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Booking Reviews Scraper
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Pay $2.00 for 1,000 reviews

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Booking Reviews Scraper

Booking Reviews Scraper

voyager/booking-reviews-scraper
Try for free

Pay $2.00 for 1,000 reviews

Scraper to get reviews from hotels, apartments and other accommodations listed on the Booking.com portal. Extract data using hotel URLs for review text, ratings, stars, basic reviewer info, length of stay, liked/disliked parts, room info, date of stay and more. Download in JSON, HTML, Excel, CSV.

What is Booking Reviews Scraper?

It's a simple and powerful tool that allows you to extract reviews from listings of your choice on Booking.com. You can get reviews from any hotels, apartments and other accommodations listed on the Booking.com portal. To get that data, just paste a URL of a hotel and click "Save & Start" button.

What Booking reviews data can I extract?

With this scraper, you will be able to extract the following data from booking websites:

📝 Review text ⭐️ Rating, review title and date
▶️ Liked and disliked parts about the stay 🗓 Reviewer’s date of stay
🛂 Reviewer’s username 🇯🇵 Reviewer’s indicated nationality
🌛 Reviewer’s length of stay 🛌 Reviewer’s room info

Why scrape reviews from Booking.com?

🔎 Conduct market research

🏖 Track brand sentiment and shifts in customer reactions

⭐️ Improve customer service

🤺 Monitor the quality of service of your competitors

🤥 Identify fake reviews

How do I use Booking Reviews Scraper?

Booking Reviews Scraper was designed to be easy to start with even if you've never extracted data from the web before. Here's how you can scrape booking reviews with this tool:

  1. Create a free Apify account using your email.
  2. Open Booking Reviews Scraper.
  3. Add one or more hotel URLs to get reviews from.
  4. Click "Start" and wait for the data to be extracted.
  5. Download your data in JSON, XML, CSV, Excel, or HTML.

Input

The input for Booking Reviews Scraper should be a hotel URL (or hotel detail page) that you want to extract reviews from. You can add more than one URL at a time. The URL will look something like this:

1{
2  "maxReviewsPerHotel": 1000,
3  "proxyConfiguration": {
4    "useApifyProxy": true
5  },
6  "startUrls": [
7    {
8      "url": "https://www.booking.com/hotel/us/chicago-t.html?aid=304142&label=gen173nr-1FCAEoggI46AdIM1gEaDqIAQGYATG4AQfIAQzYAQHoAQH4AQKIAgGoAgO4AuLFmqIGwAIB0gIkN2YzZmI0YzktMTY1ZS00OThkLTgzY2ItOTMxODA5OTI5NzNj2AIF4AIB&all_sr_blocks=5924324_246077187_2_0_0;checkin=2023-09-01;checkout=2023-09-15;dest_id=20033173;dest_type=city;dist=0;group_adults=2;group_children=0;hapos=6;highlighted_blocks=5924324_246077187_2_0_0;hpos=6;matching_block_id=5924324_246077187_2_0_0;no_rooms=1;req_adults=2;req_children=0;room1=A%2CA;sb_price_type=total;sr_order=popularity;sr_pri_blocks=5924324_246077187_2_0_0__341393;srepoch=1682350871;srpvid=88466e4a189c0182;type=total;ucfs=1&#hotelTmpl"
9    }
10  ]
11}
12...

Additionally, you can click on the "Advanced" button in the URL input field and provide any userData. Everything provided here will be available in the output as customData, to allow later easy identification of which review belongs to which hotel. Click on the input tab for a full explanation of input in JSON.

Output sample

The results will be wrapped into a dataset which you can find in the Storage tab. Here's an excerpt from the dataset you'd get if you apply the input parameters above:

scraping booking reviews

And here is the same data but in JSON. You can choose in which format to download your booking data: JSON/JSONL, Excel, HTML table, CSV, or XML.

1[{
2  "id": "65d22b83283cb5e4",
3  "hotelId": "us/chicago-t",
4  "reviewPage": 1,
5  "userName": "Simon",
6  "userLocation": "United Kingdom",
7  "roomInfo": "King Room with One King Bed - Non-Smoking",
8  "stayDate": "January 2022",
9  "stayLength": "2 nights",
10  "reviewDate": "January 12, 2022",
11  "reviewTitle": "Exceptional",
12  "rating": "10",
13  "reviewTextParts": {
14    "Liked": "Cheap and cheerful, the rooms are old school but warm and clean, staff friendly"
15  },
16  "customData": {}
17},
18{
19  "id": "3793d41df4ef9587",
20  "hotelId": "us/chicago-t",
21  "reviewPage": 1,
22  "userName": "Nilesh",
23  "userLocation": "United States of America",
24  "roomInfo": "King Room with One King Bed - Non-Smoking",
25  "stayDate": "April 2023",
26  "stayLength": "2 nights",
27  "reviewDate": "April 24, 2023",
28  "reviewTitle": "Great location hotel with amazing team in dated rooms.",
29  "rating": "7.0",
30  "reviewTextParts": {
31    "Liked": "Staff was incredibly helpful and kind.  They allowed me to check in early which helped me recoup from an early morning arrival.  Location was amazing! Fast wifi.  Nice lobby lounge.",
32    "Disliked": "The bathroom was tiny.  It was functional and the water pressure in the shower/tub was great, but it was too small to maneuver in."
33  },
34  "customData": {}
35},
36{
37  "id": "6ad30232d7be0a7b",
38  "hotelId": "us/chicago-t",
39  "reviewPage": 1,
40  "userName": "Tshepiso",
41  "userLocation": "South Africa",
42  "roomInfo": "Deluxe Double Room with Two Double Beds - Non-Smoking",
43  "stayDate": "March 2023",
44  "stayLength": "2 nights",
45  "reviewDate": "April 22, 2023",
46  "reviewTitle": "Fair",
47  "rating": "5.0",
48  "reviewTextParts": {
49    "Liked": "location",
50    "Disliked": "cleanliness"
51  },
52  "customData": {}
53},
54{
55  "id": "a96ed9c83e86814f",
56  "hotelId": "us/chicago-t",
57  "reviewPage": 1,
58  "userName": "Tetsuya",
59  "userLocation": "Japan",
60  "roomInfo": "Deluxe Double Room with Two Double Beds - Non-Smoking",
61  "stayDate": "April 2023",
62  "stayLength": "1 night",
63  "reviewDate": "April 21, 2023",
64  "reviewTitle": "Good ROI",
65  "rating": "7.0",
66  "reviewTextParts": {
67    "Liked": "large room, good location near subway station and restaurants with reasonable price",
68    "Disliked": "old facilities and building"
69  },
70  "customData": {}
71},
72{
73  "id": "f438b7c2a791b73a",
74  "hotelId": "us/chicago-t",
75  "reviewPage": 1,
76  "userName": "Joke",
77  "userLocation": "Spain",
78  "roomInfo": "Deluxe Double Room with Two Double Beds - Non-Smoking",
79  "stayDate": "April 2023",
80  "stayLength": "2 nights",
81  "reviewDate": "April 18, 2023",
82  "reviewTitle": "great location, but outdated rooms",
83  "rating": "5.0",
84  "reviewTextParts": {
85    "Liked": "Friendly staff and great location.",
86    "Disliked": "I didn't like the look of the hotel. Our room was outdated, stains on the (old) carpet, ugly wallpaper,...the heating made a lot of noise at night"
87  },
88  "customData": {}
89}]
90...

Do I need proxies to scrape Booking reviews?

If you run the scraper on the Apify platform, for successful booking reviews scraping you will need residential proxies which are included in Apify's monthly Starter plan ($49).

For more details about how our pricing works, platform credits, proxies, and usage, see the platform pricing page.

Want to scrape other travel industry data?

You can use the dedicated scrapers below if you want to scrape specific travel industry data. Each of them is built particularly for the relevant scraping case be it restaurant reviews, flight prices, or whole accommodations. Feel free to browse them:

🚩 Booking Scraper✈️ Ryanair Scraper
🌍 TripAdvisor Scraper🛫 Skyscanner Flight Scraper
🖇 Airbnb Scraper🏨 Expedia Hotels Scraper
🌟 TripAdvisor Reviews Scraper📍 Foursquare Reviews Scraper
🏎 Fast Booking Scraper🎖 Google Maps Reviews Scraper

Integrations and Booking Reviews Scraper

Last but not least, Booking Reviews Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform. You can integrate with LangChain, Make, Trello, Zapier, Slack, Airbyte, GitHub, Google Sheets, Google Drive, Asana, and more.

You can also use webhooks to carry out an action whenever an event occurs, e.g., get a notification whenever Booking Reviews Scraper successfully finishes a run.

Using Booking Reviews Scraper with the Apify API

The Apify API gives you programmatic access to the Apify platform. The API is organized around RESTful HTTP endpoints that enable you to manage, schedule, and run Apify Actors. The API also lets you access any datasets, monitor Actor performance, fetch results, create and update versions, and more. To access the API using Node.js, use the apify-client NPM package. To access the API using Python, use the apify-client PyPI package.

Check out the Apify API reference docs for full details or click on the API tab for code examples.

Our travel industry scrapers are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. However, you should be aware that your results could contain personal data. You should not scrape personal data unless you have a legitimate reason to do so.

If you're unsure whether your reason is legitimate, consult your lawyers. You can also read our blog post on the legality of web scraping and ethical scraping.

Your feedback

We’re always working on improving the performance of our Actors. So if you’ve got any technical feedback for Booking Reviews Scraper or simply found a bug, please create an issue on the Actor’s Issues tab in Apify Console.

Actor icon attribution: Condominium icons created by Dewi Sari - Flaticon

Developer
Maintained by Apify
Actor metrics
  • 77 monthly users
  • 99.5% runs succeeded
  • 0.0 days response time
  • Created in Apr 2023
  • Modified 1 day ago
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