Naver Maps Reviews Scraper

Scrape reviews from any place on Naver Maps — Korea's dominant maps and local-search service — without an API key or a headless browser. Give the Actor a place (by URL, ID, or search term) and get back every review as clean, structured data: full review text, rating, visit keywords, dates, photos, owner replies and reviewer info — one row per review, ready to export to JSON, CSV or Excel.

Naver Maps is where Korean consumers actually rate and review restaurants, cafés, clinics and shops — the reviews that shape local reputation in South Korea, and that you can't get from Google Maps, which has thin coverage there. If you're doing reputation monitoring, competitor research, sentiment analysis or voice-of-customer work for the Korean market, this is the source.

What does Naver Maps Reviews Scraper do?

It collects both kinds of review Naver shows for a place:

• Visitor reviews (방문자 리뷰) — reviews written directly on the place page by people who visited, including a rating, the review text, visit keywords (e.g. "food was delicious", "good for groups"), visit date, visit count, verification type (e.g. receipt-verified), photos, and any owner reply.
• Blog & café reviews (블로그·카페 리뷰) — external Naver Blog and Naver Café posts about the place, with the post title, an excerpt, the author, the date and a link to the full post.

You can scrape one, the other, or both. Every review is stamped with its place context (name, category, address, coordinates, place URL), so a single export is ready for analysis with no joining required.

Because it runs on the Apify platform, you also get scheduling, a REST API, webhooks, and integrations with Make, Zapier and n8n — plus automatic Korean proxy rotation so runs stay reliable.

Why use Naver Maps Reviews Scraper?

• Monitor your reputation — pull every review for your locations and track ratings, keywords and complaints over time.
• Research competitors — see what customers praise and criticise at rival places, at scale.
• Analyse sentiment & keywords — the visit keywords and full review text are ideal inputs for NLP, topic modelling and CSAT work — in Korean.
• Feed AI & dashboards — export structured reviews into your data warehouse, BI tool or LLM pipeline.

How to use Naver Maps Reviews Scraper

1. Pick your places. Paste one or more Naver place URLs (e.g. https://map.naver.com/p/entry/place/1005495070) or place IDs into the input. Or, instead, enter a search term (e.g. 강남 삼겹살) — optionally with a location — to scrape reviews from every matching place.
2. Choose review options — how many reviews per place, which review types (visitor / blog / both), and whether to include reviewer personal data.
3. Click Start and watch results stream into the dataset.
4. Export the data as JSON, CSV, Excel or HTML, or pull it via the Apify API.

Finding a place URL

Search for the place on map.naver.com, open it, and copy the URL from your browser — the number in it (e.g. 1005495070) is the place ID. naver.me share links work too; they're resolved automatically.

Input

Configure the run from the Apify UI, or via the API. Main options:

• 🔗 Naver place URLs / 🆔 Naver place IDs — the places to scrape reviews from. The most direct way to target exact places.
• 🔍 Search term(s) — instead of listing places, discover them by search (e.g. 역삼동 카페). Reviews are scraped from every matching place. Korean terms match best.
• 📍 Location / 📐 Structured location / 🛰 Custom search area — optional, to restrict a search to an area (a free-text place like Gangnam, Seoul, Korea's administrative hierarchy 시·도 → 시·군·구 → 읍·면·동, or an exact bounding box).
• ⭐ Max reviews per place — how many reviews to pull from each place (newest first). Set it high to capture a place's whole review history.
• Review type — Both, Visitor reviews only, or Blog & café reviews only.
• Include reviewer personal data — include the reviewer's nickname, profile link and photo (and blog author name for blog reviews). Turn it off to omit reviewer identity.
• 🌍 Language — preferred language for content where Naver provides it: Korean, English, Japanese or Chinese.

When you provide any place URL or ID, the search fields are ignored — direct input takes priority.

Proxy is handled automatically — every run goes through Apify Residential proxy in South Korea, since Naver is geo-sensitive and rate-limits foreign/datacenter IPs. There's nothing to configure.

Example input

Scrape the 200 newest visitor and blog reviews for two places:

{
    "placeUrls": ["https://map.naver.com/p/entry/place/1005495070", "https://map.naver.com/p/entry/place/1102875830"],
    "maxReviewsPerPlace": 200,
    "reviewSources": "both"
}

Or find places by search and scrape their reviews:

{
    "searchStrings": ["강남 삼겹살"],
    "locationQuery": "Gangnam, Seoul",
    "maxPlacesPerSearch": 10,
    "maxReviewsPerPlace": 50,
    "reviewSources": "visitor"
}

Output

Results are stored as a dataset — one item per review — which you can download as JSON, CSV, Excel or HTML, or query via the API. The Actor ships Reviews, Visitor reviews and Blog & café reviews views in the output tab so you can look at each stream on its own.

A visitor review looks like:

{
    "placeId": "1005495070",
    "placeName": "영동그집",
    "placeUrl": "https://map.naver.com/p/entry/place/1005495070",
    "category": "육류,고기요리",
    "roadAddress": "서울 강남구 봉은사로1길 42-8",
    "location": { "lat": 37.507466, "lng": 127.024445 },
    "placeReviewTotal": 4718,
    "reviewSource": "visitor",
    "reviewId": "6a5ca80b36db33d084836976",
    "rating": 5,
    "text": "고기도 직접 맛있게 구워주시고 무엇보다 너무 맛있습니다!",
    "visitedDate": "7.18.토",
    "createdDate": "7.19.일",
    "visitCount": 1,
    "originType": "영수증",
    "keywords": ["음식이 맛있어요", "친절해요"],
    "photos": ["https://pup-review-phinf.pstatic.net/…"],
    "reactionCount": 0,
    "ownerReply": null,
    "reviewerName": "또오아",
    "reviewerProfileUrl": "https://m.place.naver.com/my/…/review",
    "scrapedAt": "2026-07-19T11:03:54.876Z"
}

A blog & café review uses the same fields, with the blog-specific ones filled in (blogTitle, blogUrl, blogName) and review-only fields such as rating and keywords left empty.

Data fields

Field	Description
placeId, placeName, placeUrl	The place the review belongs to
category, address, roadAddress, location	Place category and location
placeReviewTotal	Total visitor reviews the place has
reviewSource	visitor or blog
reviewId	Unique review identifier
rating	Star rating (often empty — see note below)
text	Full review text / blog excerpt
keywords	Visit keywords chosen by the reviewer (visitor reviews)
visitedDate, createdDate	When they visited / when the review was written
visitCount	How many times the reviewer has visited
originType	How the visit was verified (e.g. receipt) / blog / cafe
photos	Review photo URLs
reactionCount	"Helpful" reactions on the review
ownerReply	The business owner's reply, if any
blogTitle, blogUrl, blogName	Post title, link and blog (blog reviews)
reviewerName, reviewerProfileUrl, reviewerImageUrl	Reviewer identity (omitted if personal data is off)

How much does it cost to scrape Naver reviews?

This Actor uses pay-per-result pricing — you're billed per review scraped, so cost scales with exactly how much data you pull. A place with 500 reviews costs the same whether you find it by URL or by search. Apify's free plan includes monthly usage credits you can use to try it out. See the Pricing tab on the Actor's page for the current per-review price, and use Max reviews per place to cap spend.

Tips

• Get a place's full history: set Max reviews per place high and leave review type on Both.
• Recent reviews only: reviews come newest-first, so a small Max reviews per place gives you just the latest ones.
• Korean search terms match Naver's index best (e.g. 카페 rather than "cafe").
• Sentiment work: the keywords and text fields together are a strong signal for Korean-language NLP.

FAQ

Do I need a Naver API key or account? No. The Actor reads publicly available review data from Naver Maps directly — no key, login or credentials required.

Why are many rating values empty? Naver has largely moved visitor reviews away from star ratings toward visit keywords (e.g. "food was delicious"). So many visitor reviews have no numeric rating — the sentiment lives in the keywords and text fields instead. Blog & café reviews never carry a rating.

What's the difference between visitor reviews and blog reviews? Visitor reviews (방문자 리뷰) are short reviews written on the place page by visitors. Blog & café reviews (블로그·카페 리뷰) are longer external posts on Naver Blog / Café that mention the place. Both are shown on every Naver place page; this Actor can scrape either or both.

Why does it use a Korean proxy? Naver serves and rate-limits by geography. Requests from South Korean residential IPs are far more reliable, so the Actor routes every run through Apify's Residential proxy in KR. This is handled internally — there's nothing to configure.

Can I run it on a schedule or from my own app? Yes. Like any Apify Actor it can be scheduled and called via the Apify API, the JavaScript / Python clients, or integrations such as Make, Zapier and n8n — see the API tab on the Actor's page.

Is web scraping legal? This Actor collects publicly available information. As with any scraping, you're responsible for how you use the data — in particular, complying with Naver's terms and applicable laws, including data-protection rules such as GDPR when personal data is involved. Use the Include reviewer personal data switch to omit reviewer identity when you don't need it. If in doubt, seek legal advice.

Feedback & support

Found a bug or need another field? Open an issue on the Actor's Issues tab — feedback is welcome, and custom tweaks are possible.