Booking.com Hotel Review Analysis and Competitor Sentiment Intelligence

Extract structured review data from any Booking.com property in minutes. Get review scores, positive and negative sentiment, reviewer demographics, room types, stay dates, category breakdowns, and management responses for hotels, apartments, hostels, and villas.

Built for hospitality operators, STR managers, and travel analysts who need guest sentiment data without reading thousands of reviews by hand.

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Who uses this and why

flowchart TD
    A[Hotel Operators] -->|Track guest complaints<br/>before they tank your score| D[Review<br/>Intelligence]
    B[STR Investors] -->|Benchmark sentiment<br/>against competing properties| D
    C[Revenue Managers] -->|Segment reviews by<br/>traveler type and season| D
    D --> E[Spot trends]
    D --> F[Fix issues early]
    D --> G[Price with confidence]

Role	What you get
Hotel operator	Recurring complaint patterns (noise, cleanliness, value) ranked by frequency
STR investor	Side by side sentiment comparison of properties in a target market
Revenue manager	Review trends segmented by families, couples, solo, business travelers
Travel analyst	Structured review datasets ready for BI dashboards or sentiment models

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How it works

flowchart LR
    A[Booking.com<br/>hotel URL] --> B[Load property page]
    B --> C[Intercept GraphQL<br/>review query]
    C --> D[Paginate through<br/>all reviews]
    D --> E[Normalize and<br/>structure data]
    E --> F[(Download as<br/>JSON, CSV, or Excel)]

The actor loads the Booking.com hotel page and intercepts the internal GraphQL query that powers the review section. It then paginates through reviews using the same API the Booking.com frontend uses. This returns richer data than page scraping: booking details, room types, check in and check out dates, management responses, helpful votes, and category score breakdowns.

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What one review record looks like

{
  "reviewScore": 7,
  "title": "Excellent location, comfortable room and cozy atmosphere.",
  "positiveText": "Ace hotel is located in a really good location, walking distance to multiple subway stops...",
  "negativeText": "Room had strange smell at time, probably due to being stuffy.",
  "reviewerName": "Tran",
  "reviewerCountry": "Vietnam",
  "travelerType": "Group",
  "roomType": "Single",
  "checkinDate": "2026-04-03",
  "checkoutDate": "2026-04-06",
  "numNights": 3,
  "reviewedDate": "2026-04-12",
  "helpfulVotes": 0,
  "hasPartnerReply": false,
  "categoryScores": [
    { "category": "Staff", "score": 8.6 },
    { "category": "Facilities", "score": 7.6 },
    { "category": "Cleanliness", "score": 7.9 },
    { "category": "Location", "score": 9.2 }
  ]
}

Every review includes: score (1 to 10), title, positive text, negative text, language, reviewer name, country, traveler type, room type, check in date, check out date, nights stayed, review date, helpful votes, whether management replied, reply text, photo count, and category scores (staff, facilities, cleanliness, comfort, value, location, wifi).

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Quick start

Extract the latest 200 reviews from a hotel:

curl -X POST "https://api.apify.com/v2/acts/scrapemint~booking-review-intelligence/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "hotelUrl": "https://www.booking.com/hotel/us/ace-new-york.html",
    "maxReviews": 200
  }'

Compare reviews across competing properties:

{
  "hotelUrls": [
    { "url": "https://www.booking.com/hotel/us/ace-new-york.html" },
    { "url": "https://www.booking.com/hotel/us/pod-39.html" }
  ],
  "maxReviews": 500,
  "travelerType": "BUSINESS_TRAVELLERS",
  "sortBy": "NEWEST_FIRST"
}

---

Inputs

Field	Type	Default	What it does
hotelUrls	array	[]	Booking.com hotel page URLs. Add multiple to compare properties.
hotelUrl	string	null	Single URL shortcut. Used when hotelUrls is empty.
maxReviews	integer	500	Cap per hotel to control cost.
sortBy	string	NEWEST_FIRST	Options: NEWEST_FIRST, OLDEST_FIRST, MOST_RELEVANT, SCORE_DESC, SCORE_ASC
travelerType	string	ALL	Filter: ALL, FAMILIES, COUPLES, SOLO_TRAVELLERS, GROUP_OF_FRIENDS, BUSINESS_TRAVELLERS

---

Pricing

Pay per review extracted. Every run includes a free tier so you can verify data quality first.

Tier	Price	Best for
Free	First 50 reviews per run	Testing the output format and data quality
Standard	$0.008 per review	Competitor benchmarking and trend tracking

flowchart LR
    A[Run the actor] --> B{First 50 reviews}
    B -->|Free| C[Verify data quality]
    C --> D{Need more?}
    D -->|Yes| E[$0.008 per review<br/>after the first 50]
    D -->|No| F[Done, $0 spent]

---

Cost comparison: Booking.com hotel review analysis

Method	Cost for 1,000 reviews	Data depth	Time
Read them on Booking.com manually	3 to 5 hours of analyst time	Notes in a spreadsheet	Hours
Review aggregator subscription	$50+ per property per month	Scores only, no text	Delayed
This actor	$7.60 once	Full text, metadata, scores, replies	Minutes

---

Data flow: from raw reviews to actionable intelligence

flowchart TD
    subgraph Input
        A[One or more<br/>Booking.com URLs]
    end
    subgraph Processing
        B[Load hotel pages]
        C[Intercept review API]
        D[Paginate all reviews]
        E[Normalize fields]
    end
    subgraph Output
        F[JSON / CSV / Excel]
    end
    subgraph Your workflow
        G[Spreadsheet analysis]
        H[BI dashboard]
        I[Sentiment ML model]
        J[Competitor report]
    end
    A --> B --> C --> D --> E --> F
    F --> G
    F --> H
    F --> I
    F --> J

---

Related tools in the hospitality intelligence suite

flowchart LR
    A[Airbnb Market<br/>Intelligence] --> B[Booking Review<br/>Intelligence]
    B --> C[Hotel Pricing<br/>Intelligence]
    C --> D[(Full competitor report<br/>$0.05 per property)]

• Airbnb Market Intelligence: live pricing, beds, ratings, and GPS coordinates for every Airbnb listing in a market
• Hotel Pricing Intelligence: nightly rate tracking across Booking.com properties (coming soon)

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Frequently asked questions

How do I analyze Booking.com reviews without reading them one by one? Run this actor with a hotel URL and a review cap. It returns every review as a structured record with score, text, reviewer info, and category breakdowns. Export as JSON, CSV, or Excel and sort, filter, or visualize in any tool you already use.

What data do I get per review? Score (1 to 10), title, positive text, negative text, language, reviewer name and country, traveler type, room type, check in and check out dates, number of nights, helpful vote count, whether management replied, the reply text, photo count, and category scores for staff, facilities, cleanliness, comfort, value, location, and wifi.

Can I filter Booking.com reviews by traveler type? Yes. Set the travelerType input to get only reviews from families, couples, solo travelers, groups, or business travelers. Useful for understanding how different guest segments experience the same property.

How many reviews can I pull from a single hotel? Up to the full review history. Set maxReviews to control the cap. Properties with 5,000+ reviews take a few minutes to finish.

Does it work for apartments and hostels, not just hotels? Any property with a public review page on Booking.com. Hotels, apartments, hostels, villas, guesthouses.

How fresh is the data? Live at query time. Every run pulls directly from Booking.com. No cached data, no stale monthly exports.

Can I compare reviews across multiple hotels? Yes. Pass multiple URLs in the hotelUrls array. Each review record includes a sourceUrl field so you can group results by property in your analysis.

What format is the output? JSON, CSV, or Excel. Download from the Apify dataset page or pull via API.