# Booking.com Reviews Scraper β€” Most Comprehensive (`scrapersdelight/booking-reviews-scraper`) Actor From $0.20 per 1,000 reviews β€” 10Γ— cheaper than typical $2/1k actors. Scrape Booking.com hotel reviews: title, positive/negative text, 0–10 score, stay date, room, nights, traveler type, country, with date/language/keyword filters + a new-review monitor with alerts. No login or API key. - **URL**: https://apify.com/scrapersdelight/booking-reviews-scraper.md - **Developed by:** [Scrapers Delight](https://apify.com/scrapersdelight) (community) - **Categories:** AI, E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 β€” Most Comprehensive **Scrape Booking.com hotel reviews β€” title, positive AND negative text, 0–10 score, stay date, room type, nights, traveler type, country, plus the hotel's full sub-score breakdown β€” at $0.20 per 1,000 reviews. Filter by date range (with early-stop), language, keyword, score band, or traveler type, and run it on a schedule as a new-review monitor with Slack/email/webhook alerts.** ### Why this one? | | **This actor** | Leading Booking.com reviews scraper | |---|---|---| | **Price per 1,000 reviews** | **$0.20** (pay-per-event) | $2.00 (pay per result) | | Positive / negative text split | βœ… separate fields | βœ… | | Review title + 0–10 score | βœ… | βœ… | | Hotel sub-score breakdown (cleanliness, comfort, location, facilities, staff, value, Wi-Fi) | βœ… on every row | not advertised | | Stay metadata (room type, nights, stay month, trip type, traveler type, mobile flag) | βœ… parsed into separate fields | not advertised | | Reviewer country + review/helpful counts | βœ… | partial | | Language filter (server-side, 35+ languages or ALL) | βœ… | βœ… | | Date-range filter with **early-stop** (stops paginating β€” you don't pay for skipped pages) | βœ… | not advertised | | Keyword filter ("breakfast", "noise", "bed bugs"…) | βœ… | not advertised | | Score-band filter (e.g. only 0–5 complaints) | βœ… | not advertised | | Sort control (newest / oldest / featured / best / worst) | βœ… | not advertised | | **New-review monitor** + Slack / email / webhook alerts | βœ… built in | ❌ | | GDPR toggle (`stripPersonalData`, ON by default) | βœ… | ❌ | | Failure handling | per-page retry with fresh browser fingerprint + rotating proxy session β€” a single bad page never kills the run | unknown | | Login / API key needed | none | none | --- ### What does Booking.com Reviews Scraper do? It reads Booking.com's own server-rendered review pages (no login, no API key, no fragile headless browser) and returns clean, structured review rows you can export to **JSON, CSV, Excel, or fetch via the Apify API**. Paste any hotel URL β€” that's it. - πŸ“ **The full review** β€” title, the positive ("liked") and negative ("disliked") texts as separate fields, plus the raw tag list. - ⭐ **Scores** β€” the review's 0–10 score, and the hotel's overall score **with the complete category breakdown** (cleanliness, comfort, location, facilities, staff, value for money, free Wi-Fi) on every row. - 🧳 **Stay context** β€” room type, number of nights, stay month, leisure vs business, couple/solo/family/group, submitted-from-mobile. - 🌍 **Reviewer context** β€” country + country code, age group when shown, how many reviews and helpful votes the reviewer has (first name only if you opt out of `stripPersonalData`). - πŸ—“οΈ **Date filters that save money** β€” `dateFrom`/`dateTo` with newest-first early-stop: pagination halts the moment reviews get older than your window. - πŸ”Ž **Keyword & score filters** β€” isolate every "breakfast" complaint or every ≀5/10 review across thousands of stays. - πŸ”” **New-review monitor** β€” schedule it and get **Slack / email / webhook alerts** for each fresh review per hotel (reputation watch for your own or competitor hotels). ### What data does it extract? One dataset row per review: - πŸ†” `review_id` (stable dedup hash), `page_number`, `scraped_at` - 🏨 `hotel_name`, `hotel_cc`, `hotel_slug`, `hotel_url`, `reviews_url` - ⭐ `hotel_score`, `hotel_total_reviews`, `hotel_reviews_in_language`, `hotel_score_breakdown {cleanliness, comfort, location, facilities, staff, value_for_money, free_wifi}` - πŸ“ `title`, `title_is_scoreword`, `positive_text`, `negative_text`, `owner_response`* - πŸ”’ `score` (0–10), `score_best` - πŸ—“οΈ `review_date` (ISO), `review_date_raw`, `stayed_date` (e.g. "June 2026") - 🧳 `nights`, `trip_type` (leisure/business), `traveler_type` (couple/solo/family/group), `room_type`, `submitted_via_mobile`, `tags_raw[]` - 🌍 `reviewer_country`, `reviewer_country_code`, `reviewer_age_group`, `reviewer_review_count`, `reviewer_helpful_votes`, `reviewer_name` (null unless you disable `stripPersonalData`) - 🌐 `language` (the language scope you requested), ✨ `is_new` (monitor mode) *\* Booking.com's review pages only rarely include property responses; the field is populated whenever the page shows one.* ### Who is it for? - 🏨 **Hoteliers & revenue managers** watching their own and competitors' guest sentiment in near-real-time. - πŸ“Š **Hospitality analysts & consultants** building sentiment datasets across markets, brands, or seasons. - πŸ€– **AI / NLP teams** that need labeled positive/negative hotel text at scale (the pos/neg split is free labeling). - πŸ›ŽοΈ **Reputation & CX tools** wiring new reviews into Slack, dashboards, or ticketing via webhook. - 🏠 **OTA / travel product teams** doing competitive research on inventory quality. ### How to use it (step by step) 1. Click **Try for free**. 2. Paste one or more **hotel URLs** β€” any form works: `https://www.booking.com/hotel/gb/royal-national.html`, the reviews URL, or just `gb/royal-national`. 3. *(Optional)* set filters: date range, language (`all` = every language), keyword, score band, trip/traveler type. 4. Click **Start**, then open the **Dataset** tab to view/export. 5. *(Optional)* set `monitorMode: true` + an Apify **Schedule** + a Slack/webhook/email channel to get pinged on every new review. #### Quick start ```json { "startUrls": ["https://www.booking.com/hotel/gb/royal-national.html"], "maxReviewsPerHotel": 200 } ```` #### Competitor reputation monitor ```json { "startUrls": [ "https://www.booking.com/hotel/us/pod.html", "https://www.booking.com/hotel/us/your-competitor.html" ], "monitorMode": true, "slackWebhookUrl": "https://hooks.slack.com/services/…" } ``` #### Complaint mining (last 90 days, low scores only) ```json { "startUrls": ["https://www.booking.com/hotel/gb/royal-national.html"], "maxReviewsPerHotel": 0, "dateFrom": "2026-03-14", "maxScore": 5, "language": "all" } ``` ### Input | Field | What it does | |-------|--------------| | `startUrls` | hotel page URLs (or `cc/slug` shorthand), one per hotel | | `maxReviewsPerHotel` | cap per hotel (0 = all reviews; default 50) | | `sortBy` | `newest` (default) Β· `oldest` Β· `featured` Β· `score_high` Β· `score_low` | | `language` | server-side language scope: `all` (default, most reviews), `en`, `fr`, `de`, … or `default` | | `dateFrom` / `dateTo` | review-date window (YYYY-MM-DD); newest-first runs early-stop below `dateFrom` | | `keyword` | only reviews mentioning this word (title/positive/negative/room) | | `tripType` / `travelerType` | leisure/business Β· couple/solo/family/group | | `minScore` / `maxScore` | 0–10 score band | | `stripPersonalData` | ON by default β€” drops the reviewer's first name (GDPR-friendly) | | `maxRetriesPerPage` | anti-WAF retry budget per page (default 10) | | `monitorMode`, `alertOnNewReview` | recurring new-review watcher | | `webhookUrl`, `slackWebhookUrl`, `emailRecipients` | alert channels | | `proxyConfiguration` | Apify proxy (datacenter works; RESIDENTIAL for very large runs) | ### Output Example record (truncated): ```json { "hotel_name": "Royal National Hotel", "hotel_score": 7.9, "hotel_total_reviews": 19601, "hotel_score_breakdown": { "cleanliness": 8.3, "comfort": 8.3, "location": 9.1, "facilities": 7.8, "staff": 8.6, "value_for_money": 7.9, "free_wifi": 8.2 }, "title": "Clean rooms, great location and pleasant staff.", "positive_text": "The location was ideal. Walking distance to Russell Square Underground…", "negative_text": "Our tv did not work correctly…", "score": 8, "score_best": 10, "review_date": "2026-06-12", "stayed_date": "June 2026", "nights": 3, "trip_type": "leisure", "traveler_type": "Couple", "room_type": "Standard Plus Double Room", "submitted_via_mobile": true, "reviewer_country": "Ireland", "reviewer_country_code": "ie", "reviewer_review_count": 1, "reviewer_helpful_votes": 0, "personal_data_stripped": true } ``` ### How much does it cost? Pay-per-event β€” you pay for what you pull, no subscription: | Event | What it covers | Price | |-------|----------------|-------| | `lot-scraped` | each review returned | **$0.0002** (= **$0.20 / 1,000 reviews**) | | `monitor-run-completed` | each scheduled watch run | $0.02 / run | | `new-lot-detected` | each new review found by the monitor | $0.005 / review | | `alert-delivered` | each Slack/email/webhook push | $0.002 / alert | *(Final per-event prices are set on the actor's pricing page.)* **That's $0.20 per 1,000 reviews β€” about 10Γ— cheaper than the typical $2.00/1k Booking reviews actor.** A daily monitor on 5 hotels costs roughly $0.10/day plus the new reviews it actually finds. ### Is it legal? (GDPR & ToS note β€” read this) - **Personal data:** reviews can contain personal data (the reviewer's first name and nationality). By default this actor **strips the reviewer's name** (`stripPersonalData: true`). Only disable that if you have a lawful basis under GDPR/your local privacy law to process reviewer names, and honor deletion requests. - **Terms of Service:** Booking.com's ToS does not welcome automated collection; this is a **gray area** that this actor does not resolve for you. The data scraped is publicly visible without any login, and courts have repeatedly found scraping public pages lawful (e.g. *hiQ v. LinkedIn*) β€” but **you are responsible for your own use case, jurisdiction, and volume**. Use the data for analysis/monitoring, don't republish reviews wholesale. ### FAQ **Do I need a Booking.com account, login, or API key?** No. The reviews are served on public pages β€” the actor reads them directly. No login or API key. **Which hotels can it scrape?** Any property on Booking.com with guest reviews β€” paste its hotel page URL in any country/locale form. **How many reviews can I get per hotel?** All of them (set `maxReviewsPerHotel: 0`). Pages hold 25 reviews each and the actor paginates gaplessly until the end, your cap, or your date window. **Does it get non-English reviews?** Yes β€” the default `language: "all"` returns every language (e.g. 19,601 vs only 6,547 English reviews for a large London hotel). Set a 2-letter code to scope to one language. **Can I get only recent reviews?** Yes β€” `dateFrom` (+ default newest-first sort) stops pagination the moment it reaches older reviews, so a "last 30 days" pull on a 20k-review hotel costs only a few pages. **Can I monitor a hotel for new reviews?** Yes. `monitorMode: true` + an Apify Schedule. The actor remembers what it has seen per hotel (in a persistent named store) and outputs/alerts **only new reviews**, to Slack, email, or any webhook. **Does it include review scores and sub-scores?** Every review carries its 0–10 score, and every row carries the hotel's full category breakdown (cleanliness, comfort, location, facilities, staff, value, Wi-Fi). Booking.com does not publish per-review category scores on its review pages. **Does it include the hotel's reply to a review?** The `owner_response` field is populated whenever Booking.com's review page shows one β€” most properties' responses are not exposed there, so expect it to be null for most rows. **Why do I see "WAF/odd response … retry" lines in the log?** Booking.com randomly challenges ~2 of 3 requests with an AWS WAF page. The actor retries with a fresh browser fingerprint, cookie jar, and proxy session β€” a clean page typically arrives within 1–4 tries. That's normal and already priced in. **What if one hotel URL fails?** The run keeps going β€” each hotel is isolated, and whatever was collected is always pushed. A failed page logs a warning instead of crashing the run. **Is the reviewer's name included?** Not by default (`stripPersonalData: true`). Disable it only if you have a lawful basis to process names; nationality, review counts, and helpful votes are kept either way. **How do I export the data?** JSON, CSV, Excel, HTML, or RSS from the Dataset tab, or via the Apify API. Webhooks/Slack/email for monitor alerts. **Can I run it from my own code / Make / Zapier / n8n?** Yes β€” call it via the Apify API or client libraries, or wire the alert webhook into any automation tool. ### You might also like - πŸ›« Airline & travel review scrapers - 🏠 Vacation-rental review & listing scrapers - ⭐ Google Maps / TripAdvisor review scrapers ### Feedback Found a missing field or want a new filter? Open an issue on the actor β€” fast fixes and feature requests welcome. # Actor input Schema ## `startUrls` (type: `array`): Booking.com hotel page URLs β€” any of these forms works: https://www.booking.com/hotel/gb/royal-national.html, https://www.booking.com/reviews/gb/hotel/royal-national.html, or the shorthand 'gb/royal-national'. One entry per hotel. ## `maxReviewsPerHotel` (type: `integer`): Hard cap on reviews collected per hotel (cost/safety guard). Defaults to 50 for a fast first run β€” set 0 for ALL reviews of each hotel. ## `sortBy` (type: `string`): Server-side review ordering. 'Newest first' is best for monitors and date-range scrapes (enables early-stop). ## `language` (type: `string`): Server-side language filter (Booking's own r\_lang). 'all' = every language (most reviews). Use a 2-letter code like en, fr, de, es, it, nl, zh, ja… 'default' lets Booking pick. ## `dateFrom` (type: `string`): Only reviews posted ON/AFTER this date. With 'Newest first' the actor stops paginating as soon as it reaches older reviews (early-stop = fewer requests). ## `dateTo` (type: `string`): Only reviews posted ON/BEFORE this date. ## `keyword` (type: `string`): Only reviews whose title/positive/negative text or room type contains this word (case-insensitive). E.g. 'breakfast', 'noise', 'bed bugs'. ## `tripType` (type: `string`): Only leisure or business trips. ## `travelerType` (type: `string`): Only reviews by this traveler type. ## `minScore` (type: `integer`): Only reviews scored at/above this. Combine with maxScore to isolate e.g. complaints (0–5). ## `maxScore` (type: `integer`): Only reviews scored at/below this. ## `stripPersonalData` (type: `boolean`): ON (default): drop the reviewer's first name from the output (nationality, age group, and review counts are kept β€” they don't identify a person). Turn OFF only if you have a lawful basis to process reviewer names. ## `maxRetriesPerPage` (type: `integer`): Booking.com's AWS WAF challenges requests at random (~2 of 3). Each retry uses a fresh cookie jar + proxy session; a clean page usually arrives within 1–4 tries. Raise this if you see 'no clean reviews page' warnings. ## `monitorMode` (type: `boolean`): Recurring watcher: diff against the prior run's seen reviews (per hotel) and output/alert ONLY new reviews. Pair with an Apify Schedule and Sort by = Newest first. ## `alertOnNewReview` (type: `boolean`): In monitor mode, deliver an alert for each new review via the channels below. ## `webhookUrl` (type: `string`): POST endpoint for new-review alert payloads (Make / Zapier / n8n / custom). One JSON body per alert. ## `slackWebhookUrl` (type: `string`): Slack incoming-webhook URL for formatted new-review cards. ## `emailRecipients` (type: `array`): Email addresses for the new-review digest (sent via apify/send-mail). ## `proxyConfiguration` (type: `object`): Proxy settings. Booking.com's WAF challenges requests at random β€” the retry loop beats it with datacenter proxies; switch to RESIDENTIAL for large bulk runs if you see many retries. ## `diagnose` (type: `boolean`): Dev only. Dumps the raw reviews HTML to the key-value store (DEBUG\_REVIEWS\_HTML), logs the parsed first review + a pagination check, then exits. Leave off for normal runs. ## Actor input object example ```json { "startUrls": [ "https://www.booking.com/hotel/gb/royal-national.html", "https://www.booking.com/hotel/us/pod.html" ], "maxReviewsPerHotel": 50, "sortBy": "newest", "language": "all", "tripType": "all", "travelerType": "all", "stripPersonalData": true, "maxRetriesPerPage": 10, "monitorMode": false, "alertOnNewReview": true, "proxyConfiguration": { "useApifyProxy": true }, "diagnose": false } ``` # Actor output Schema ## `items` (type: `string`): The dataset of scraped Booking.com reviews (one review per row). # 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 = { "startUrls": [ "https://www.booking.com/hotel/gb/royal-national.html", "https://www.booking.com/hotel/us/pod.html" ], "maxReviewsPerHotel": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("scrapersdelight/booking-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 = { "startUrls": [ "https://www.booking.com/hotel/gb/royal-national.html", "https://www.booking.com/hotel/us/pod.html", ], "maxReviewsPerHotel": 50, } # Run the Actor and wait for it to finish run = client.actor("scrapersdelight/booking-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 '{ "startUrls": [ "https://www.booking.com/hotel/gb/royal-national.html", "https://www.booking.com/hotel/us/pod.html" ], "maxReviewsPerHotel": 50 }' | apify call scrapersdelight/booking-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapersdelight/booking-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Booking.com Reviews Scraper β€” Most Comprehensive", "description": "From $0.20 per 1,000 reviews β€” 10Γ— cheaper than typical $2/1k actors. Scrape Booking.com hotel reviews: title, positive/negative text, 0–10 score, stay date, room, nights, traveler type, country, with date/language/keyword filters + a new-review monitor with alerts. No login or API key.", "version": "0.1", "x-build-id": "JwlvuDH29brdG32u0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapersdelight~booking-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapersdelight-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/scrapersdelight~booking-reviews-scraper/runs": { "post": { "operationId": "runs-sync-scrapersdelight-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/scrapersdelight~booking-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-scrapersdelight-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", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "Hotel URLs", "type": "array", "description": "Booking.com hotel page URLs β€” any of these forms works: https://www.booking.com/hotel/gb/royal-national.html, https://www.booking.com/reviews/gb/hotel/royal-national.html, or the shorthand 'gb/royal-national'. One entry per hotel.", "items": { "type": "string" } }, "maxReviewsPerHotel": { "title": "Max reviews per hotel", "minimum": 0, "type": "integer", "description": "Hard cap on reviews collected per hotel (cost/safety guard). Defaults to 50 for a fast first run β€” set 0 for ALL reviews of each hotel.", "default": 50 }, "sortBy": { "title": "Sort by", "enum": [ "newest", "oldest", "featured", "score_high", "score_low" ], "type": "string", "description": "Server-side review ordering. 'Newest first' is best for monitors and date-range scrapes (enables early-stop).", "default": "newest" }, "language": { "title": "Review language", "type": "string", "description": "Server-side language filter (Booking's own r_lang). 'all' = every language (most reviews). Use a 2-letter code like en, fr, de, es, it, nl, zh, ja… 'default' lets Booking pick.", "default": "all" }, "dateFrom": { "title": "Reviews from (YYYY-MM-DD)", "type": "string", "description": "Only reviews posted ON/AFTER this date. With 'Newest first' the actor stops paginating as soon as it reaches older reviews (early-stop = fewer requests)." }, "dateTo": { "title": "Reviews until (YYYY-MM-DD)", "type": "string", "description": "Only reviews posted ON/BEFORE this date." }, "keyword": { "title": "Keyword filter", "type": "string", "description": "Only reviews whose title/positive/negative text or room type contains this word (case-insensitive). E.g. 'breakfast', 'noise', 'bed bugs'." }, "tripType": { "title": "Trip type", "enum": [ "all", "leisure", "business" ], "type": "string", "description": "Only leisure or business trips.", "default": "all" }, "travelerType": { "title": "Traveler type", "enum": [ "all", "couple", "solo", "family", "group" ], "type": "string", "description": "Only reviews by this traveler type.", "default": "all" }, "minScore": { "title": "Min score (0–10)", "minimum": 0, "maximum": 10, "type": "integer", "description": "Only reviews scored at/above this. Combine with maxScore to isolate e.g. complaints (0–5)." }, "maxScore": { "title": "Max score (0–10)", "minimum": 0, "maximum": 10, "type": "integer", "description": "Only reviews scored at/below this." }, "stripPersonalData": { "title": "Strip personal data (GDPR-friendly)", "type": "boolean", "description": "ON (default): drop the reviewer's first name from the output (nationality, age group, and review counts are kept β€” they don't identify a person). Turn OFF only if you have a lawful basis to process reviewer names.", "default": true }, "maxRetriesPerPage": { "title": "Max retries per page", "minimum": 1, "maximum": 30, "type": "integer", "description": "Booking.com's AWS WAF challenges requests at random (~2 of 3). Each retry uses a fresh cookie jar + proxy session; a clean page usually arrives within 1–4 tries. Raise this if you see 'no clean reviews page' warnings.", "default": 10 }, "monitorMode": { "title": "Monitor mode (new-review watcher)", "type": "boolean", "description": "Recurring watcher: diff against the prior run's seen reviews (per hotel) and output/alert ONLY new reviews. Pair with an Apify Schedule and Sort by = Newest first.", "default": false }, "alertOnNewReview": { "title": "Alert on new reviews", "type": "boolean", "description": "In monitor mode, deliver an alert for each new review via the channels below.", "default": true }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "POST endpoint for new-review alert payloads (Make / Zapier / n8n / custom). One JSON body per alert." }, "slackWebhookUrl": { "title": "Slack webhook URL", "type": "string", "description": "Slack incoming-webhook URL for formatted new-review cards." }, "emailRecipients": { "title": "Email recipients", "type": "array", "description": "Email addresses for the new-review digest (sent via apify/send-mail).", "items": { "type": "string" } }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Proxy settings. Booking.com's WAF challenges requests at random β€” the retry loop beats it with datacenter proxies; switch to RESIDENTIAL for large bulk runs if you see many retries.", "default": { "useApifyProxy": true } }, "diagnose": { "title": "Diagnostic mode (dev)", "type": "boolean", "description": "Dev only. Dumps the raw reviews HTML to the key-value store (DEBUG_REVIEWS_HTML), logs the parsed first review + a pagination check, then exits. Leave off for normal runs.", "default": false } } }, "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 } } } } } } } } } } ```