The Smart Way to Scrape TripAdvisor Data with Python

There are millions of reviews available on TripAdvisor. Imagine what you could do with access to that data—unfiltered customer opinions, pricing trends, and competitive intel all in one place. If you want to elevate your travel analysis or competitor research, scraping TripAdvisor is a game changer.
This step-by-step guide shows you exactly how to extract TripAdvisor data using Python and save it neatly in a CSV file for further analysis. Ready to get your hands dirty? Let’s go.

Build Your Scraper Toolkit

We’re using two Python staples for this:
requests — to fetch web pages
lxml — to parse HTML and extract data with XPath
Install them fast with:

pip install requests lxml

The Power of Headers and Proxies

TripAdvisor guards its data fiercely. Your requests must mimic real users, or you’ll get blocked. That’s where request headers come in—particularly the User-Agent string. It convinces the site your scraper is a genuine browser.
Proxies add another layer. Instead of all requests coming from one IP (a red flag), proxies let you rotate IP addresses. That means more data, fewer blocks, and longer scraping sessions. Invest in quality proxies—they’re worth it.

Step 1: Set Up Your Environment and Targets

Start by importing your libraries and listing the TripAdvisor URLs you want to scrape:

import requests
from lxml.html import fromstring
import csv

urls_list = [
    'https://www.tripadvisor.com/Hotel_Review-1',
    'https://www.tripadvisor.com/Hotel_Review-2'
]

Step 2: Craft Browser-Like Headers

Set headers to match a real browser request. This lowers your risk of detection:

headers = {
    'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
    'accept-language': 'en-IN,en;q=0.9',
    'cache-control': 'no-cache',
    'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36',
}

Step 3: Set Up Your Proxies

Here’s a simple proxy setup example:

proxies = {
    'http': 'http://your_proxy_address:port',
    'https': 'http://your_proxy_address:port',
}

Use them in your requests to stay under the radar:

response = requests.get(url, headers=headers, proxies=proxies)

Step 4: Scrape and Parse Like a Pro

Loop through URLs, fetch HTML, and parse it with XPath to pull the details you need:

extracted_data = []

for url in urls_list:
    response = requests.get(url, headers=headers, proxies=proxies)
    parser = fromstring(response.text)
    
    title = parser.xpath('//h1[@data-automation="mainH1"]/text()')[0]
    about = parser.xpath('//div[@class="_T FKffI bmUTE"]/div/div/text()')[0].strip()
    images_url = parser.xpath('//div[@data-testid="media_window_test"]/div/div/button/picture/source/@srcset')
    price = parser.xpath('//div[@data-automation="commerce_module_visible_price"]/text()')[0]
    ratings = parser.xpath('//div[@class="jVDab W f u w GOdjs"]/@aria-label')[0].split(' ')[0]
    features = parser.xpath('//div[@class="f Q2 _Y tyUdl"]/div[2]/span/span/span/text()')
    reviews = parser.xpath('//span[@class="JguWG"]/span//text()')
    listing_by = parser.xpath('//div[@class="biGQs _P pZUbB KxBGd"]/text()')[0]
    similar_experiences = parser.xpath('//div[@data-automation="shelfCard"]/a/@href')
    
    data = {
        'title': title,
        'about': about,
        'price': price,
        'listing_by': listing_by,
        'ratings': ratings,
        'image_urls': images_url,
        'features': features,
        'reviews': reviews,
        'similar_experiences': similar_experiences
    }
    extracted_data.append(data)

Step 5: Save Your Data Into CSV

Turn those raw insights into a CSV file for easy analysis:

csv_columns = ['title', 'about', 'price', 'listing_by', 'ratings', 'image_urls', 'features', 'reviews', 'similar_experiences']

with open("tripadvisor_data.csv", 'w', newline='', encoding='utf-8') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
    writer.writeheader()
    for data in extracted_data:
        writer.writerow(data)

print('Saved to tripadvisor_data.csv')

Harness Reviews for Smarter Strategy

Collecting data is just the start. What counts is the insight you extract:
Understand customer sentiment by analyzing reviews.
Track competitor pricing and offers.
Spot emerging travel trends early.
Identify new opportunities through related experience links.
This data empowers you to make informed, strategic decisions that move the needle in tourism marketing and beyond.

Conclusion

By scraping TripAdvisor data, you gain more than just information—you unlock insights that drive smarter decisions. From understanding customer sentiment to monitoring competitors and spotting trends, this data gives you a competitive edge in travel and hospitality. Use these tools to turn raw reviews into powerful, actionable intelligence.