This tutorial shows how to scrape Kakao reviews from Kakao Map and Kakao Place store URLs into CSV with the Kakao Map Review Scraper template for UScraper. You will import the workflow, replace sample URLs, set the export path, validate rows, and check common failure cases.
Before you start
Prerequisites, inputs, and policy checks
You need UScraper installed as a local desktop app, a short list of normal https://place.map.kakao.com/... URLs, and a writable CSV folder. Start with one or two places, not a large batch. Kakao Place pages are dynamic, and behavior can change by language, network speed, session state, review volume, or verification prompts.
This guide covers visible public review pages you can inspect in a browser. It does not cover private pages, login-only data, CAPTCHA bypassing, blocked sessions, or access-control workarounds. Review Kakao's current terms and your data-use obligations before collecting reviewer names, review text, profile links, ratings, or photos.
Technical visibility is not permission. Keep volume modest and stop when Kakao asks for manual verification.
Workflow anatomy
What the Kakao Map review scraper does
The template is built around known Kakao Place URLs. It does not discover businesses from a keyword first. For upstream discovery, use Kakao Map Shop List Scraper, then feed approved place URLs into this review workflow.
In plain English, the flow is:
Navigate through place URLs -> wait for load -> short sleep
-> inject review loader and parser -> check for generated rows
-> Structured Export -> Loop Continue
The key design choice is the hidden row layer. The injected script loads reviews, scrolls where possible, deduplicates obvious repeats, and writes one .uscraper-kakao-review-row element per review. Structured Export reads stable data-* attributes from those generated rows instead of changing Kakao card markup.
kakao-map-review-scraper.csvColumn
store_name
Place name.
Column
category
Place category.
Column
overall_rating
Store rating.
Column
rating_count
Rating count.
Column
review_count
Review count.
Column
address
Road address.
Column
parcel_address
Parcel address.
Column
phone
Business phone.
Column
reviewer
Reviewer name.
Column
reviewer_link
Profile URL.
Column
reviewer_review_count
Reviewer activity.
Column
reviewer_average_rating
Reviewer average.
Column
reviewer_follower_count
Follower count.
Column
review_date
Review date.
Column
rating
Review rating.
Column
review_content
Review text.
Column
review_images
Photo URLs.
API context
Kakao Map API vs scraper workflow
People compare a Kakao Map API vs scraper path because the jobs differ. Kakao's Map API supports map features in apps, while the Local API covers place search, category search, geocoding, and coordinate-to-address workflows. Those are useful for sanctioned app development, but they are not a CSV export path for visible review rows.
| Option | Best fit | Practical limitation |
|---|---|---|
| Kakao Map API | Product map features | Not a review-feed spreadsheet export. |
| Kakao Local API | Search, category, address, and coordinate work | Requires API setup and does not target bulk review export. |
| Hosted alternatives | Managed cloud scale and APIs | Usually metered and less inspectable for analyst QA. |
| UScraper template | Supervised CSV export from known place URLs | Needs validation when Kakao changes markup or prompts. |
If you are building a production map feature, start with official Kakao documentation. If you need selected restaurant, clinic, hotel, salon, branch, or competitor reviews in a spreadsheet, a no-code browser workflow is easier to inspect.
Runbook
How to scrape Kakao Place reviews to CSV
Replace the sample place URLs
In Navigate, replace the bundled examples with approved Kakao Place URLs. Keep one URL per input.
Watch the first browser run
Run one place and watch the page. Resolve prompts, empty review areas, verification screens, or changed layouts before trusting the CSV.
Set the export folder
In Structured Export, confirm kakao-map-review-scraper.csv, headers, append mode, and a project-specific folder.
Validate rows before scaling
Open the CSV, compare rows against Kakao Map, check Korean text encoding, and then add more URLs.
Validate the export before analysis
Do not jump straight from scrape to dashboard. Treat the first run as calibration.
| Check | What to look for | Fix |
|---|---|---|
| Row count | Does the CSV roughly match loaded reviews? | Increase waits or rerun after confirming reviews load. |
| Store context | Are store_name, address, phone, and rating correct? | Verify the URL and page language. |
| Review body | Are Korean characters intact? | Open with UTF-8 aware spreadsheet software. |
| Images | Are photo URLs present only when the review has photos? | Treat blanks as normal when reviewers did not attach images. |
| Diagnostic row | Did NO_PUBLIC_REVIEWS_FOUND_OR_KAKAO_BLOCKED_REVIEW_API appear? | Inspect the browser session before scaling. |
The columns support review coding, branch comparison, local SEO reporting, and customer-experience audits. Before contacting reviewers, republishing quotes, training a model, or sharing competitor data, review privacy and policy obligations separately.
Troubleshooting
Common issues with Kakao Map review scraping
Confirm the place URL works, reviews are visible, and no prompt interrupted the run. Then rerun one place with longer waits.
When to use alternatives
UScraper is a good fit when you want a supervised local desktop app workflow, an inspectable browser run, and CSV output you can open immediately. Hosted services make sense for scheduled cloud runs, APIs, or procurement-backed scale. Python examples work when an engineering team wants code ownership and can maintain selectors, retries, pacing, exports, and compliance review.
For a broader research pipeline, start with the UScraper template library, pair Kakao exports with Naver Map Review Scraper, and keep this guide linked from the UScraper blog for analyst onboarding.