# Booking Reviews Scraper - Guest Reviews & Scores (`jdtpnjtp/booking-reviews-scraper`) Actor Scrape Booking.com guest reviews for any hotel: scores, positive/negative text, reviewer country, room type, stay length, partner replies. Real-time data, all review filters, full pagination. Pay only per review - no subscription. - **URL**: https://apify.com/jdtpnjtp/booking-reviews-scraper.md - **Developed by:** [Data Forge](https://apify.com/jdtpnjtp) (community) - **Categories:** Travel, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.90 / 1,000 guest reviews This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ## Booking.com Reviews Scraper - Guest Reviews & Scores **Bulk-pull guest reviews for any Booking.com hotel in one run - scores, what guests liked and disliked, reviewer country, room type, length of stay, and the hotel's reply.** Type a hotel, set how many reviews you want, and export clean, structured rows. No login, no subscription, pay only per review. --- ### What this actor does Give it a hotel (a slug like `le-bristol-paris`, a hotel name, or a Booking.com URL) and it paginates the property's guest reviews, pulling everything you need to analyze sentiment, spot patterns, and benchmark a property: - 📊 **Score** - the guest review score (1-10) for each stay - 👍 **Liked / 👎 Disliked** - the positive and negative free text, separated - 🙋 **Reviewer** - name, country, traveller type (couple, solo, family, group) - 🛏 **Stay context** - room type, number of nights, check-in date, season - 🕒 **Timing** - the review date (converted from epoch to a clean `YYYY-MM-DD`) - 👏 **Engagement** - helpful votes, and the hotel's `partner_reply` (in the `data` payload) Need reviews for several hotels at once? Paste their URLs into **Start URLs** - every hotel's reviews land in the same dataset, ready to compare side by side. ### Input | Field | What it does | |---|---| | **Hotel** | Slug, hotel name, or Booking.com hotel URL. Optional if you use Start URLs. | | **Start URLs** | Paste several Booking.com hotel URLs to pull reviews for each. | | **Max reviews** | Cap per hotel. The actor paginates Booking.com (up to 25/page) to reach it. | | **Sort** | Most relevant, newest first, or oldest first. | | **Reviews per page** | How many reviews to request per upstream page (1-25). | | **Filters** | Guest type, score band, language, season of stay, free-text search. | Friendly-label filters (guest type, score band, season) map to Booking.com's own values for you - pick **Families** or **Wonderful (9+)**, no codes to look up. ### Output One flat row per review, discriminated by `row_type`, with the full payload under `data`. The dataset ships with **two ready-made table tabs** - **Overview** and **Reviews** - so the data reads as a clean spreadsheet you can sort, filter and export to CSV/Excel/JSON in one click. Common columns: `score`, `title`, `positive_text`, `negative_text`, `reviewer_name`, `reviewer_country`, `traveller_type`, `room_type`, `num_nights`, `checkin_date`, `customer_type`, `review_date`, `language`, `helpful_votes`, `review_url`. The `data` payload adds `reviewer_country_code`, `reviewer_avatar`, and the hotel's `partner_reply`. Error rows carry an `error_code` and are **free** - you only pay for real reviews. A run-summary record (`OUTPUT` key) reports review counts and the estimated cost. Live per-event pricing is shown on this actor's Apify Store page. > Tip: combine **score band** and **language** to isolate a segment fast - for example only the English-language "Poor (3-5)" reviews to read exactly what is going wrong. ### Why this actor - **Real reviews, real time** - pulled live from Booking.com, with the hotel's own replies, not a stale snapshot. - **Positive and negative, separated** - clean `positive_text` / `negative_text` columns, ideal for sentiment analysis. - **Every review filter** - guest type, score band, language, season, text search. Friendly labels, no codes. - **Bulk across hotels** - one hotel or a whole list, all in one dataset to compare. - **Clean tabs** - an export-ready Reviews table, not a wall of mixed columns. - **You only pay for results** - errors and your own misconfigurations are never charged. ### More Booking.com tools Part of our Booking.com suite - pick the actor that fits the job: - **[Booking.com Hotels Scraper](https://apify.com/jdtpnjtp/booking-hotels-scraper)** - search any destination for hotels with live prices, scores and full detail pages. - **[Booking.com Airport Taxis Scraper](https://apify.com/jdtpnjtp/booking-airport-taxis-scraper)** - live airport transfer quotes between any two points. ### Need help? Enterprise plans? Custom scraping? I personally support every customer - drop a message on any channel. [![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white)](https://t.me/j4dtpnj2tp) [![WhatsApp](https://img.shields.io/badge/WhatsApp-25D366?style=for-the-badge&logo=whatsapp&logoColor=white)](https://wa.me/380686031542) [![Email](https://img.shields.io/badge/Email-D14836?style=for-the-badge&logo=gmail&logoColor=white)](mailto:jdtpnjtp@gmail.com) # Actor input Schema ## `hotel` (type: `string`): The hotel to pull reviews for. Accepts a **slug** (`le-bristol-paris`), a **hotel name** (`Le Bristol Paris`), or a full **Booking.com hotel URL** - the actor resolves it automatically. Optional if you provide Start URLs below. ## `startUrls` (type: `array`): Paste specific Booking.com hotel URLs to pull reviews for each, in addition to (or instead of) the single Hotel above. Reviews for every hotel land in the same dataset. ## `maxReviews` (type: `integer`): Upper bound on reviews pulled per hotel. 20 = quick sample, 200 = deep dataset. The actor paginates Booking.com (up to 25/page) under the hood to reach this number. ## `sort` (type: `string`): Order of reviews. Most relevant surfaces the reviews Booking highlights; the recency sorts walk newest-first or oldest-first. ## `count` (type: `integer`): How many reviews to request per upstream page (1-25). Lower it for tighter control over partial runs; the default works for most jobs. ## `customerType` (type: `string`): Keep only reviews from a single traveller segment. ## `scoreRange` (type: `string`): Keep only reviews in a single Booking.com score band. ## `language` (type: `string`): Keep only reviews written in this language. Pick from the list or type any 2-letter ISO code. ## `timeOfYear` (type: `string`): Keep only reviews from stays in this part of the year. ## `text` (type: `string`): Keep only reviews mentioning this phrase (e.g. `breakfast`, `view`, `noise`). Free text. ## Actor input object example ```json { "hotel": "le-bristol-paris", "startUrls": [ "https://www.booking.com/hotel/fr/le-bristol-paris.html" ], "maxReviews": 50, "sort": "most_relevant", "count": 10, "customerType": "", "scoreRange": "", "language": "", "timeOfYear": "", "text": "breakfast" } ```` # Actor output Schema ## `dataset` (type: `string`): The default dataset. Each row carries one Booking.com guest review, discriminated by row\_type, with common fields flattened to top-level columns and the full payload under `data`. Use the Overview / Reviews tabs to view clean tables. ## `summary` (type: `string`): A single JSON object written to the default key-value store under the OUTPUT key. Contains hotels, review\_results, errors, total\_rows, estimated\_cost\_usd, limit\_reached, and actor\_version. # 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 = { "hotel": "le-bristol-paris", "startUrls": [ "https://www.booking.com/hotel/fr/le-bristol-paris.html" ], "maxReviews": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("jdtpnjtp/booking-reviews-scraper").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 = { "hotel": "le-bristol-paris", "startUrls": ["https://www.booking.com/hotel/fr/le-bristol-paris.html"], "maxReviews": 50, } # Run the Actor and wait for it to finish run = client.actor("jdtpnjtp/booking-reviews-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "hotel": "le-bristol-paris", "startUrls": [ "https://www.booking.com/hotel/fr/le-bristol-paris.html" ], "maxReviews": 50 }' | apify call jdtpnjtp/booking-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jdtpnjtp/booking-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Booking Reviews Scraper - Guest Reviews & Scores", "description": "Scrape Booking.com guest reviews for any hotel: scores, positive/negative text, reviewer country, room type, stay length, partner replies. Real-time data, all review filters, full pagination. Pay only per review - no subscription.", "version": "1.0", "x-build-id": "uDflGbsen1kPxJxT0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jdtpnjtp~booking-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jdtpnjtp-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jdtpnjtp~booking-reviews-scraper/runs": { "post": { "operationId": "runs-sync-jdtpnjtp-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jdtpnjtp~booking-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-jdtpnjtp-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "hotel": { "title": "Hotel (slug, name, or Booking.com URL)", "type": "string", "description": "The hotel to pull reviews for. Accepts a **slug** (`le-bristol-paris`), a **hotel name** (`Le Bristol Paris`), or a full **Booking.com hotel URL** - the actor resolves it automatically. Optional if you provide Start URLs below." }, "startUrls": { "title": "Start URLs (Booking.com hotel pages)", "uniqueItems": true, "type": "array", "description": "Paste specific Booking.com hotel URLs to pull reviews for each, in addition to (or instead of) the single Hotel above. Reviews for every hotel land in the same dataset.", "items": { "type": "string" } }, "maxReviews": { "title": "Max reviews (per hotel)", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Upper bound on reviews pulled per hotel. 20 = quick sample, 200 = deep dataset. The actor paginates Booking.com (up to 25/page) under the hood to reach this number.", "default": 50 }, "sort": { "title": "Sort reviews by", "enum": [ "most_relevant", "f_recent_desc", "f_recent_asc" ], "type": "string", "description": "Order of reviews. Most relevant surfaces the reviews Booking highlights; the recency sorts walk newest-first or oldest-first.", "default": "most_relevant" }, "count": { "title": "Reviews per page", "minimum": 1, "maximum": 25, "type": "integer", "description": "How many reviews to request per upstream page (1-25). Lower it for tighter control over partial runs; the default works for most jobs.", "default": 10 }, "customerType": { "title": "Guest type", "enum": [ "", "FAMILIES", "COUPLES", "SOLO_TRAVELLERS", "GROUPS" ], "type": "string", "description": "Keep only reviews from a single traveller segment.", "default": "" }, "scoreRange": { "title": "Score band", "enum": [ "", "REVIEW_ADJ_SUPERB", "REVIEW_ADJ_GOOD", "REVIEW_ADJ_AVERAGE_PASSABLE", "REVIEW_ADJ_POOR", "REVIEW_ADJ_VERY_POOR" ], "type": "string", "description": "Keep only reviews in a single Booking.com score band.", "default": "" }, "language": { "title": "Review language", "type": "string", "description": "Keep only reviews written in this language. Pick from the list or type any 2-letter ISO code.", "default": "" }, "timeOfYear": { "title": "Season of stay", "enum": [ "", "_03_05", "_06_08", "_09_11", "_12_02" ], "type": "string", "description": "Keep only reviews from stays in this part of the year.", "default": "" }, "text": { "title": "Text search", "type": "string", "description": "Keep only reviews mentioning this phrase (e.g. `breakfast`, `view`, `noise`). Free text." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```