# Tabelog Restaurant Reviews Scraper - Low-cost💲🔥🍣🇯🇵 (`delectable_incubator/tabelog-restaurant-reviews-scraper-low-cost`) Actor ⭐ Extract Tabelog restaurant reviews with ease. Collect reviewer names, countries, ratings, food & service scores, meal types, spending amounts, visit dates, review titles, comments, reviewer profiles, photos, and review URLs. Ideal for Japan restaurant reputation analysis & review datasets 🇯🇵📊 - **URL**: https://apify.com/delectable\_incubator/tabelog-restaurant-reviews-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. - **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use). - **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`. - **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`. - **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`. - **Any other language** — the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README

Tabelog Restaurant Reviews Scraper

*** ## 🍣🇯🇵 Tabelog Restaurant Reviews Scraper | 食べログレビュー取得 | Bulk Review Scraper | Apify Actor ### 🚀 Scrape Tabelog Restaurant Reviews in Bulk | 食べログの口コミを一括取得 The **Tabelog Restaurant Reviews Scraper (Apify Actor)** is a powerful and scalable tool designed to extract detailed customer reviews from **Tabelog (食べログ)** restaurant review pages. Scrape reviews from multiple Japanese restaurant profiles in a single run with automatic pagination, reviewer information, ratings, visit details, spending data and detailed review content. Perfect for **restaurant reputation analysis, Japan food market research, customer sentiment analysis, restaurant intelligence, competitor research, tourism datasets, price analysis and AI datasets**. *** ### 🔥 Why This Tabelog Review Scraper? | なぜこのスクレイパー？ ✔ Scrape Tabelog restaurant reviews — **食べログの口コミを取得** ✔ Supports **bulk restaurant review URLs — レビューURLを一括処理** ✔ Automatic review pagination — **レビューの自動ページング** ✔ Extract reviewer information — **レビュアー情報** ✔ Extract reviewer country — **レビュアーの国** ✔ Extract restaurant ratings — **評価・スコア** ✔ Extract food, service and atmosphere scores ✔ Extract cost-performance ratings — **コストパフォーマンス** ✔ Extract drinks ratings — **ドリンク評価** ✔ Extract meal type — **食事タイプ** ✔ Extract amount spent — **支払金額** ✔ Extract visit date — **訪問日** ✔ Extract visit count — **訪問回数** ✔ Extract review title and comments — **口コミタイトル・本文** ✔ Extract reviewer profiles ✔ Extract review photos count ✔ Japan residential proxy support — **日本プロキシ対応** ✔ Clean structured JSON / CSV / Excel output ✔ No coding required *** ### 🎯 What This Scraper Does | このスクレイパーで取得できるデータ This Actor extracts individual reviews from Tabelog restaurant review-list pages: ``` /dtlrvwlst/ ``` It automatically follows numbered review pages: ``` ?PG=2 ?PG=3 ?PG=4 ... ``` until the configured maximum number of reviews is reached or no additional pages are available. #### 📌 Main Features | 主な機能 🍣 **Restaurant Review Extraction — 口コミ取得** ⭐ **Rating Extraction — 評価取得** 👤 **Reviewer Information — 口コミ投稿者情報** 🌍 **Reviewer Country — 投稿者の国** 🍽️ **Meal Type — 食事タイプ** 💴 **Amount Spent — 支払金額** 📅 **Visit Date — 訪問日** 🔄 **Visit Count — 訪問回数** 💬 **Review Title & Comment — タイトル・口コミ本文** 📊 **Detailed Rating Breakdown — 詳細評価** 📷 **Review Photo Count — 写真枚数** 🔗 **Reviewer & Review URLs — プロフィール・口コミURL** *** ### ⚡ Input Configuration | 入力設定 #### 🔥 BULK REVIEW URL MODE | 口コミURL一括取得 Provide one or multiple Tabelog restaurant review URLs. ``` { "urls": [ "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/", "https://tabelog.com/en/kyoto/A2601/A260201/26041019/dtlrvwlst/" ], "maxItemsPerUrl": 50 } ``` Each URL is processed independently. *** ### 📄 Supported Review URLs | 対応URL The scraper supports Tabelog restaurant review-list pages such as: ``` https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/ ``` Existing sorting and filtering parameters are preserved during pagination, including parameters such as: ``` sby srt use_type ``` This means your original review sorting/filter configuration remains active while the Actor navigates through subsequent pages. *** ### 📊 Extracted Review Data | 取得データ | Field | Description | 日本語 | | --------------------- | -------------------- | --------- | | reviewerName | Reviewer name | 口コミ投稿者 | | reviewerUrl | Reviewer profile URL | 投稿者プロフィール | | reviewerCountry | Reviewer country | 国 | | reviewerReviewCount | Reviewer posts | 投稿件数 | | reviewerFollowerCount | Reviewer followers | フォロワー数 | | mealType | Meal type | 食事タイプ | | ratingValue | Overall rating | 総合評価 | | scoreFood | Food score | 料理評価 | | scoreService | Service score | サービス評価 | | scoreAtmosphere | Atmosphere score | 雰囲気評価 | | scoreCost | Cost performance | コスパ評価 | | scoreDrinks | Drinks score | ドリンク評価 | | paymentAmount | Amount spent | 支払金額 | | visitDate | Visit date | 訪問日 | | visitCount | Visit count | 訪問回数 | | title | Review title | 口コミタイトル | | comment | Review comment | 口コミ本文 | | reviewUrl | Review URL | 口コミURL | | photoCount | Number of photos | 写真枚数 | *** ### ⭐ Detailed Rating Breakdown | 詳細評価 Tabelog reviews can contain multiple rating categories. The Actor extracts: 🍱 **Food — 料理** 👨‍🍳 **Service — サービス** 🏮 **Atmosphere — 雰囲気** 💰 **Cost Performance — コストパフォーマンス** 🍷 **Drinks — ドリンク** ⭐ **Overall Rating — 総合評価** This makes the dataset ideal for detailed restaurant performance analysis. *** ### 📦 Example Output | 出力例 ``` { "source": "tabelog", "sourceUrl": "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/", "pageUrl": "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/?PG=2", "pageNumber": 2, "reviewId": "12345678", "bookmarkId": "987654", "reviewerName": "John", "reviewerUrl": "https://tabelog.com/en/user/...", "reviewerCountry": "United States", "reviewerReviewCount": 125, "reviewerFollowerCount": 45, "mealType": "Dinner", "ratingValue": 4.5, "scoreFood": 4.5, "scoreService": 4.0, "scoreAtmosphere": 4.5, "scoreCost": 4.0, "scoreDrinks": 4.5, "paymentAmount": "¥8,000 - ¥9,999", "visitDate": "2026/07", "visitCount": "1 time", "title": "Excellent Japanese restaurant", "comment": "Amazing food and great atmosphere. Highly recommended.", "reviewUrl": "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/...", "photoCount": 8 } ``` *** ### 📊 Dataset Views | データセットビュー The Actor includes three preconfigured dataset views. #### 📋 Overview | 概要 A simplified table containing: - Reviewer - Country - Meal - Rating - Amount Spent - Visit Date - Review Title - Comment - Review URL Perfect for quickly browsing and analyzing reviews. #### 📑 Detailed Reviews | 詳細口コミ The detailed dataset includes all available review fields, including reviewer statistics, rating breakdowns, visit information, review IDs and photo counts. #### 🔗 By Source URL | URL別 Analyze extracted reviews by their original input URL. Perfect for comparing multiple restaurants or review pages in the same run. *** ### 💡 Use Cases | 活用例 🍣 **Restaurant Reputation Analysis — 飲食店の評判分析** ⭐ **Customer Review Analysis — 口コミ分析** 📊 **Sentiment Analysis — 感情分析** 🇯🇵 **Japan Food Market Research — 日本の飲食市場調査** 🏆 **Restaurant Competitor Analysis — 競合分析** 💴 **Restaurant Price Analysis — 価格分析** 👥 **Customer Behavior Research — 顧客行動分析** 📈 **Hospitality Intelligence — 飲食業界インテリジェンス** ✈️ **Japan Tourism Research — 日本観光市場調査** 🤖 **AI Training Datasets — AI学習データ** 📊 **Business Intelligence — ビジネスインテリジェンス** *** ### 🚀 Key Features | 主な特徴 ⚡ **Bulk Review URL Scraping** 🇯🇵 **食べログレビュー対応** 📄 **Automatic Pagination** 🔍 **Review Filtering Preservation** ⭐ **Detailed Ratings** 👤 **Reviewer Profiles** 🌍 **Reviewer Country** 💴 **Spending Information** 📅 **Visit Dates** 🍽️ **Meal Types** 📷 **Review Photo Counts** 🔗 **Review URLs** 📊 **Multiple Dataset Views** 🇯🇵 **Japan Residential Proxy Support** 🔁 **Scalable Cloud Execution** *** ### 🌐 Proxy Configuration | プロキシ設定 Tabelog is a Japan-focused website, so the Actor is configured to support Japanese residential proxies. Default configuration: ``` { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "JP" } ``` 🇯🇵 **Japan Proxy — 日本のプロキシ** Using a Japan-based residential proxy can improve reliability when accessing Tabelog pages. *** ### 📤 Output Formats | 出力形式 Export your dataset through Apify in: ✔ JSON ✔ CSV ✔ Excel / XLSX ✔ XML ✔ HTML ✔ RSS *** ### 🔥 Why Use This Tabelog Reviews Scraper? ✔ Built for **Tabelog (食べログ)** review extraction ✔ Bulk review URL scraping ✔ Automatic `?PG=N` pagination ✔ Detailed reviewer information ✔ Full rating breakdown ✔ Visit and spending information ✔ Review title and comment extraction ✔ Reviewer profile information ✔ Japan residential proxy support ✔ Multiple dataset views ✔ No coding required ✔ Ready for automation workflows *** ### 💸 Pricing | 料金 This scraper runs on a **pay-per-result pricing model**. You only pay for successfully extracted review records. 💳 **Price: $2.99 / 1,000 results** *** ### ❓ FAQ | よくある質問 #### Can I scrape multiple Tabelog review URLs? Yes. Add multiple restaurant review URLs to the `urls` input. #### Can I scrape multiple restaurants in one run? Yes. Each review URL is processed independently. #### Does the scraper support pagination? Yes. The Actor automatically follows `?PG=2`, `?PG=3`, etc. #### Are sorting parameters preserved? Yes. Existing parameters such as `sby`, `srt` and `use_type` are preserved across pagination. #### Can I limit the number of reviews? Yes. Use `maxItemsPerUrl`. #### Can I extract reviewer information? Yes. The scraper extracts reviewer names, profiles, countries, review counts and follower counts when available. #### Does it extract rating categories? Yes. Food, service, atmosphere, cost performance, drinks and overall ratings are extracted when available. #### Is a Japan proxy supported? Yes. The default proxy configuration uses Japanese residential IPs. #### Is coding required? No. This is a fully no-code Apify Actor. *** ### ⚠️ Disclaimer | 免責事項 This tool is an independent scraper and is not affiliated with, endorsed by, or sponsored by Tabelog. Tabelog and **食べログ** are trademarks of their respective owners. Users are responsible for complying with Tabelog's terms of service and applicable laws when using this tool. *** ### 🌍 PrimeScrape Ecosystem Built for modern data extraction and automation workflows. 🍣 **Restaurant Intelligence — 飲食店インテリジェンス** ⭐ **Review Intelligence — 口コミインテリジェンス** 🇯🇵 **Japan Market Research — 日本市場調査** 📊 **Market Intelligence — 市場インテリジェンス** 🤖 **AI Datasets — AIデータセット** 📈 **Business Intelligence — ビジネスインテリジェンス** ⚙️ **Automation Pipelines — 自動化** 🌍 **Large-Scale Data Collection — 大規模データ収集** *** ### 📬 Support | サポート ⭐⭐⭐⭐⭐ Leave a review if you enjoy this scraper. 📩 Need custom scraping solutions, enterprise integrations, bulk datasets or dedicated extraction workflows? Contact **PrimeScrape** directly through Apify. # Actor input Schema ## `urls` (type: `array`): One or more Tabelog English-site restaurant review-list URLs to scrape, e.g. https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/. Optional sort/filter query params already on the URL (sby, srt, use\_type, ...) are preserved across pagination. ## `maxItemsPerUrl` (type: `integer`): Maximum number of reviews to collect for each URL in the bulk list above. ## `proxy` (type: `object`): Residential JP proxy strongly recommended (Tabelog is a Japan-focused site). ## Actor input object example ```json { "urls": [ "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/", "https://tabelog.com/en/kyoto/A2601/A260201/26041019/dtlrvwlst/" ], "maxItemsPerUrl": 50, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "JP" } } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/", "https://tabelog.com/en/kyoto/A2601/A260201/26041019/dtlrvwlst/" ], "maxItemsPerUrl": 50, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "JP" } }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/tabelog-restaurant-reviews-scraper-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": [ "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/", "https://tabelog.com/en/kyoto/A2601/A260201/26041019/dtlrvwlst/", ], "maxItemsPerUrl": 50, "proxy": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "JP", }, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/tabelog-restaurant-reviews-scraper-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://tabelog.com/en/kyoto/A2601/A260304/26042130/dtlrvwlst/", "https://tabelog.com/en/kyoto/A2601/A260201/26041019/dtlrvwlst/" ], "maxItemsPerUrl": 50, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "JP" } }' | apify call delectable_incubator/tabelog-restaurant-reviews-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,delectable_incubator/tabelog-restaurant-reviews-scraper-low-cost" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/mbLyQf90XrlVRDKA8/builds/xkWNcbRJxg2gCBqTx/openapi.json