# Booking.com Hotel Reviews Scraper (`powerai/booking-hotel-reviews-scraper`) Actor Export guest reviews for a Booking.com property—scores, pros and cons, and stay details—ready for CX and reputation workflows. - **URL**: https://apify.com/powerai/booking-hotel-reviews-scraper.md - **Developed by:** [PowerAI](https://apify.com/powerai) (community) - **Categories:** E-commerce, Travel, Integrations - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Hotel Reviews Scraper Pull **guest reviews** for a single property into structured rows: scores, pros/cons, stay context, and reviewer metadata—so you can use them in spreadsheets, dashboards, or support without copying from the site. ### Who it’s for - **Guest experience & operations** teams who need a snapshot of what guests say about one hotel. - **Revenue & brand** stakeholders tracking sentiment and recurring themes for a property. - **Agencies** reporting on a client or competitor listing. ### What you can do with it - **Read many reviews at once** for a known property ID. - **Choose how many reviews** to collect so runs stay predictable. - **Optionally sort** (when you already know a valid sort option from your Booking workflow) and **language** for the response. ### How it works (in plain terms) You provide the **hotel (property) ID** from your usual Booking data flow (for example after a hotel search). The tool **pages through review results** until it reaches your **maximum number of rows** or there are no more reviews on the next page. ### Input | Field | Required | What it means | |-------|----------|----------------| | **Hotel ID** (`hotel_id`) | Yes | The property identifier (same as in search results for that hotel). | | **Maximum results** (`maxResults`) | No | Upper limit on how many reviews to collect across pages (default **100**). | | **Sort option** (`sort_option_id`) | No | How reviews are ordered (e.g. most relevant or by date), when your workflow supplies a valid value. | | **Language** (`languagecode`) | No | Response language (e.g. `en-us`). | ### Output - **One row per review** per page, until your cap or the end of results. - **Identity & timing**: review and property IDs, review **date**, review **language**, and guest **country** when present. - **Ratings & summary**: overall **score** (`average_score`), short **title**, and flags such as moderation or anonymous-style fields when the platform returns them. - **Verbatim feedback**: **pros** and **cons** (plus translated fields when empty or filled), and **hotel response** when the property has replied. - **Stay context**: **travel purpose**, **room name**, **check-in / check-out**, **nights**, and **room photo** links under `stayed_room_info` when available. - **Reviewer profile**: **name**, **avatar**, **group type** (e.g. friends, family), review counts, and helpful votes. - **Engagement**: **tags**, **reviewer-uploaded photos**, **helpful votes** on the review. - Every row adds **when that record was written** (`scrapedAt`) so you know which export run it came from. Illustrative shape only: some reviews omit or shorten fields; image URLs are long and may expire. #### Sample output (one dataset row, trimmed) ```json { "review_id": 5205721280, "hotel_id": 5955189, "title": "Pleasant", "date": "2025-09-02 05:24:15", "languagecode": "en-gb", "countrycode": "in", "average_score": 2.40000009536743, "travel_purpose": "other", "pros": "1.Whenever help was asked staff responded well…\n2.Professional behaviour of staff\n3.Assistance was provided in shifting luggage…", "cons": "1.Rooms are congested\n2.…bedsheets…\n3.…breakfast…\n4.…advance payment…", "pros_translated": "", "cons_translated": "", "title_translated": "", "hotelier_response": "", "tags": [], "reviewer_photos": [], "helpful_vote_count": 0, "stayed_room_info": { "room_name": "Deluxe Room", "room_id": 595518907, "checkin": "2025-08-29", "checkout": "2025-09-01", "num_nights": 3, "photo": { "photo_id": 613758060, "url_square60": "https://cf.bstatic.com/xdata/images/hotel/square60/613758060.jpg?…" } }, "author": { "name": "Aneeta", "user_id": 958511072, "type_string": "Group of friends", "countrycode": "in", "avatar": "https://lh3.googleusercontent.com/a/ACg8ocItbpgTDUZ3z79qwU1o8VK2yZh7WHNo_QucX8MiZMKNHAjxmw=s96-c64" }, "scrapedAt": "2026-03-25T05:49:29.224Z" } ```` ### Good to know - The **hotel ID** must match a real property; wrong IDs return empty or error responses. - Review text and fields can vary by guest, language, and platform updates. - Use data in line with Booking.com’s terms and the laws that apply to you. # Actor input Schema ## `hotel_id` (type: `string`): Property ID from hotel search or other Booking flows. ## `maxResults` (type: `integer`): Max review rows to collect across pages. ## `sort_option_id` (type: `string`): From getHotelReviewsSortOption, e.g. sort\_most\_relevant or sort\_recent\_desc. ## `languagecode` (type: `string`): e.g. en-us ## Actor input object example ```json { "hotel_id": "5955189", "maxResults": 25, "sort_option_id": "sort_most_relevant", "languagecode": "en-us" } ``` # 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_id": "5955189", "maxResults": 25, "sort_option_id": "sort_most_relevant", "languagecode": "en-us" }; // Run the Actor and wait for it to finish const run = await client.actor("powerai/booking-hotel-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_id": "5955189", "maxResults": 25, "sort_option_id": "sort_most_relevant", "languagecode": "en-us", } # Run the Actor and wait for it to finish run = client.actor("powerai/booking-hotel-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_id": "5955189", "maxResults": 25, "sort_option_id": "sort_most_relevant", "languagecode": "en-us" }' | apify call powerai/booking-hotel-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=powerai/booking-hotel-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Booking.com Hotel Reviews Scraper", "description": "Export guest reviews for a Booking.com property—scores, pros and cons, and stay details—ready for CX and reputation workflows.", "version": "0.0", "x-build-id": "tf4jaqCMcXtCtDnxk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/powerai~booking-hotel-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-powerai-booking-hotel-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/powerai~booking-hotel-reviews-scraper/runs": { "post": { "operationId": "runs-sync-powerai-booking-hotel-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/powerai~booking-hotel-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-powerai-booking-hotel-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "hotel_id" ], "properties": { "hotel_id": { "title": "Hotel ID", "type": "string", "description": "Property ID from hotel search or other Booking flows.", "default": "5955189" }, "maxResults": { "title": "Maximum results", "minimum": 25, "maximum": 10000, "type": "integer", "description": "Max review rows to collect across pages.", "default": 25 }, "sort_option_id": { "title": "Sort option", "type": "string", "description": "From getHotelReviewsSortOption, e.g. sort_most_relevant or sort_recent_desc.", "default": "sort_most_relevant" }, "languagecode": { "title": "Language code", "type": "string", "description": "e.g. en-us", "default": "en-us" } } }, "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 } } } } } } } } } } ```