Tabelog Scraper

Tabelog Scraper — Extract restaurant data and reviews from Tabelog

What does Tabelog Scraper do?

Tabelog Scraper extracts restaurant details and reviews from Tabelog (食べログ), Japan's largest restaurant review platform with 890,000+ restaurants and 85 million+ reviews. Simply enter a keyword like "ラーメン" or paste restaurant URLs, and the scraper collects structured data you can download as JSON, CSV, Excel, or connect to your apps via the Apify API.

You can use it in two ways:

• 🔍 Search mode — Find restaurants by keyword and area (e.g., "寿司" in Tokyo)
• 🔗 Direct URL mode — Provide specific Tabelog restaurant URLs

Why scrape Tabelog restaurant data?

Tabelog is Japan's #1 restaurant review site, trusted by millions of diners. Extracting its data opens doors to powerful use cases:

• 📊 Market research — Study dining trends, pricing, and customer satisfaction across Japan
• 🤖 NLP & sentiment analysis — Build Japanese language models with authentic restaurant review corpora
• 🗺️ Travel & food apps — Power recommendation engines with real restaurant data
• 📈 Competitive intelligence — Monitor competitor restaurants, pricing, and customer feedback
• 🗄️ Restaurant databases — Build comprehensive directories with address, hours, budget, and ratings

Combined with the Apify platform, you get automatic retries, proxy rotation, scheduled runs, API access, and seamless integrations with tools like Google Sheets, Slack, Zapier, and more.

What data can you extract from Tabelog?

Restaurant information

Field	Example
restaurantName	グルガオン
restaurantId	13002457
restaurantRating	3.72
restaurantGenre	インド料理、インドカレー
address	東京都中央区銀座1-6-13 銀座106ビル B1F
postalCode	1040061
latitude / longitude	35.6747 / 139.7671
nearestStation	銀座一丁目駅から35m
businessHours	月・火・水・木・金
budgetDinner	￥2,000～￥2,999
budgetLunch	￥1,000～￥1,999
paymentMethods	カード可（VISA、Master、JCB、AMEX、Diners）
seats	38席（テーブル席のみ）
privateRoom	無
smokingPolicy	全席禁煙
parking	無
awards	アジア・エスニック 百名店 2024 選出店
reservation	予約可
homepage	http://gurgaontokyo.com/
officialAccounts	Instagram, Twitter URLs
childPolicy	子供可
services	テイクアウト、複数言語メニューあり
phoneNumber	03-3563-0623
photoUrl	Main restaurant photo URL
budgetFromReviews	￥3,000～￥3,999 (aggregated from reviewer spend)
serviceCharge	ランチは現金払いのみ
charterReservation	不可
spaceFacilities	おしゃれな空間、落ち着いた空間
drinks	ワインあり、カクテルあり
cuisineNotes	ベジタリアンメニューあり
occasions	家族・子供と ｜ 知人・友人と
remarks	瓶コーク提供店

Review data (when includeReviews is enabled)

Each review row includes all restaurant fields above, plus:

Field	Example
reviewText	美味しすぎて感動した銀座にあるインドカレー屋さん…
rating	4.5
scoreFood, scoreService, scoreAtmosphere, scoreCostPerformance, scoreDrinks	4.5, 4.0, 4.0, 4.5, null (sub-scores; null when the reviewer left one blank)
visitDate	2026/04
visitCount	3 (the reviewer's 3rd visit)
reviewerName	わらびもちーず
reviewerReviewCount / reviewerFollowerCount	604 / 257
reviewTitle	銀座の名店カレー
mealType	ディナー
priceRange	￥2,000～￥2,999
reviewId	B470973875
photos	Photo URLs (when enabled)

How to scrape Tabelog restaurant data

1. Go to Tabelog Scraper on Apify Store
2. Click Try for free to open the Actor in Apify Console
3. Choose your input method:
   • Search mode: Enter a keyword (e.g., カレー) and select an area (e.g., tokyo)
   • Direct URLs: Paste one or more Tabelog restaurant URLs
4. Set Include reviews to true for full review text, or false for restaurant info only
5. Click Start and wait for the run to finish
6. Download your data as JSON, CSV, Excel, or connect via API

Input example — Search mode

{
    "searchKeyword": "ラーメン",
    "searchArea": "tokyo",
    "maxRestaurants": 10,
    "includeReviews": true,
    "maxReviewsPerRestaurant": 50,
    "sortBy": "newest"
}

Input example — Direct URL mode

{
    "startUrls": [
        { "url": "https://tabelog.com/tokyo/A1304/A130401/13002457/" }
    ],
    "includeReviews": true,
    "maxReviewsPerRestaurant": 100
}

💡 Tip: Set includeReviews to false if you only need restaurant details — it's much faster and cheaper.

Input options

Tabelog Scraper has the following input options. Click on the Input tab for more information.

Parameter	Type	Default	Description
startUrls	Array	—	Tabelog restaurant page URLs
searchKeyword	String	—	Search keyword (e.g., 寿司, カレー)
searchArea	String	all	Prefecture filter — 47 prefectures supported
maxRestaurants	Integer	5	Max restaurants from search results
includeReviews	Boolean	true	Extract reviews or restaurant info only
maxReviewsPerRestaurant	Integer	100	Max reviews per restaurant (0 = all). Tabelog only serves ~60 review pages, so ~1,100 reviews per restaurant is the hard ceiling — higher values are capped by the site, not by this Actor.
reviewLanguage	String	all	Filter reviews by language
includePhotos	Boolean	false	Include review photo URLs
sortBy	String	newest	Sort order: newest, rating_high, rating_low, default
minRating	Number	—	Only reviews rated at least this (e.g. 4.0). With sortBy: rating_high the crawl stops as soon as ratings drop below it.
visitedAfter	String	—	Only reviews for visits in this month or later, as YYYY-MM (e.g. 2026-01).
mealType	String	all	all, lunch or dinner
withPhotosOnly	Boolean	false	Only reviews that include photos
onlyNewReviews	Boolean	false	Skip reviews an earlier run already delivered — for scheduled monitoring. State is kept in the tabelog-scraper-seen-reviews key-value store in your account (delete it to start over).
proxyConfiguration	Object	—	Apify Proxy settings

You must provide either startUrls or searchKeyword (or both).

Review filters are free: reviews that don't match minRating / visitedAfter / mealType / withPhotosOnly are dropped on the review list page — they are never fetched in full and never charged. maxReviewsPerRestaurant counts matching reviews only.

Output example

You can download the dataset extracted by Tabelog Scraper in various formats such as JSON, HTML, CSV, or Excel.

Restaurant info only (includeReviews: false)

{
    "restaurantName": "グルガオン",
    "restaurantUrl": "https://tabelog.com/tokyo/A1301/A130101/13002457/",
    "restaurantId": "13002457",
    "restaurantRating": 3.72,
    "restaurantGenre": "インド料理、インドカレー",
    "restaurantArea": "東京",
    "totalReviewCount": 3177,
    "address": "東京都中央区銀座1-6-13 銀座106ビル B1F",
    "postalCode": "1040061",
    "latitude": 35.6747,
    "longitude": 139.7671,
    "nearestStation": "銀座一丁目駅から35m",
    "businessHours": "月・火・水・木・金",
    "budgetDinner": "￥2,000～￥2,999",
    "budgetLunch": "￥1,000～￥1,999",
    "paymentMethods": "カード可（VISA、Master、JCB、AMEX、Diners）",
    "seats": "38席（テーブル席のみ）",
    "smokingPolicy": "全席禁煙",
    "awards": ["アジア・エスニック 百名店 2024 選出店"],
    "homepage": "http://gurgaontokyo.com/",
    "scrapedAt": "2026-05-02T12:00:00.000Z"
}

With reviews (includeReviews: true)

{
    "restaurantName": "グルガオン",
    "restaurantRating": 3.72,
    "address": "東京都中央区銀座1-6-13 銀座106ビル B1F",
    "reviewText": "美味しすぎて感動した銀座にあるインドカレー屋さん\n\n大人気と聞いていたので...",
    "rating": 4.5,
    "scoreFood": 4.5,
    "scoreService": 4.0,
    "scoreAtmosphere": 4.0,
    "scoreCostPerformance": 4.5,
    "scoreDrinks": null,
    "visitDate": "2025/10",
    "visitCount": 1,
    "reviewerName": "わらびもちーず",
    "reviewerReviewCount": 604,
    "reviewerFollowerCount": 257,
    "mealType": "ディナー",
    "priceRange": "￥2,000～￥2,999",
    "reviewUrl": "https://tabelog.com/tokyo/A1301/A130101/13002457/dtlrvwlst/B...",
    "scrapedAt": "2026-05-02T12:00:00.000Z"
}

How much does it cost to scrape Tabelog?

Tabelog Scraper uses a pay-per-result pricing model on top of Apify platform usage costs.

Mode	Estimated cost
🏪 Restaurant info only	~$0.50 per 100 restaurants
📝 With reviews	~$3.00 per 1,000 reviews

You can try the Actor for free on Apify's free plan. The free tier includes enough compute units to scrape several restaurants with reviews and explore the output format.

💡 Cost-saving tip: Use includeReviews: false when you only need restaurant metadata — it skips individual review page visits and runs significantly faster.

Tips for best results

• 🇯🇵 Use a Japan proxy — Tabelog may geo-restrict access. On Apify, the default proxy works well, but Japan-based proxies give the best reliability.
• ⏱️ Start small — Test with 1-2 restaurants and a few reviews to check output format before scaling up.
• 🔢 Set maxReviewsPerRestaurant — Restaurants can have thousands of reviews. Set a limit to control costs and run time. Note that Tabelog itself caps review pagination at ~60 pages, so you cannot get more than ~1,100 reviews per restaurant no matter how high you set this.
• 📊 Use Apify integrations — Connect results directly to Google Sheets, webhooks, or your database via the Apify API.
• 📅 Schedule recurring runs — Track review trends over time by scheduling the Actor to run daily, weekly, or monthly.

Is it legal to scrape Tabelog?

This scraper only extracts publicly available information from Tabelog. It does not extract private user data such as email addresses or personal contact information. The scraper respects rate limits with built-in delays between requests.

You should be aware that extracted results may contain personal data (reviewer names, profile URLs). Personal data is protected by the GDPR in the European Union, APPI in Japan, and other regulations worldwide. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your use case is legitimate, consult your lawyers.

Limitations and known issues

• Japanese language — Data is extracted in Japanese. The English version (/en/) has limited restaurant coverage.
• Pagination cap — Tabelog exposes at most ~60 review-list pages per restaurant (the next page returns 404), so roughly 1,100 reviews are reachable. Restaurants with more reviews than that cannot be fully scraped, and a few reviews are rating-only with no text, so the result count is normally below the restaurant's displayed review total.
• Rate limiting — The scraper uses 1–2 second delays between requests for polite crawling. Large-scale scraping will take time proportional to the number of reviews.

FAQ

Does Tabelog have an API?

Tabelog does not provide a free public API for extracting restaurant data. This scraper serves as an alternative by extracting structured data from publicly available Tabelog pages.

Can I scrape Tabelog in English?

Tabelog has a limited English site at tabelog.com/en/, but most content is only available in Japanese. This scraper extracts data in Japanese by default.

How many results can I get for free?

On Apify's free plan, you can scrape approximately 5-10 restaurants with reviews per month, depending on the number of reviews per restaurant.

Can I extract data from all 47 prefectures?

Yes. The searchArea input supports all 47 Japanese prefectures (from hokkaido to okinawa), or you can set it to all for a nationwide search.

How does the 4-stage pipeline work?

The scraper operates in four stages:

1. Search — Finds restaurant URLs from keyword search results
2. Restaurant — Visits each restaurant page and extracts detailed metadata
3. Review list — Browses review list pages and collects review metadata
4. Review detail — Visits individual review pages for full, untruncated review text

How do I collect only new reviews on a schedule?

Set onlyNewReviews: true and sortBy: newest, then schedule the run (e.g. daily). The first run collects as usual and remembers every review ID per restaurant; later runs stop at the first review they already delivered, so you only get — and only pay for — reviews posted since the previous run.

Why did I get fewer reviews than the restaurant shows?

This is expected, and there are two separate reasons:

1. Tabelog's pagination ceiling — the review list serves at most ~60 pages per restaurant (requesting page 61 returns a 404), which works out to roughly 1,100 reviews. Reviews older than that are not reachable through the site's own listing, so no scraper can retrieve them.
2. Rating-only reviews — some users post a score with no written review. Those entries have no text to extract, so they are skipped rather than returned as empty rows.

So a restaurant displaying 1,491 reviews typically yields around 950–1,100 rows. Setting maxReviewsPerRestaurant higher than ~1,100 does not increase the result count. The run log states the exact reason and the number collected when the limit is reached.

I found a bug or need a custom feature

Please report issues or feature requests via the Issues tab on the Actor page. Feedback is always welcome!