NAVER Place Review Scraper

Naver Place Review Scraper

Naver Place Review Scraper extracts public visitor reviews and Naver Blog reviews from Naver Place (네이버 플레이스), Korea's dominant local business and maps platform. Give it a Place URL, a bare Place ID, or a search keyword, and it returns structured review text, star ratings, visit dates, voted keywords, owner replies, photos, and aggregated rating statistics.

Scrape visitor reviews with star ratings and voted keywords, Naver Blog and Cafe posts linked to a place, and per-place rating statistics — from restaurants, cafes, hospitals, hotels, and any other Naver Place category.

Copy to your AI assistant

Copy this block into ChatGPT, Claude, Cursor, or any LLM to start using this actor.

Use Apify Actor parsebird/naver-place-review-scraper to scrape public Naver Place (네이버 플레이스) visitor reviews and Naver Blog reviews. Example with ApifyClient: client.actor("parsebird/naver-place-review-scraper").call(run_input={"placeUrls":["https://map.naver.com/p/entry/place/11571707"],"maxReviewPages":3,"includeBlogReviews":True}), then read client.dataset(run["defaultDatasetId"]).iterate_items(). Key inputs: placeUrls array of Naver Place URLs or bare Place IDs, startUrls array alternative in [{"url":...}] format, searchKeywords array to find places by keyword, maxPlacesPerKeyword integer default 5, maxReviews integer optional cap that overrides maxReviewPages, maxReviewPages integer default 1 (50 reviews per page), reviewSort enum NEWEST/RANKING default NEWEST, includeBlogReviews boolean default true, maxBlogReviewPages integer default 10 (10 posts per page, Naver caps around 100), includeReviewPhotos boolean default false, includeReviewStats boolean default true, maxConcurrency integer default 5, proxyConfiguration object. Output fields include reviewType (visitor/blog/place_stats), placeId, placeName, placeCategory, placeAddress, placeRating, reviewText, reviewRating, reviewerName, reviewerProfileUrl, reviewDate, visitDate, visitCount, votedKeywords, visitCategories, reviewPhotos, reviewReplyText, blogTitle, blogContents, blogUrl, bloggerName, blogDate, averageRating, starDistribution, topVotedKeywords, searchKeyword, scrapedAt. API docs: https://docs.apify.com/api/client/python/ and https://docs.apify.com/api/client/js/. Token: https://console.apify.com/account/integrations.

What does Naver Place Review Scraper do?

Naver Place Review Scraper collects public review data from Naver Place, the review and local-business layer behind Naver Map that most Korean consumers check before visiting a restaurant, cafe, clinic, salon, or attraction. Use it to monitor review sentiment, benchmark competitors, feed review data into NLP or LLM pipelines, or build datasets for market research on any Korean local business.

• 📍 Accept three input modes: Place URLs, bare Place IDs, or search keywords — mix and match in the same run.
• ⭐ Extract visitor review text, star rating, visit date, visit count, and reviewer profile info.
• 🏷️ Capture Naver's voted keywords (e.g. "음식이 맛있어요") and visit-context tags attached to each review.
• 💬 Pull the business owner's public reply text and reply date when present.
• 📷 Optionally include review photo URLs.
• 📝 Scrape linked Naver Blog and Naver Cafe posts about the place, including title, excerpt, and blogger info.
• 📊 Generate a per-place statistics row: average rating, star distribution, and top voted keywords.
• 🔎 Search by Korean or English keyword (e.g. 강남 맛집 or Seoul BBQ) to discover places before scraping their reviews.
• ⚡ Run places concurrently, with adjustable concurrency and residential proxy support for reliable access at scale.
• 📤 Send results to Apify integrations, the Apify API, webhooks, databases, or spreadsheets, and download JSON, CSV, or Excel.

What data can you extract from Naver Place?

Field	Description
reviewText	Full visitor review body (Korean)
reviewRating	Star rating, 1–5
reviewerName	Reviewer nickname, profile URL, and total review count
reviewDate / visitDate	Review creation date and actual visit date (ISO YYYY-MM-DD)
visitCount / visitCountLabel	Visit number, e.g. "3번째 방문" (3rd visit)
votedKeywords	Naver's voted review tags, e.g. "음식이 맛있어요", "분위기가 좋아요"
visitCategories	Visit-context tags such as wait time and reservation method
reviewPhotos	Photo URLs attached to the review (optional)
reviewReplyText	Business owner's public reply
blogTitle / blogContents / blogUrl	Naver Blog or Cafe post title, excerpt, and link
bloggerName	Blog or cafe author name
averageRating / starDistribution	Aggregated place rating and 5★–1★ breakdown
topVotedKeywords	Most common voted keywords across all reviews, with counts

How to scrape Naver Place reviews

1. Open Naver Place Review Scraper on Apify.
2. Add one or more Place URLs (e.g. https://map.naver.com/p/entry/place/11571707) or bare Place IDs in placeUrls. Short naver.me links are resolved automatically.
3. Or leave placeUrls empty and add search keywords to searchKeywords, such as 강남 맛집 or Seoul BBQ; the actor finds matching places and scrapes their reviews.
4. Set maxReviewPages (50 reviews per page) or maxReviews for an exact cap. Keep the value low for a fast, cheap test run.
5. Toggle includeBlogReviews and includeReviewStats depending on which review types you need.
6. Enable a proxy in proxyConfiguration for large or repeated runs to avoid Naver rate limiting.
7. Run the actor, open the dataset, and export results as JSON, CSV, or Excel, or connect them through the Apify API.

Input parameters

Parameter	Type	Required	Default	Description
placeUrls	array	No	Prefilled with 1 place	Naver Place URLs or plain Place IDs
startUrls	array	No	[]	Alternative [{"url": "..."}] input format for integrations
searchKeywords	array	No	[]	Keywords to search for places, then scrape their reviews
maxPlacesPerKeyword	integer	No	5	Max places to take from each keyword search (1–50)
maxReviews	integer	No	—	Max visitor reviews per place; overrides maxReviewPages when set (1–50,000)
maxReviewPages	integer	No	1	Visitor review pages per place, 50 reviews per page (1–1,000)
reviewSort	string	No	NEWEST	NEWEST or RANKING (Naver's own recommendation order)
includeReviewPhotos	boolean	No	false	Include photo URLs attached to each visitor review
includeBlogReviews	boolean	No	true	Also collect linked Naver Blog / Cafe posts
maxBlogReviewPages	integer	No	10	Blog review pages per place, 10 posts per page (1–100)
includeReviewStats	boolean	No	true	Add one aggregated rating-statistics row per place
maxConcurrency	integer	No	5	Places to scrape in parallel (1–10)
proxyConfiguration	object	No	Apify proxy	Proxy settings; recommended for large runs

Output example

Visitor review:

{
  "reviewType": "visitor",
  "placeId": "11571707",
  "placeName": "경복궁",
  "placeCategory": "궁궐",
  "placeAddress": "서울 종로구 사직로 161 경복궁",
  "placeRating": 4.69,
  "placeTotalReviews": 12514,
  "placeUrl": "https://pcmap.place.naver.com/place/11571707",
  "reviewId": "6a786af0d4bfe92dced69067",
  "reviewText": "서울여행 가족들과 경복궁 나들이! 한복을 입고 가면 무료입장...",
  "reviewRating": 5,
  "reviewerName": "Marge simpson 0410",
  "reviewerProfileUrl": "https://m.place.naver.com/my/5fd063c7b421d3204bb3eb2a/review?v=2",
  "reviewerReviewCount": 391,
  "reviewDate": "2026-08-09",
  "visitDate": "2026-08-09",
  "votedKeywords": ["사진이 잘 나와요", "볼거리가 많아요", "뷰가 좋아요"],
  "reviewReplyText": null,
  "searchKeyword": null,
  "language": "ko",
  "scrapedAt": "2026-08-10T22:26:16.443959Z"
}

Blog review:

{
  "reviewType": "blog",
  "placeId": "1743025750",
  "placeName": "포비 강남점",
  "blogTitle": "고터 파미에스테이션 카페 포비 FOURB",
  "blogContents": "카페명: 포비 강남 FOURB. 베이글 맛집으로 유명한, 호불호는 있을 듯!",
  "blogUrl": "https://m.cafe.naver.com/cafeflex/22617",
  "bloggerName": "아메리카노",
  "blogDate": "2022-08-07",
  "scrapedAt": "2026-08-10T22:26:16.443959Z"
}

Download results in JSON, CSV, Excel, HTML, or XML directly from the Apify dataset, or read them through the API.

Python API example

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("parsebird/naver-place-review-scraper").call(run_input={
    "placeUrls": ["https://map.naver.com/p/entry/place/11571707"],
    "maxReviewPages": 3,
    "includeBlogReviews": True,
    "includeReviewStats": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["reviewType"], item.get("reviewText") or item.get("blogTitle"))

See the official Apify Python client documentation for authentication and dataset options.

JavaScript API example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('parsebird/naver-place-review-scraper').call({
  searchKeywords: ['강남 맛집'],
  maxPlacesPerKeyword: 5,
  maxReviewPages: 1,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

See the official Apify JavaScript client documentation for more examples.

How much does it cost to scrape Naver Place reviews?

What is the price per Naver Place review?

Event	Free	Bronze	Silver	Gold
review-scraped	$0.00099 ($0.99 / 1,000)	$0.00089 ($0.89 / 1,000)	$0.00089 ($0.89 / 1,000)	$0.00089 ($0.89 / 1,000)

Each review-scraped event means one visitor review or blog review was pushed to the dataset. Place statistics rows are not charged. Scraping 500 visitor reviews (maxReviewPages: 10) from one place costs about $0.45–$0.50. Apify's free trial credits cover small test runs.

Use cases

• Monitor review sentiment and rating trends for a restaurant, cafe, salon, clinic, or hotel over time.
• Benchmark competitor ratings, voted keywords, and review volume across multiple Naver Place listings.
• Feed Korean review text into NLP or LLM pipelines for sentiment analysis or summarization.
• Build local-business or franchise datasets for market research and site-selection analysis.
• Track owner responsiveness by monitoring reviewReplyText and reviewReplyDate over scheduled runs.
• Discover and evaluate places by keyword, such as sourcing candidates for a "best of" list.

How it works

1. The actor resolves each input into a numeric Naver Place ID, following short naver.me links when needed, or runs keyword searches against Naver's public place-search API.
2. It fetches place details (name, category, address, rating totals) for context on every review.
3. It pages through visitor reviews using Naver's cursor-based pagination until the requested count or page limit is reached.
4. If enabled, it separately pages through linked Naver Blog and Naver Cafe posts using offset-based pagination.
5. It normalizes Korean date formats, voted keywords, and reviewer info into a stable dataset schema.
6. It pushes each review to the Apify dataset and applies PPE charging only on the Apify platform, stopping automatically if the user's spending limit is reached.

Is it legal to scrape Naver Place?

Is scraping Naver Place reviews legal?

Scraping publicly available web data is generally allowed in many jurisdictions, but you should review Naver's terms of service, avoid collecting private or sensitive information, and make sure your use case complies with applicable laws. For background, read Apify's guide: Is web scraping legal?.

Other scrapers / Related Actors

Actor	Best for
Naver Place Review Scraper	Naver Place visitor reviews, blog reviews, and rating statistics
GetYourGuide Review Scraper	Tour and activity review data with ratings and text
Website Contact Finder	Finding public contact details for a business or lead list
Shopify Store Leads Scraper	Finding Shopify stores and ecommerce leads
Google Ads Transparency Center Scraper	Competitor ad research and monitoring

FAQ

Can I use this as a Naver Place review API?

Yes. Start runs and fetch datasets through the Apify API, Python client, JavaScript client, webhooks, or integrations.

Can I scrape reviews for a place I only know by name?

Yes. Add the place name or a location-plus-category phrase to searchKeywords (e.g. 강남 맛집), and the actor finds matching places before scraping their reviews.

Does this actor accept short naver.me links?

Yes. placeUrls resolves naver.me short links to a Place ID automatically.

How many reviews can I collect per place?

Set maxReviews for an exact cap (up to 50,000) or maxReviewPages for page-based control (50 reviews per page, up to 1,000 pages). Naver's blog review feed is capped at roughly 100 posts per place regardless of maxBlogReviewPages.

Why is reviewReplyText empty for most reviews?

Only a minority of Naver Place reviews receive a public owner reply. The field is null when no reply exists.

Does this actor require a proxy?

A proxy is recommended for large or repeated runs to avoid Naver rate limiting. The input schema defaults to Apify proxy settings.

Can I schedule recurring review monitoring?

Yes. Use Apify schedules to run this Naver Place scraper daily, weekly, or at any interval and track rating changes over time.

Where can I report issues or request fields?

Open the Issues tab on the actor page and include your input example, run ID, and the Naver Place URL or ID.