestore/products/10503912668"
}

Advanced Usage

Get past 10,000 reviews on a price-comparison product

A price-comparison listing serves at most 10,000 reviews in any one order, however many it says it has. The two orders reach genuinely different reviews. Measured overlap between Top ranked and Newest first was zero out of 60, so running the same product twice, once in each order, roughly doubles what you can reach. Store listings have no such limit, so a store link is the better route to a very deep pull.

Rating distribution over time

reviewRating and reviewDate are on every row whichever listing answered, so a pivot on month and rating works across a mixed dataset without any cleaning. reviewDate is a calendar date on price-comparison rows and a full timestamp on store rows, as each listing publishes it; both start with the year, so they still sort together.

Which variant disappoints

buyOption and productOptionContent carry the exact variant the reviewer bought. Grouping ratings by option is the fastest way to find the colour, size or bundle that is dragging a product's average down.

Photo and video reviews for creative work

reviewType: "PHOTO" returns reviews with reviewer photos; "VIDEO" returns the ones with video, which are genuinely rare: on a product with 323,490 reviews, only 969 had video. Expect far fewer rows from that filter than from the others.

Feed it from the search Actor

Run the Naver Shopping Scraper on a keyword or category, take the productPageUrl column, and paste it here to collect reviews for a whole shortlist.

How Much Does It Cost to Scrape Naver Shopping Reviews?

Pay per event, so you pay for reviews rather than for run time.

Event	Price per 1,000
Review	$0.99
Actor start	$0.005 per run, per GB
Result (each row stored)	$0.01

You pay for the reviews you get back, so a product with twelve of them costs twelve reviews' worth.

Scheduling and Integrations

Run on a schedule from the Apify Console, or trigger it from the API, the Apify MCP server, Zapier, Make, n8n, Airbyte or a webhook. Results export to JSON, CSV, Excel, XML or RSS, and can be pushed straight into Google Sheets or a database.

FAQ

Which links work? Price-comparison links (search.shopping.naver.com/catalog/…), SmartStore links (smartstore.naver.com/…), brand store links (brand.naver.com/…), mobile versions of any of those, and bare ID numbers.

A price-comparison link and a store link for the same product give different reviews. Which is right? Both. A price-comparison listing has its own review collection; a store listing has the seller's. They are not subsets of one another, and neither is a superset: measured across 32 products that have both, the counts track each other closely and the store side is often the larger. Paste whichever listing you care about.

How many reviews can I get from one product? Store listings: no limit we have found, bounded only by the per-product cap you set, which goes to 50,000. Price-comparison listings: 10,000 per review order, so up to about 20,000 by running both orders.

Why did I get fewer reviews than I asked for? Either the product has fewer, or you used a filter. VIDEO in particular is very selective. The run log reports what was actually collected.

Are the reviews in a separate dataset? No. Reviews are this Actor's output, so they go in the default dataset, one row per review. The Naver Shopping Scraper and Naver Shopping Product Detail Scraper put reviews in a separate reviews dataset instead, because products own their default one.

Does it include reviewer personal data? Reviewer IDs are published by Naver already masked (rmld****). Review text and photos are user-generated content and may contain personal data, so handle them under GDPR, CCPA and Korea's PIPA as applicable.

Is there a free tier? There is no separate free tier. Runs draw on your Apify platform credit like any other Actor, including the free credit every account gets each month.

Can I also get Coupang reviews? Yes, from the Coupang Review Scraper. Naver Shopping and Coupang together cover most of Korean online retail.

Is it legal to scrape Naver Shopping reviews? This Actor collects only publicly visible data, nothing behind a login. You are responsible for complying with Naver's terms of service and with applicable data protection law: GDPR, CCPA, and Korea's Personal Information Protection Act (PIPA). Review content includes personal data such as reviewer names and photos, so treat it accordingly.

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Support

Found a bug, or need a field that is not here yet? Open an issue on the Actor's Issues tab. Questions about a specific run are answered fastest with the run ID.

Legal Compliance

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