DEV Community: Leo Lee

How do I analyze Coupang and Olive Young reviews in English (without reading Korean)?

Leo Lee — Sun, 18 Oct 2026 08:03:45 +0000

Short answer: export the reviews with a Coupang (쿠팡) or Olive Young (올리브영) review scraper, then label each one by complaint type, sentiment and purchase motive with English keys — and count the labels instead of translating every review. Korean Review Classifier does the labelling step for any review scraper's dataset. It costs $0.50 per 1,000 reviews, with no prompts and no LLM key. It does not scrape the stores itself.

Korean reviews are long, full of slang, and often mix several complaints in one line. A team that does not read Korean usually ends up with a machine translation it still has to read row by row. Labels with probabilities can be filtered and charted instead.

What you get

Rows from the classifier's public example run (2026-10-04), with one custom label, "mentions the smell". English in parentheses is added for readers — the tool returns the original Korean text.

text (English added)	complaint (probability)	sentiment	motive	mentions the smell
한 달도 안 돼서 충전이 안 됩니다. 교환 요청했는데 답이 없어요. (Stopped charging in under a month. Asked for an exchange, no reply.)	quality_defect (0.95), customer_service (0.92)	negative	unknown	false
배송이 일주일이나 걸렸고 박스가 다 찢어져서 왔어요. (Delivery took a week and the box arrived torn.)	packaging (0.97), delivery (0.96)	negative	unknown	false
향이 너무 강해서 두통이 와요. 가격 대비 별로입니다. (The scent is so strong it gives me a headache. Not worth the price.)	other (0.76), price_value (0.74), skin_trouble (0.70)	negative	unknown	true
항상 쓰던 거라 이번에도 재구매했어요. 할인할 때 사니 좋네요! (My usual product, bought it again. Nice to get it on sale!)	none (0.98)	positive	repurchase	false

Complaint types are delivery, quality/defect, size/fit, price/value, customer service, packaging, no effect, skin trouble and other — each with a probability, and each with a Korean name too (quality_defect / 품질·불량), so the same table works for a Korean team. Purchase motive covers price/discount, reviews and reputation, brand, gift and repurchase.

How to set it up (5 minutes)

Export the reviews. Run a review scraper on Apify Store. Field detection was checked against real output of, for example, Coupang Review Scraper (zen-studio), Olive Young Review Scraper (cocomoon) for the Korean store, and Olive Young Review Scraper (solidcode) for Olive Young Global.
Open the classifier on Apify: Korean Review Classifier.
Pass the scraper's dataset. The text, rating and ID fields are detected automatically. Add up to 10 yes/no labels of your own, in English or Korean:

   {
     "datasetId": "YOUR_REVIEW_DATASET_ID",
     "extraTextFields": ["title"],
     "customLabels": ["mentions the smell", "says they will buy again"]
   }

To try it without a dataset, the form default classifies two pasted reviews for about $0.001.

Filter and count. Sort by complaint type to see why buyers leave 1-star reviews, or compare the complaint mix of your product against a competitor's.
Schedule it with Apify Schedules to classify a scraper's new reviews every day.

For scale: 1,000 reviews cost $0.50 and 10,000 cost $5.00; Apify's free $5 monthly credit covers about 10,000 reviews. About 100 reviews take 5 seconds. Rows without review text, such as the summary rows some scrapers add, are skipped and not charged.

You can also ask Claude, Cursor or Claude Code through the Apify MCP server — "Classify dataset abc123 from my Coupang scraper run and summarize the complaints." Setup is on the tool page.

How accurate is it?

On 100 hand-labelled Korean reviews from Coupang, Naver Shopping and Naver Place (47 of them with 1–4 stars), complaint type matched 88% of the time and sentiment 96%. It is built and tested for Korean; English reviews also work. Automated labels can be wrong — check a sample before a big decision.

Other ways, and their limits

Machine-translate and read. Works for a few dozen reviews. Past that, you are still reading every row, and translation flattens the slang that carries the complaint.
Count star ratings. Fast, but a 4-star review can still mention skin trouble, and stars do not say whether the problem was delivery or the product.
Prompt a general chatbot. It can summarize a pasted batch, but you maintain the prompt and turn free text back into a table. This tool returns the same labels with probabilities every run.

Independent tool — not affiliated with Coupang, CJ Olive Young, NAVER Corp. or the authors of the scrapers above. More Naver, AliExpress and K-beauty data tools: leoworks.kr/tools.

How do I find what customers complain about in Google Maps reviews (without reading them all)?

Leo Lee — Sun, 18 Oct 2026 08:02:45 +0000

Short answer: scrape the reviews, then label each one by complaint type, praise, seriousness and whether it still needs an owner reply — and read the totals instead of the stars. Google Maps Review Classifier does the labelling step for any Google Maps reviews scraper's dataset, and adds a reply queue and a per-place summary. It costs $0.50 per 1,000 reviews, with no prompts and no LLM key.

We tried it on 10 dentists in Chicago, using the newest 50 reviews of each (500 reviews; 377 had text). The average rating was 4.45 stars. Under that average, 15.9% of the reviews with text contained a complaint, and 19 were flagged as serious.

What the stars hid

Complaint types across the 377 reviews (a review can have more than one):

Complaint type	Reviews	Share
Staff	30	8.0%
Booking	27	7.2%
Price	22	5.8%
Trust (claims of overtreatment or upselling)	20	5.3%
Wait	19	5.0%
Quality of care	16	4.2%

Praise ran the other way: staff were praised in 313 reviews (83%), quality of care in 206 (55%) and booking in 93 (25%). Sentiment was negative in 13.3% of reviews.

The 19 serious-issue alerts were all patients' claims — of misdiagnosis, overtreatment, upselling or a mistake during an extraction. One said they "insisted I needed a deep cleaning for $3k." These are claims in reviews, not established facts, but they are the ones an owner would want to see the same day.

Unanswered reviews matter more than the average

57 reviews needed an owner reply, and 34 of them were still unanswered. Overall, owners replied to 57.3% of reviews. The practices are anonymized below (A–E are five of the ten):

Practice	Reviews	Avg. stars	With a complaint	Serious alerts	Unanswered (needs reply)	Owner reply rate	Most common complaints
A	42	3.71	31%	2	13	0%	wait, staff
B	41	4.10	24%	7	1	95%	trust, price
C	39	4.18	26%	1	9	8%	booking, staff
D	29	4.45	24%	2	0	97%	wait, booking
E	26	5.00	0%	0	0	100%	—

Practice A never replied and had the lowest average. B had the most serious alerts but answered nearly everything. The last 90 days looked like the period before (complaints 15.5% vs 16.2%), so this was not a recent dip.

How to do it for your own places (5 minutes)

Scrape the reviews. Run a Google Maps reviews scraper for the places you care about — for example Google Maps Reviews Scraper with Sort by: newest. We kept reviewer personal data off.
Open the classifier on Apify: Google Maps Review Classifier, and pick that run's dataset.
Optional filters and labels. Only reviews newer than 90 days, or only 1–3 stars; add up to 10 yes/no labels of your own, like "mentions parking". Skipped reviews are not charged.
Read the results. Open Needs a reply or download REPLY_QUEUE.csv (unanswered reviews, lowest stars first) and REPORT_PLACES.csv (complaint and praise mix, reply rate and trend per place).
Schedule it with Apify Schedules — for example, new reviews every week.

For scale: our 500 reviews cost about $0.29 for the scraper run and $0.19 to classify the 377 with text — about $0.48 in total. Star-only ratings are skipped and not charged.

You can also ask Claude, Cursor or Claude Code through the Apify MCP server — "Which of these reviews still need a reply, and what are they about?" Setup is on the tool page.

How accurate is it?

On 100 hand-labelled English Google Maps reviews of Chicago businesses that were not used for tuning, complaint types matched 89% of the time, sentiment 98% and praise 95%. Serious-issue alerts found 10 of 10, with 1 false alarm in 93. Other languages are accepted but not yet measured, so treat them as beta. Automated labels can be wrong; check a sample before you act on one review.

Other ways, and their limits

Read the reviews yourself. Works for one place and a few dozen reviews. It does not scale to ten places every week, and the average star rating hides the one-in-six reviews with a complaint.
Use the platform's own review inbox. Good for replying, but it does not group complaints by type or compare places.
Paste reviews into a chatbot. Fine for a quick read, but you maintain the prompt and turn free text back into a table yourself. This tool returns the same labels with probabilities every run.

Independent tool — not affiliated with Google or the authors of the scraper above. Practices are anonymized; review quotes are patients' claims, shown as short examples. More data tools: leoworks.kr/tools.

How do I find the buying-intent comments on my Instagram and TikTok posts?

Leo Lee — Fri, 09 Oct 2026 13:02:35 +0000

Short answer: export the comments with a comment scraper, then label each one by what it is — purchase intent, question, complaint, request, praise or spam — and filter. Social Comment Classifier does the labelling step: it takes the dataset from an Instagram, TikTok, Facebook or YouTube comment scraper and returns a type, a sentiment and a "needs reply" flag for every comment. It costs $0.50 per 1,000 comments, with no prompt writing and no LLM key.

"Price?", "link?" and "when is the restock?" are easy to miss under a few hundred emoji and "love this". Those are the comments closest to a sale, and the ones a brand most wants to answer first.

What you get

Real rows from a run on 2026-10-09 — public comments from the four comment scrapers listed below, with one custom label ("mentions shipping or delivery"):

comment	source	type (probability)	sentiment	needs reply	custom: shipping
When will you restock Generation G Fuzz?	Instagram	purchase_intent (1.00)	neutral	yes	no
Link	Facebook (live shopping)	purchase_intent (0.56)	neutral	no	no
How much do they hold?	Instagram	question (1.00)	neutral	yes	no
…still am waiting for my package…	TikTok	complaint (1.00)	negative	yes	yes
can y'all make the resurfacing retinol serum in a bigger bottle ?	TikTok	request (1.00)	neutral	yes	no
obsessed with every single one of these!	Instagram	praise (1.00)	positive	no	no
For me personally… I do buy quality fakes/knockoffs at (shop name)	YouTube	spam (0.72)	neutral	no	no

Each comment gets one main type, a probability for every type (typeScores), positive / neutral / negative sentiment, and whether the brand or creator should answer. Rows keep the comment ID and post fields from the scraper (id, cid, postUrl, videoWebUrl, …), so you can jump back to the comment.

When a comment fits several types, one wins in a fixed order — spam, then purchase intent, complaint, question, request, praise. "Love it! Where can I buy it in the UK?" is purchase_intent, not praise.

Each run also saves a free summary by post: the comment-type mix, sentiment shares, the share of comments that need a reply, and up to 3 example comments for purchase intent, questions, complaints and requests.

How to set it up (5 minutes)

Export the comments. Run one of the common comment scrapers on Apify Store. Field detection was checked against real output of each: Instagram Comments Scraper, TikTok Comments Scraper, Facebook Comments Scraper, YouTube Comments Scraper.
Open the classifier on Apify: Social Comment Classifier.
Pass the scraper's dataset. The text, post title and ID fields are detected automatically. Add up to 10 yes/no labels of your own in plain language if you need them:

   {
     "datasetId": "YOUR_COMMENTS_DATASET_ID",
     "customLabels": ["mentions shipping or delivery"]
   }

To try it without a dataset, the form default classifies two pasted comments for about $0.001.

Filter the results. purchase_intent is your sales list. Needs reply = yes is your answer queue. Complaints and requests can go to support and the product team.
Schedule it with Apify Schedules — for example, classify new comments every morning.

For scale: 10,000 comments cost $5.00, and about 100 comments take 3–4 seconds. Apify's free $5 monthly credit covers about 10,000 comments. Comments without text are skipped and not charged.

You can also ask Claude, Cursor or Claude Code through the Apify MCP server — "Classify dataset abc123 from my TikTok comments run and list the unanswered questions." Setup is on the tool page.

How accurate is it?

On 100 hand-labelled public English comments that were not used for tuning, the main type matched 90% of the time and sentiment 87%. Complaints were found 97% of the time and questions 100%. Purchase intent was found 71% of the time, but that set had only 7 purchase-intent comments, so treat it as a rough number. Very short comments without context ("No", "Vibes") are the hardest.

English is measured. Other languages are accepted and usually work, but their accuracy has not been measured — treat them as beta. Automated labels can be wrong; check a sample before a big decision.

Other ways, and their limits

Read the comments yourself. Fine for one post. It stops scaling once you have several posts a week, and the "link?" comments are buried in praise.
Search for keywords like "price", "link" or "buy". Quick, but it misses "when is the restock?" and "do you ship to Canada?", and catches praise that happens to contain the word.
Prompt a general chatbot. It can label a pasted batch, but you write and maintain the prompt and parse free text back into a table. This tool returns fixed labels with probabilities instead.

Independent tool — not affiliated with Meta (Instagram, Facebook), TikTok, Google (YouTube) or the authors of the scrapers listed above. The comments shown are public comments used as examples. More data tools for e-commerce and social listening: leoworks.kr/tools.

How do I track K-beauty bestsellers on Olive Young Global and Amore Mall every day?

Leo Lee — Fri, 09 Oct 2026 12:28:39 +0000

Short answer: both stores publish bestseller rankings you can read without an account — the work is checking them every day and keeping the history. OliveYoung Global & Amore Mall Rankings + Reviews reads the rankings by category, returns rank, rank change since the last run, price, rating and review count, and can add the newest reviews with complaint labels. It costs $2 per 1,000 ranking rows, $1 per 1,000 reviews and $0.50 per 1,000 review classifications. No login.

Olive Young Global (global.oliveyoung.com) is the international shop of Olive Young (올리브영) and ranks products across many K-beauty brands. Amore Mall (아모레몰) is Amorepacific's own store, so its ranking covers the group's brands — Sulwhasoo, Laneige, Hera, IOPE, innisfree and others. Watching both shows what sells across brands and what sells inside one brand house.

What you get

Real rows from a run on 2026-10-04 (Olive Young Global, "All" bestsellers, 20 reviews per product, classification on).

Ranking rows:

rank	brand	product	price	rating	reviewCount
1	MEDIHEAL	Essential Mask Sheet 10ea Set (+1ea)	$13.50 (50% off $27)	4.9	7,761
2	FOODOLOGY	Coleology Cutting Jelly 30 Sticks	$45.21	4.7	2,145
3	fwee	3D Voluming Gloss Set	$11.93	4.8	1,635

Each ranking row also carries the Korean product and brand names (productNameKo, brandKo — e.g. 메디힐 for MEDIHEAL), original price, discount, stock status, product URL and image. From the second run on, previousRank, rankChange and isNew show what moved and what is new.

Review rows (English in parentheses added for readers):

brand	rating	language	text (first words)	complaint (probability)	sentiment	motive
AESTURA	4	en	Feels heavy, I would only recommend it to people who have dry skin. May break you out.	skin_trouble (0.65), other (0.54)	negative	unknown
AESTURA	4	en	Repurchase. Good to calm the skin. Cream can feel a little thick	other (0.80)	positive	repurchase
MEDIHEAL	5	ja	何回もリピートするくらい好きなパックです！… (A mask I love enough to repurchase again and again!)	none (0.98)	positive	repurchase

Note the first row: a 4-star review that still flags possible skin trouble. Star ratings alone would have hidden it. Amore Mall reviews also include the reviewer's age group and skin type and short survey answers (moisture, scent, mildness).

When reviews are collected, the run also saves a free per-product summary — average rating, complaint rate, complaint mix, sentiment shares and the three strongest complaint reviews.

How to set it up (5 minutes)

Open the tool on Apify: OliveYoung Global & Amore Mall Rankings + Reviews.
Pick the rankings — Olive Young Global categories (Skincare, Makeup, Suncare, Face Masks, …) and/or Amore Mall (most purchased or most clicked; daily, weekly or monthly):

   {
     "sources": ["oliveyoung_global", "amoremall"],
     "oliveyoungCategories": ["skincare", "suncare"],
     "amoremallRankings": ["best_purchased"],
     "maxProductsPerRanking": 30,
     "reviewsPerProduct": 50,
     "classifyReviews": true
   }

Set depth — products per ranking (up to the top 100) and reviews per product (0 = rankings only). The form default — top 5 products with their 5 newest reviews, classified — costs about $0.05.
Optional: only your brands. Keep just your brand and competitors; the whole top 100 is still searched, so the ranks stay real.
Schedule it daily and read the results as a table in Apify (Rankings and Reviews tabs), download them, or pull them by API.

For scale: the daily top 50 of 3 categories for 30 days, without reviews, is $9.00; the top 20 with 20 classified reviews each is $0.64 per run. Failed requests are reported with an error row and not charged.

You can also ask Claude, Cursor or Claude Code through the Apify MCP server — "Get today's top 10 skincare bestsellers on Olive Young Global with their newest 5 reviews and tell me which products have skin-trouble complaints." Setup is on the tool page.

How accurate are the labels?

On hand-labelled Olive Young Global reviews (about half with 1–3 stars), complaint type matched 87% on English reviews the tool hadn't seen before and 93% on Japanese reviews. Reviews in other languages are collected but not classified, and not charged for classification. Automated labels can be wrong — check a sample before a big decision.

Other ways, and their limits

Check the ranking pages yourself. Easy for a glance, but rankings update during the day and you keep no history of what moved.
Read reviews product by product. Fine for one product; slow when you want to compare the complaint mix across the top 20.
Olive Young Korea (oliveyoung.co.kr). This tool doesn't cover it — the Korean store blocks automated access. If you already have Korean reviews from another scraper, Korean Review Classifier adds the same complaint, sentiment and motive labels.

Independent tool — not affiliated with CJ Olive Young or Amorepacific. More Naver, AliExpress and K-beauty data tools: leoworks.kr/tools.

How do I check whether my brand shows up in Naver's AI Briefing (Korea's AI search answer)?

Leo Lee — Fri, 09 Oct 2026 12:27:27 +0000

Short answer: run your keywords through Naver search logged out and read the AI answer at the top — or let a scheduled tool do it every day. Naver AI Briefing Monitor checks each keyword, tells you whether Naver showed an AI briefing, and returns the answer, the sources it cites, any ads inside it, and whether your brand and your competitors are mentioned or recommended. It needs no Naver login and costs $3 per 1,000 queries checked, plus $12 per 1,000 briefings captured.

Naver is Korea's #1 search engine, and its AI briefing (AI 브리핑) is the generated answer at the top of the results page — Naver's version of Google AI Overviews. If you sell in Korea, it is the first answer many shoppers read, so it is worth knowing what it says about your category.

What the tool reads

For each query you give it, the tool loads Naver search (PC by default, mobile optional) and records:

Whether a briefing appeared (hasBriefing). Not every query gets one. In a test of 20 queries on 2026-10-02, 8 of 20 showed a briefing — mostly informational, how-to, review and comparison queries, rarely bare "OO 추천" (OO recommendations) or brand-name-only queries.
The answer text and the short highlighted answer when there is one.
Cited sources — title, URL and platform (Naver Blog, Naver Clip, news sites, other websites).
Ads Naver put inside the briefing, if any.
Brand judgments — for each brand and competitor you list: mentioned or not, recommended or not, sentiment, and how many cited sources name it.

What you get

Real rows from a run on 2026-10-06 (brand 다이슨 / Dyson, competitors 삼성 / Samsung and LG), plus one row from a 2026-10-04 run. English in parentheses is added for readers — the tool returns the query and answer in Korean, as Naver shows them.

query	hasBriefing	다이슨 (Dyson) mentioned / recommended	삼성 · LG mentioned / recommended	citations
무선청소기 추천 2026 (cordless vacuum recommendations 2026)	true	true / true (positive)	true / true · true / true	5
콜라겐 효과 (collagen benefits)	true	false / false	false · false	4
레티놀 부작용 (retinol side effects)	false	—	—	0

In the vacuum query, 4 of the 5 cited sources were Naver Blog posts and one was danawa.com (a Korean price-comparison site), and each of the three brands was named in one cited source. The collagen answer cited hospitals and a medical news site, and none of the three brands appeared — which is what you'd expect, and the rows say so plainly instead of leaving a blank. A query without a briefing comes back with hasBriefing: false and is charged only the basic check.

How to set it up (5 minutes)

Open the tool on Apify: Naver AI Briefing Monitor.
Add queries — the keywords your buyers search. Informational, how-to, review and comparison queries get briefings more often than bare "recommendation" or brand-name-only queries:

   {
     "queries": ["무선청소기 추천 2026", "콜라겐 효과"],
     "brandNames": ["다이슨"],
     "competitorNames": ["삼성", "LG"]
   }

Use brand names (삼성, LG) rather than product-line names — the AI answer usually names the brand. Up to 10 brands and competitors per run.

Run it once to see which of your queries get a briefing at all. The form default (two queries, no brands) costs about $0.03.
Schedule it daily. Answers and cited sources change over time; a daily run turns the dataset into a history of who Naver's AI mentions.
Read the results as a table in Apify, download them, or pull them by API.

For scale: 20 queries checked daily for 30 days, with about 40% of them showing a briefing, is $4.68. Queries that fail to load are reported with an error field and not charged.

If the briefing cites Naver Blog posts — as in the vacuum example — Naver Blog Brand Monitor can tell you which of those posts are sponsored.

You can also call the tool from Claude, Cursor or Claude Code through the Apify MCP server — ask "Check whether 다이슨 is mentioned in Naver's AI answer for 무선청소기 추천 2026 and list the cited sources" and the agent runs it. Setup is on the tool page.

How accurate is it?

Briefing presence, answer and cited sources matched what a browser showed for 20 test queries. The mention / recommendation judgments agreed with hand labels on 97% of 38 judgments. That is a small test, and answers can differ between sessions — read the trend over days, not one check.

Other ways, and their limits

Search it yourself. Fine for a handful of keywords. It doesn't scale to daily checks across a category, and a logged-in browser can show you a different page than a new visitor sees.
Screenshot and compare by hand. Works for a one-off report, but you lose the structure — which source was cited, which brand was recommended — that you need to compare week to week.
Track only your classic rankings. Useful, but the AI answer sits above them and cites its own sources — in the vacuum example above, four of five were blog posts.

Independent tool — not affiliated with NAVER Corp. AI briefings are generated by Naver and may contain inaccuracies. More Naver, AliExpress and K-beauty data tools: leoworks.kr/tools.

How can I find an AliExpress product's real complaints before I order or dropship it?

Leo Lee — Tue, 06 Oct 2026 05:28:32 +0000

Short answer: read the reviews — but read them by complaint type, not by star rating. A 5-star review can still mention a charging base that runs down fast. AliExpress Reviews Scraper pulls a product's reviews with AliExpress' English translation, then labels each one with complaint types, sentiment and purchase motive, so you can count why buyers are unhappy instead of scrolling. It costs $1.50 per 1,000 reviews, plus $0.50 per 1,000 if you turn on the labels.

Why star ratings aren't enough

Two things hide the problems you care about as a sourcer:

Mixed reviews get high stars. Buyers often give 5 stars and then mention a defect in passing.
Reviews are in many languages. A listing shipped worldwide collects reviews in Spanish, Ukrainian and many other languages. AliExpress translates them, but you still have to read them one by one.

What you get

Real rows from a run on 2026-10-05 (1,000 reviews of one earbuds listing, labels on). Text is shortened here:

stars	country	AliExpress English translation	complaint type (probability)	sentiment
1	US	"It arrived completely scratched and as if it had already been used."	quality/defect (0.98)	negative
5	MX	"…not made of the best quality material, but for their price they are quite good. … their charging base discharges very quickly."	quality/defect (0.86), other (0.56)	positive
5	US	"Everything has arrived. The gift was won on Aliexpress."	none (0.98)	positive

The second row is the point: 5 stars, positive overall, and still a quality complaint you'd want to know about before ordering 500 units.

Each row also keeps the original text, date, the option bought (color/size), photos, AliExpress' aspect ratings (e.g. "Quality of sound: Poor") and helpful votes. Complaint types are: quality/defect, size/fit, delivery, packaging, price/value, customer service, no effect, skin/body reaction, other.

Each run also saves a per-product summary at no extra charge: the product's total review count, average rating and star distribution, plus — for the reviews you collected — the complaint rate and mix, sentiment shares, purchase motives and the 3 strongest complaint reviews.

How to do it (5 minutes)

Open the tool on Apify: AliExpress Reviews Scraper.
Paste product URLs or IDs — any AliExpress site works (aliexpress.com, aliexpress.us, ko.aliexpress.com…):

   {
     "productUrls": ["https://www.aliexpress.com/item/1005008549911526.html"],
     "reviewsPerProduct": 20
   }

That first run is about $0.04 and 10 seconds, with labels on by default.

Go bigger for a sourcing decision — 200 reviews from each of a few competing listings. 10 products × 200 labelled reviews is about $4.
Open the per-product summary (Output tab → Summary by product) and compare complaint rates across listings. Read the 3 strongest complaints for each.
Optional: schedule it weekly on your own listings to catch new complaints early.

Don't have product URLs yet? AliExpress Search Scraper returns search results per keyword with rank, price, sold count and rating; pass its productUrl values to the review tool.

How accurate are the labels?

On 30 hand-labelled AliExpress reviews the tool hadn't seen before (earbuds, phone cases, dresses, kitchen tools, LED strips, car holders; about half with 1–3 stars), complaint type matched 93% and sentiment 87%. That's a small test and automated labels can be wrong — check a sample of rows before a big order.

Other ways, and their limits

Read reviews on the product page. Works for one listing. AliExpress doesn't let you sort reviews by date or stars, so finding the bad ones in 1,000 reviews takes a while.
Export reviews and paste them into a chatbot. Possible for a few dozen; long lists get cut off, and the answer can change each time you ask.
Filter by 1–2 stars only. Faster, but you miss complaints inside 4–5 star reviews — like the charging base above.

Independent tool — not affiliated with AliExpress or Alibaba Group. More Naver, AliExpress and K-beauty data tools: leoworks.kr/tools.

How do I track where my product ranks on Naver Shopping (without a Naver login)?

Leo Lee — Tue, 06 Oct 2026 05:26:16 +0000

Short answer: search your keyword on Naver from a phone and count — or let a scheduled scraper do it every day. Naver Shopping Rank Tracker checks the shopping section of Naver's mobile search for each keyword and returns your product's organic rank, its ad slot if it has one, and the change since the last check. It needs no Naver login and costs $3 per 1,000 keyword checks.

For brands, agencies and Smartstore sellers in Korea, the shopping section of Naver search (네이버 쇼핑) is the product list shoppers see first when they search on Naver from a phone — so that is the shelf to watch.

What "rank" means here

When someone searches a product keyword in the Naver app or mobile site, the integrated search page shows a shopping section: a list of products, with sponsored slots mixed in. That is the list this tool reads.

Organic rank — position among non-ad products, 1 = top. Naver shows about 35 organic products per keyword, so ranks 1 to roughly 35 are available.
Ad rank — position among sponsored slots, if your product is advertised.
Visual position — where the card sits among all cards (ads included), as a shopper scrolls.

It is not the ranking on the separate price-comparison site (search.shopping.naver.com).

What you get

Real rows from a run on 2026-10-04 (English in parentheses added for readers — the tool returns the keyword as you typed it):

keyword	match	found	organicRank	rankChange	price (KRW)	reviewCount	rating
무선이어폰 (wireless earbuds)	브리츠 공식몰 (a store name)	true	10	+1	39,900	5,526	4.68
무선이어폰	a catalog URL	true	2	— (first run)	130,830	8,263	4.9
블루투스 이어폰 (bluetooth earphones)	브리츠 공식몰	true	6	— (first run)	39,900	5,526	4.68
텀블러 (tumbler)	스탠리 (a store name)	false	—	—	—	—	—

When a product isn't found, the row says how deep Naver's list went (notFoundWithin: 36), so "not ranked" never looks like an error. Each row also has the product title and URL, purchase count, page number and check time in UTC and Korea time.

How to set it up (5 minutes)

Open the tool on Apify: Naver Shopping Rank Tracker.
Add targets — one keyword plus what to look for. The match can be a Smartstore or brand-store product URL, a catalog URL, a numeric product ID, or a store name:

   {
     "targets": [
       { "keyword": "무선이어폰", "match": "브리츠 공식몰" }
     ]
   }

That one check costs $0.003.

Optional: competitors — add "topCompetitors": 10 to also get the top 10 organic products for each keyword, with price, reviews and rating ($0.50 per 1,000 competitor rows).
Schedule it daily. From the second run on, every row shows rankChange and previousOrganicRank, so the dataset becomes a rank history.
Read the results as a table in Apify, download them, or pull them by API.

For scale: 50 keywords checked daily for 30 days is $4.50, and the free $5 monthly Apify credit covers about 1,666 checks. Keywords that fail after retries are reported with an error field and not charged.

You can also call it from Claude, Cursor or Claude Code through the Apify MCP server — ask "Where does 브리츠 공식몰 rank for 무선이어폰 on Naver Shopping?" and the agent runs the tool. Setup is on the tool page.

Other ways, and their limits

Search it yourself. Fine for a few keywords once. It doesn't scale to daily checks, and a logged-in phone may show you a personalized list.
Naver's official Shopping Search API was shut down on 31 July 2026, so there is no official endpoint for this anymore.
The price-comparison site (search.shopping.naver.com) goes deeper, but it blocks automated access — and it's a different list from the one most phone shoppers see first.

Rankings move a little between checks, so look at the trend over days, not a single number.

Independent tool — not affiliated with NAVER Corp. More Naver, AliExpress and K-beauty data tools: leoworks.kr/tools.