# Airbnb Reviews Scraper (`knagymate/airbnb-reviews-scraper`) Actor Scrape Airbnb listing reviews at scale with structured data including ratings, translated comments, host responses, and category breakdowns. Extract guest sentiment, reviewer details, and listing metrics for market research, competitor analysis, and AI training. Export to JSON, CSV, Excel, or APIs. - **URL**: https://apify.com/knagymate/airbnb-reviews-scraper.md - **Developed by:** [knagymate](https://apify.com/knagymate) (community) - **Categories:** Travel, Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 83.3% runs succeeded, 1 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $2.00 / 1,000 results 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 ## Airbnb Reviews Scraper \| Extract Guest Reviews, Ratings & Host Responses at Scale Scrape **Airbnb listing reviews** in seconds and transform them into structured datasets for **data analysis, market research, sentiment analysis, and travel intelligence**. This actor extracts real guest reviews including ratings, translated content, host responses, reviewer details, and category rating breakdowns --- ready for export to **JSON, CSV, Excel, or API pipelines**. ------------------------------------------------------------------------ ### πŸ”₯ Why use this Airbnb scraper? - Extract **real Airbnb reviews at scale** - Get **ratings, review text, and translated content** - Capture **host responses, reviewer names, and category ratings** - Ideal for **AI training, analytics, dashboards, and competitor research** - Built for **fast, reliable, and scalable scraping** ------------------------------------------------------------------------ ### πŸš€ Features - Scrape reviews from any **Airbnb listing URL** - Extract structured data including: - Review text (original + translated) - Star rating - Host responses (original + translated) - Reviewer and host (reviewee) names - Listing rating average and total rating count - Category rating breakdown (cleanliness, accuracy, check-in, communication, location, value) - Sorting options: - Most relevant - Most recent - Highest rated - Lowest rated - Incremental scraping via **cutoff date** - Export to **JSON, CSV, Excel, or integrate via API** ------------------------------------------------------------------------ ### πŸ“₯ Input ``` json { "startUrl": "https://www.airbnb.hu/rooms/1453066826493838112", "maxReviews": 100, "sortBy": "mostRecent", "cutoffDate": "2024-01-01" } ```` #### Input parameters - **startUrl** (required): Airbnb listing page URL - **maxReviews**: Maximum number of reviews to scrape (default: 100) - **sortBy**: - `mostRelevant` - `mostRecent` - `highestRated` - `lowestRated` - **cutoffDate**: Only return reviews newer than this date (when set, sorting automatically switches to `mostRecent`) *** ### πŸ“€ Output Each review is returned as structured data: ```json { "id": "1491969879716769009", "createdAt": "2025-08-20 12:37:49+00:00", "comment": "Tolle Lage, schΓΆner ruhiger Strand.", "localizedComment": "Great location, nice quiet beach.", "response": null, "localizedResponse": null, "language": "de", "rating": 4.0, "reviewerName": "Anita", "revieweeName": "Nativoo", "ratingAverage": 4.69, "ratingCount": 49, "ratingDetails": [ { "rating": 4.7, "category": "CLEANLINESS" }, { "rating": 4.8, "category": "ACCURACY" }, { "rating": 4.6, "category": "CHECKIN" }, { "rating": 4.7, "category": "COMMUNICATION" }, { "rating": 4.6, "category": "LOCATION" }, { "rating": 4.6, "category": "VALUE" } ] } ``` *** ### πŸ“Š Use cases #### πŸ“ˆ Market research & analytics Analyze guest sentiment, satisfaction trends, and review patterns across listings. #### 🏠 Competitor benchmarking Compare listings based on ratings, feedback, and guest experience. #### πŸ€– AI & data pipelines Use structured reviews for: - NLP models - Sentiment analysis - Recommendation systems #### πŸ“‰ Reputation monitoring Track how listing perception changes over time. *** ### ⚠️ Notes - Airbnb often provides **auto-translated reviews** - Some fields may be missing depending on the review - Popular listings can have **hundreds of reviews** - Some reviews may include host responses and reviewer metadata - Always comply with Airbnb Terms of Service *** ### πŸ’‘ Pro tips - Use `mostRecent` + `cutoffDate` for **incremental scraping** - Run multiple jobs for **large-scale datasets** - Use `ratingDetails` to analyze listing performance by category - Combine with other review sources for richer datasets (multi-platform analysis): - Fast Agoda Reviews Scraper β†’ https://apify.com/knagymate/fast-agoda-reviews-scraper - Trip.com Reviews Scraper β†’ https://apify.com/knagymate/trip-com-reviews-scraper - Klook Reviews Scraper β†’ https://apify.com/knagymate/klook-reviews-scraper - Explore all scrapers β†’ https://apify.com/knagymate # Actor input Schema ## `startUrl` (type: `string`): URL of the Airbnb listing to scrape reviews from. ## `maxReviews` (type: `integer`): Maximum number of reviews to scrape. ## `sortBy` (type: `string`): Sorting order of reviews. ## `cutoffDate` (type: `string`): Only reviews newer than this date will be returned. ## Actor input object example ```json { "startUrl": "https://www.airbnb.hu/rooms/1453066826493838112", "maxReviews": 100, "sortBy": "mostRelevant" } ``` # 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 = { "cutoffDate": "" }; // Run the Actor and wait for it to finish const run = await client.actor("knagymate/airbnb-reviews-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "cutoffDate": "" } # Run the Actor and wait for it to finish run = client.actor("knagymate/airbnb-reviews-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "cutoffDate": "" }' | apify call knagymate/airbnb-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=knagymate/airbnb-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Airbnb Reviews Scraper", "description": "Scrape Airbnb listing reviews at scale with structured data including ratings, translated comments, host responses, and category breakdowns. Extract guest sentiment, reviewer details, and listing metrics for market research, competitor analysis, and AI training. Export to JSON, CSV, Excel, or APIs.", "version": "0.0", "x-build-id": "H3BLX1ZfXNJRQEJHY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/knagymate~airbnb-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-knagymate-airbnb-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/knagymate~airbnb-reviews-scraper/runs": { "post": { "operationId": "runs-sync-knagymate-airbnb-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/knagymate~airbnb-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-knagymate-airbnb-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrl" ], "properties": { "startUrl": { "title": "Airbnb Listing URL", "type": "string", "description": "URL of the Airbnb listing to scrape reviews from.", "default": "https://www.airbnb.hu/rooms/1453066826493838112" }, "maxReviews": { "title": "Maximum number of reviews", "minimum": 1, "type": "integer", "description": "Maximum number of reviews to scrape.", "default": 100 }, "sortBy": { "title": "Sort reviews by", "enum": [ "mostRelevant", "mostRecent", "highestRated", "lowestRated" ], "type": "string", "description": "Sorting order of reviews.", "default": "mostRelevant" }, "cutoffDate": { "title": "Cutoff date", "type": "string", "description": "Only reviews newer than this date will be returned." } } }, "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 } } } } } } } } } } ```