Naver Place Reviews Scraper

Extract visitor reviews and blog reviews from Naver Place — Korea's #1 local business platform. Cursor-based pagination with no server-side limit on visitor reviews.

Features

• Visitor reviews: cursor-based pagination, 50 per page, no upper limit on total reviews
• Blog reviews: offset-based pagination, ~100 items per place (server-side cap)
• TLS fingerprinting: curl_cffi with Chrome impersonation — bypasses bot detection
• Structured output: voted keywords, visit counts, owner replies, star distribution
• Two input modes: direct Place URL/ID or keyword search

What you get

Visitor review fields

Field	Description
reviewText	Full review body (Korean)
reviewRating	1-5 stars
reviewerName	Author nickname + profile URL + total review count
reviewDate	Review creation date (YYYY-MM-DD)
visitDate	Actual visit date
visitCount	"2번째 방문" (2nd visit)
reviewVerification	How Naver verified the visit: 영수증 (receipt), 예약 (booking), 결제내역 (payment record); null if unverified
orderedItem	Menu item or ticket the reviewer bought, when Naver links one
reviewerAverageRating	Reviewer's average star rating across all their reviews; null if they don't rate
votedKeywords	"음식이 맛있어요", "분위기가 좋아요", etc.
reviewPhotos	Photo URLs (optional)
reviewReplyText	Business owner's response

Blog review fields

Field	Description
blogTitle	Blog post title
blogContents	Post excerpt/contents
blogUrl	Link to full blog post
blogSource	blog (Naver Blog) or cafe (Naver Café post)
bloggerName	Blogger name
blogDate	Post date

Aggregated statistics

When includeReviewStats is enabled (default), one stats row per place — a single dataset item.

Whole place (Naver's own statistics, the full review history):

• totalVisitorReviews, averageRating — review count and average star rating
• placeRatingDistribution — star histogram, e.g. {"4.5": 68, "5": 364}
• placeThemes — what reviews talk about, e.g. {"맛": 782, "분위기": 184}
• placeTopMenus — most-mentioned menu items, e.g. {"타코": 374}
• placeKeywordDistribution — voted keywords ("음식이 맛있어요" …) with vote counts

This run (only the reviews you scraped):

• ratingDistribution, keywordDistribution — the same histograms over this run's reviews
• scrapedVisitorReviews, scrapedBlogReviews — how many rows this run collected

Histograms are ordered highest count first (stars ascending). Verticals Naver does not rate or tag — clinics, hospitals — return {}; null means Naver's statistics could not be fetched on this run.

Stats only, or blog reviews only

Set includeVisitorReviews to false:

• with includeBlogReviews: true — blog reviews only;
• with includeBlogReviews: false — just the stats row: one dataset item per place, the cheapest way to compare themes, menus and keyword votes across many places.

Input options

Option 1: Direct Place URLs or IDs

{
    "placeUrls": [
        "https://map.naver.com/v5/entry/place/37234567",
        "37234567"
    ],
    "maxReviewPages": 3
}

Option 2: Search by keyword

{
    "searchKeywords": ["강남 맛집", "Seoul BBQ"],
    "maxPlacesPerKeyword": 5,
    "maxReviewPages": 1
}

Option 3: Every review since a date

{
    "placeUrls": ["37234567"],
    "visitedSince": "2026-09-01"
}

Visitor reviews whose visit date is on or after visitedSince. Naver lists reviews by visit date, so the run stops as soon as it reaches older visits — you only pay for the window you asked for. Without maxReviews, every review in the window is collected; with it, maxReviews still caps each place. A review written recently about an older visit is outside the window — for scheduled runs that must catch every new review, use Option 4.

Option 4: Scheduled runs — only new reviews

{
    "placeUrls": ["37234567"],
    "maxReviews": 50,
    "onlyNewReviews": true
}

Each run looks at the same newest reviews it normally would, but skips the ones an earlier onlyNewReviews run already returned — they are not returned and not charged again. A re-run with nothing new returns 0 items; the run status says how many were skipped. The stats row (if enabled) gains skippedPreviouslyScraped.

• History is kept per place in a key-value store in your Apify account (naver-place-reviews-seen by default). Use reviewHistoryName to keep separate histories for separate pipelines; delete the store or change the name to start over.
• Only reviews that were actually stored count as delivered — if a run's cost limit cuts it short, the rest are offered again next time.
• Don't run two onlyNewReviews runs for the same place at the same moment; both would see the same history and return the same new reviews.

Full input example

{
    "placeUrls": ["37234567"],
    "maxReviewPages": 10,
    "visitedSince": "2026-09-01",
    "includeVisitorReviews": true,
    "includeBlogReviews": true,
    "maxBlogReviewPages": 10,
    "includeReviewPhotos": false,
    "includeReviewStats": true,
    "onlyNewReviews": false
}

Pagination guide

maxReviewPages	Reviews collected
1	~50
3	~150
10	~500
50	~2,500
100	~5,000

Output format

Visitor review

{
    "reviewType": "visitor",
    "placeId": "37234567",
    "placeName": "맛있는 식당",
    "placeCategory": "음식점",
    "placeAddress": "서울특별시 강남구 ...",
    "placeRating": 4.52,
    "placeTotalReviews": 3162,
    "placeUrl": "https://pcmap.place.naver.com/place/37234567",
    "reviewId": "abc123",
    "reviewText": "정말 맛있었어요! 분위기도 좋고 서비스도 훌륭합니다.",
    "reviewRating": 5,
    "reviewerName": "맛집탐험가",
    "reviewerProfileUrl": "https://m.place.naver.com/my/...",
    "reviewerReviewCount": 42,
    "reviewerAverageRating": 4.9,
    "reviewVerification": "영수증",
    "orderedItem": "치즈닭갈비",
    "reviewDate": "2026-05-15",
    "visitDate": "2026-05-10",
    "visitCount": "2번째 방문",
    "votedKeywords": ["음식이 맛있어요", "분위기가 좋아요"],
    "reviewPhotos": null,
    "reviewReplyText": "감사합니다! 또 방문해주세요.",
    "reviewReplyDate": "2026-05-16",
    "searchKeyword": null,
    "language": "ko",
    "scrapedAt": "2026-05-17T12:00:00.000Z"
}

Blog review

{
    "reviewType": "blog",
    "placeId": "37234567",
    "placeName": "맛있는 식당",
    "blogTitle": "강남 맛집 탐방기",
    "blogContents": "오늘은 강남에 위치한...",
    "blogUrl": "https://blog.naver.com/...",
    "blogSource": "blog",
    "bloggerName": "맛집블로거",
    "bloggerHome": "https://blog.naver.com/blogger123",
    "blogDate": "2026-05-10",
    "blogThumbnailUrl": "https://...",
    "isRepresentative": false,
    "scrapedAt": "2026-05-17T12:00:00.000Z"
}

Technical details

• HTTP client: curl_cffi with TLS fingerprinting (impersonate="chrome")
• Visitor review pagination: cursor-based via after parameter (not page-based)
• Blog review pagination: offset-based (page + display=10)
• Rate limiting: automatic retry with exponential backoff on HTTP 429
• Deduplication: reviewId-based dedup for visitor reviews, id-based for blog reviews

Use cases

• Korean market entry research — what do locals say about competitors?
• Restaurant/cafe competitive intelligence
• Customer sentiment analysis (NLP-ready structured data)
• Brand reputation monitoring in Korea
• Academic research on Korean consumer behavior

Korea Data Suite by huggable_quote

Actor	Description
Naver Map	Business listings & details
Naver Blog	Blog posts & content
Naver Shopping	Product data & prices
Naver News	News articles & headlines
Naver Place Reviews	This actor
KRX Stock	Korean stock market data