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TripAdvisor offers online hotel reservations and bookings for transportation, lodging, travel experiences, and restaurants. Users can find better service by comparing the hotels or restaurants on the platform to enjoy their trip better.
In this tutorial, we are going to show you how to scrape hotel data from TripAdvisor.
For TripAdvisor scraping, you can use our ready-to-use Task Template available on the home page or follow this tutorial to build the task from scratch.
To demonstrate, we will use this URL as an example: https://www.tripadvisor.com/Hotels-g186338-London_England-Hotels.html
The main steps are shown in the menu on the right and you can download the demo task file here.
1. Create a Go to Web Page - to open the target Webpage
Paste the URL and click Start
2. Click See all - to load all hotels
You need to click the See all button first to show all the hotels.
Select the See all button
Choose the Click Button on the Tips
Set up AJAX timeout as 5s
Go to the settings of the Click Item
Modify the Xpath of the Click Item to //span[.='See all']/..
Go to Options
Set the Wait before action to 4s
3. Set up Pagination Loop - to scrape data from multiple listing pages
Scroll down to find the next page button (->) and click on it
Select Loop click
Adjust Set AJAX timeout to 10s
Update the Pagination loop XPath to //a[@aria-label='Next page']
4. Create a Loop Item- to click on each hotel to get data
Click on 2 random hotel titles
Select Loop and click on each element
Choose No in the coming message on the Tips panel
Click on any text that needs to be extracted
Select Text
Repeat until all the data needed is in place
Go to Data Preview and double-click on the header to rename the field
Change the Loop mode to Variable List and then modify the XPath of the Loop Item to //div[@data-automation="hotel-card-title"]/a
Below is what the final workflow looks like. If everything is in place, you can continue to run the task.
5. Run the task - to get your target data
Run the task in the top right corner
Run on your device to run the task on your local device, or select Run in the cloud to run the task on the Cloud (for premium users only)
Here is the sample output:
