How to Scrape Tripadvisor Data

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Tripadvisor is one of the largest travel sites in the world, with over 1 billion reviews and opinions on hotels, restaurants, experiences and more. As a trusted source for travel information, Tripadvisor contains a wealth of data that can be valuable for various business use cases, from competitor analysis to location planning and more.

In this comprehensive guide, we‘ll walk through how to scrape Tripadvisor pages and extract key data using Python and BeautifulSoup.

Overview of Tripadvisor Data

Some of the main types of data you can scrape from Tripadvisor include:

  • Business listings – names, ratings, price ranges, location info, tags, contact details etc.
  • Reviews – text, ratings, date, reviewer info.
  • Photos – images uploaded by users.
  • Forum posts/discussions.

This data can be used for purposes like:

  • Competitor analysis – analyzing competitors‘ ratings, reviews, strengths/weaknesses.
  • Location analysis – researching potential new business locations.
  • Sentiment analysis – analyzing review text sentiment.
  • Pricing analysis – tracking prices over time.
  • Ad targeting – identifying customer interests and trends.

Now let‘s look at how to extract this data by scraping Tripadvisor pages.

Prerequisites

To follow this guide and scrape Tripadvisor pages, you‘ll need:

  • Python 3.x installed
  • Basic knowledge of Python and HTML
  • The following Python libraries:
    • requests
    • BeautifulSoup
    • pandas

You can install these with pip:

pip install requests beautifulsoup4 pandas

Optionally, you may also want an API like the Tripadvisor Scraper API to handle proxies, IP rotation, and other scraping infrastructure.

Scrape a Tripadvisor Business Page

Let‘s start by scraping a single business page on Tripadvisor.

Here‘s an example page we‘ll scrape:

https://www.tripadvisor.com/Restaurant_Review-g60763-d1218066-Reviews-Katz_s_Delicatessen-New_York_City_New_York.html

This contains info like the business name, rating, address, reviews and more that we want to extract.

Send Request

First we‘ll send a request to fetch the page HTML:

import requests

url = ‘https://www.tripadvisor.com/Restaurant_Review-g60763-d1218066-Reviews-Katz_s_Delicatessen-New_York_City_New_York.html‘

response = requests.get(url)
html = response.text

This uses the requests module to send a GET request to the URL and store the HTML of the page in a variable.

Parse with BeautifulSoup

Next we can parse the HTML using BeautifulSoup:

from bs4 import BeautifulSoup

soup = BeautifulSoup(html, ‘html.parser‘)

This loads the HTML into a BeautifulSoup object which we can now query to extract data.

Extract Business Name

Let‘s extract the business name first. Inspecting the page HTML, we can see the name is contained in an h1 with class ui_header:

<h1 class="ui_header h1">Katz‘s Delicatessen</h1>

We can use a BeautifulSoup selector to extract it:

name = soup.select_one(‘h1.ui_header.h1‘).text

print(name)
# Katz‘s Delicatessen

.select_one() allows us to pass a CSS selector to extract the first matching element.

Extract Rating

Next, let‘s extract the overall rating. The rating elements have class ui_bubble_rating, so we can select it:

rating = soup.select_one(‘.ui_bubble_rating‘)[‘alt‘]

print(rating)
# 4.5 of 5 bubbles

This grabs the alt attribute which contains the rating text.

Extract Number of Reviews

To get the number of reviews, we‘ll look for an element with class reviews_header_count: 

num_reviews = soup.select_one(‘.reviews_header_count‘).text 

print(num_reviews)
# 6,963 reviews

Extract Address

For the address, we can select the element with class street-address:

address = soup.select_one(‘.street-address‘).text

print(address)
# 205 E Houston St

Extract Phone Number

The phone number is contained within an a tag inside an element with class ui_icon phone:

phone = soup.select_one(‘.ui_icon.phone a‘).text

print(phone) 
# +1 212-254-2246

Extract All Review Data

Finally, let‘s extract all the key review data – text, date, ratings and username.

First we‘ll locate where all reviews are contained – in div elements with class reviewSelector.

Then we can loop through these and extract the details:

reviews = []

review_elems = soup.select(‘.reviewSelector‘)
for r in review_elems:

  text = r.select_one(‘.partial_entry‘).text.strip()
  date = r.select_one(‘.ratingDate‘)[‘title‘] 
  rating = r.select_one(‘.ui_bubble_rating‘)[‘alt‘]
  user = r.select_one(‘.info_text‘).text.strip()

  reviews.append({
    ‘text‘: text,
    ‘date‘: date, 
    ‘rating‘: rating,
    ‘user‘: user
  })

print(reviews[0])

This finds all reviews, then loops through to extract the text, date, rating and username for each one.

We store each extracted review in a dictionary, then append to a reviews list.

This gives us a list containing all review data we can work with!

Export to CSV

To export the scraped data to a CSV file, we can use pandas:

import pandas as pd

df = pd.DataFrame(reviews) 
df.to_csv(‘tripadvisor_reviews.csv‘, index=False)

This converts our reviews list into a DataFrame, then writes to a CSV file.

And that covers the key steps to scrape data from a Tripadvisor business page! You can expand on this to extract additional info like amenities, website URLs etc.

Next let‘s look at scraping search results pages.

Scrape Tripadvisor Search Results

Tripadvisor search results pages contain multiple business listings that we can scrape.

For example:

https://www.tripadvisor.com/Restaurants-g60763-New_York_City_New_York.html

This page has 100 restaurant listings in New York that we can extract data from.

The process is similar to scraping a single business page, but we need to iterate through each listing.

Here‘s an example:

import requests
from bs4 import BeautifulSoup

url = ‘https://www.tripadvisor.com/Restaurants-g60763-New_York_City_New_York.html‘

response = requests.get(url)
soup = BeautifulSoup(response.text, ‘html.parser‘)

results = []

for result in soup.select(‘.listing‘):

  name = result.select_one(‘.listing_title‘).text
  rating = result.select_one(‘.ui_bubble_rating‘)[‘alt‘]
  num_reviews = result.select_one(‘.review_count‘).text

  address = result.select_one(‘.street-address‘).text

  url = result.select_one(‘.listing_title a‘)[‘href‘]

  results.append({
    ‘name‘: name,
    ‘rating‘: rating,
    ‘num_reviews‘: num_reviews,  
    ‘address‘: address,
    ‘url‘: url
  })

print(results[0])

This loops through each listing (with class listing), extracts the key details like name, rating, reviews etc, then appends it to a results list.

To extract additional data like price ranges, cuisine types, photos etc, you would follow a similar process of locating the elements and extracting the text or attributes.

Scraping additional pages is also just a matter of updating the URL parameter.

Handling Pagination

To scrape multiple search results pages, we need to handle Tripadvisor‘s pagination.

Tripadvisor uses AJAX pagination, so we‘ll need to analyze network requests to find the APIs returning each page‘s data.

Using your browser‘s Network tools, you can monitor requests as you click through pages.

You‘ll notice calls like this fetching each page in JSON format:

https://www.tripadvisor.com/data/graphql/batched

With parameters like:

{
  "filters": { "page": 2 }, 
  "variables": {
    "searchSessionId": "abc123",
    "paging": { "sortOrder": "popularity" } 
  }
}

To handle pagination in our scraper, we need to:

  1. Extract the searchSessionId from the webpage or initial request.

  2. Increment the page parameter with each request.

  3. Parse the JSON response to extract the listings data.

Here is an example:

import requests
import json

url = ‘https://www.tripadvisor.com/data/graphql/batched‘

# Extract searchSessionId from webpage
search_id = # ...

page = 1 

while True:

  variables = {
    ‘searchSessionId‘: search_id,
    ‘paging‘: {‘sortOrder‘: ‘popularity‘} 
  }

  params = {
    ‘filters‘: {‘page‘: page},
    ‘variables‘: json.dumps(variables)
  }

  response = requests.get(url, params=params)
  data = response.json()

  # Extract listings from data

  page += 1

  if checkEndOfResults(data):
    break

This paginates through all pages by incrementing the page counter and extracting each page‘s data.

For a full working example of scraping Tripadvisor search pages with pagination, refer to this script.

Scraping Tripadvisor at Scale

While the examples above are focused on scraping individual pages, to harvest Tripadvisor data at scale you will need:

  • Proxies – to prevent IP blocking and rotate different IP addresses.
  • Concurrency – scraping with multiple threads/processes.
  • Infrastructure – platform to deploy and run your scraper on.

Handling this requires significant development work.

A managed solution like the Tripadvisor Scraper API can take care of these complexities and provide an API for scraping Tripadvisor at scale.

The API provides built-in proxy rotation, infrastructure, and can crawl through millions of pages per day.

Some examples of how the API can be used:

Scrape hotel listings by country

import tripadvisor_scraper

api = tripadvisor_scraper.TripadvisorScraperAPI(token={‘your token‘})

listings = api.scrape_location(‘hotels‘, ‘United States‘)

Extract hotel details by URL

data = api.scrape_url(‘https://www.tripadvisor.com/Hotel_Review-g60763-d92532-Reviews-The_Ritz_Carlton_New_York_Central_Park-New_York_City_New_York.html‘)

print(data[‘name‘], data[‘rating‘])

Search and paginate through restaurants 

results = api.search(‘restaurants in New York‘)

while results.has_next:
  print(len(results.results))
  results = api.get_next_page(results.cursor)

The API handles proxy rotation, pagination, scraping infrastructure and more, allowing you to focus on consuming the extracted data.

Scraping Tripadvisor Ethically

When scraping any website, it‘s important to do so ethically and legally. Here are some guidelines for ethical Tripadvisor scraping:

  • Don‘t overload servers – limit request rate/concurrency and implement politeness delays. Tripadvisor specifically prohibits "rapid scraping".

  • Obey robots.txt – don‘t scrape pages blocked in robots.txt.

  • Cache data – store scraped data rather than re-scraping constantly.

  • Rotate proxies – use different IPs to distribute load and avoid detection.

  • Don‘t republish data – don‘t directly make Tripadvisor data public or sell it.

  • Attribute data – if using Tripadvisor data in public projects, attribute it to Tripadvisor.

  • Consider legal implications – scraping laws vary by country/state so seek legal advice if needed.

Adhering to good scraping practices helps avoid issues down the track. When in doubt, consult Tripadvisor‘s terms of use and seek legal advice for your use case.

Tripadvisor Scraping FAQs

Is it legal to scrape Tripadvisor?

Web scraping is generally legal in most jurisdictions if done ethically and non-commercially. However laws vary internationally so seek legal advice if scraping at scale.

How do I get around captcha when scraping Tripadvisor?

CAPTCHAs can be difficult for scrapers to solve. Using proxies and implementing delays between requests can help avoid detection and CAPTCHAs. A scraping API handles CAPTCHAs automatically.

Why am I getting blocked when scraping Tripadvisor?

Blocks commonly occur when scraping without proxies at a high request rate. Use a proxy service to distribute requests across different IPs. Implement throttling, delays, and randomness to make your scraper act more human.

How can I get JSON data from Tripadvisor?

Some Tripadvisor data requires scraping interactive AJAX requests rather than the main HTML pages. Monitor network traffic in your browser to identify these API endpoints.

Can I extract user profiles and statistics from Tripadvisor?

Tripadvisor has increased security around user profiles to prevent scraping. Focus on public data like reviews and listings as opposed to private user information.

Conclusion

Tripadvisor is a valuable source of travel data for businesses – but extracting it requires carefully scraping Tripadvisor‘s front-end UI as well as APIs.

In this guide we covered key techniques for scraping Tripadvisor pages using Python and BeautifulSoup, including:

  • Extracting business listing data like names, ratings and contact info.
  • Scraping review details such as text, ratings and dates.
  • Handling pagination to scrape multiple search results pages.
  • Avoiding bans through proxies and good scraping practices.

The code examples provide a template to build on for your own Tripadvisor scraping projects. Expand on these snippets to extract additional fields relevant to your use case.

For large scale scraping, handling proxies, infrastructure and captchas can be challenging. An API like Oxylabs‘ Tripadvisor Scraper provides an easy way to harvest Tripadvisor data through a simple API.

Scraping unlocks the potential of Tripadvisor‘s data, but must be done ethically and legally. Use good practices around politeness, attribution and caching to ensure you stay on the right side of Tripadvisor‘s terms of use.

Let us know in the comments if you have any other questions on extracting value from Tripadvisor data through web scraping!

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