How to Scrape TripAdvisor.com (2025 Updated)

TripAdvisor.com is one of the most popular service portals in the travel industry, containing data about trips, hotels and restaurants.

In this tutorial, we'll take a look at how to scrape TripAdvisor reviews as well as other details like hotel information. We'll also automate finding hotel pages by scraping search. The concepts we'll explain can be applied to other parts of the website, such as restaurants, tours and activities.

Why Scrape TripAdvisor?

TripAdvisor is one of the most popular data sources in the travel industry. Most people are interested in scraping TripAdvisor reviews but this public source also contains data like the hotel, tour and restaurant information and pricing. So, by scraping TripAdvisor, we can gather information about the hotel industry as well as public opinions about it.

This data offers great value in the business intelligence areas, such as the market and competitive analysis. In other words, data available on TripAdvisor can give us a inisghts into the travel industry, which can be used to generate leads and improve business performances.

For more on scraping use cases see our extensive write-up Scraping Use Cases

Project Setup

To scrape TripAdvisor, we'll use a few Python packages:

• httpx - HTTP client library which will let us communicate with TripAdvisor.com's servers
• parsel - HTML parsing library we'll use to parse our scraped HTML files using web selectors, such as XPath and CSS.

These packages can be easily installed via pip command:

$ pip install "httpx[http2,brotli]" parsel

Alternatively, you're free to swap httpx out with any other HTTP client package such as requests as we'll only need basic HTTP functions which are almost interchangeable in every library. As for, parsel, another great alternative is beautifulsoup package.

Hands on Python Web Scraping Tutorial and Example Project

TripAdvisor is a tough target to scrape - if you're new to web scraping with Python we recommend checking out our full introduction tutorial to web scraping with Python and common best practices.

Finding Tripadvisor Hotels

Let's start our TripAdvisor scraper by taking a look at how can we find hotels on the website. For this, let's take a look at how TripAdvisor's search function works:

In the short video above, we can see that a GraphQl-powered POST request is sent in the background when we type in our search query. This request returns search page recommendations. Each of these recommendations contains preview data of hotels, restaurants or tours.

Let's replicate this graphql request in our Python-based scraper. We'll establish an HTTP connection session and submit a POST type request that mimics what we've observed above:

import json
import random
import string
import asyncio

from typing import List, Dict
from loguru import logger as log
from scrapfly import ScrapeConfig, ScrapflyClient

SCRAPFLY = ScrapflyClient(key="Your Scrapfly API key")

BASE_CONFIG = {
    # Tripadvisor.com requires Anti Scraping Protection bypass feature:
    "asp": True,
    # set the proxy location to US
    "country": "US",
}


async def scrape_location_data(query: str) -> List[Dict]:
    """
    scrape search location data from a given query.
    e.g. "New York" will return us TripAdvisor's location details for this query
    """
    log.info(f"scraping location data: {query}")
    # the graphql payload that defines our search
    # note: that changing values outside of expected ranges can block the web scraper
    payload = json.dumps(
        [
            {
                "variables": {
                    "request": {
                        "query": query,
                        "limit": 10,
                        "scope": "WORLDWIDE",
                        "locale": "en-US",
                        "scopeGeoId": 1,
                        "searchCenter": None,
                        # note: here you can expand to search for differents.
                        "types": [
                            "LOCATION",
                            # "QUERY_SUGGESTION",
                            # "RESCUE_RESULT"
                        ],
                        "locationTypes": [
                            "GEO",
                            "AIRPORT",
                            "ACCOMMODATION",
                            "ATTRACTION",
                            "ATTRACTION_PRODUCT",
                            "EATERY",
                            "NEIGHBORHOOD",
                            "AIRLINE",
                            "SHOPPING",
                            "UNIVERSITY",
                            "GENERAL_HOSPITAL",
                            "PORT",
                            "FERRY",
                            "CORPORATION",
                            "VACATION_RENTAL",
                            "SHIP",
                            "CRUISE_LINE",
                            "CAR_RENTAL_OFFICE",
                        ],
                        "userId": None,
                        "context": {},
                        "enabledFeatures": ["articles"],
                        "includeRecent": True,
                    }
                },
                "query": "84b17ed122fbdbd4",
                "extensions": {"preRegisteredQueryId": "84b17ed122fbdbd4"},
            }
        ]
    )

    # we need to generate a random request ID for this request to succeed
    random_request_id = "".join(random.choice(string.ascii_lowercase + string.digits) for i in range(64))
    headers = {
        "Accept": "*/*",
        "Accept-Encoding": "gzip, deflate, br",
        "Connection": "keep-alive",
        "Host": "www.tripadvisor.com",
        "Origin": "https://www.tripadvisor.com",
        "X-Requested-With": "XMLHttpRequest",
        "content-type": "application/json",
        "x-requested-by": random_request_id
    }
    result = await SCRAPFLY.async_scrape(
        ScrapeConfig(
            url="https://www.tripadvisor.com/data/graphql/ids",
            headers=headers,
            body=payload,
            method="POST",
            **BASE_CONFIG,
        )
    )
    data = json.loads(result.content)
    results = data[0]["data"]["Typeahead_autocomplete"]["results"]
    # strip metadata
    results = [r["details"] for r in results if r['__typename'] == 'Typeahead_LocationItem']
    log.info(f"found {len(results)} results")
    return results

async def run():
    result_location = await scrape_location_data(query="Malta")
    with open("location.json", "w", encoding="utf-8") as file:
        json.dump(result_location, file, indent=2, ensure_ascii=False)


if __name__ == "__main__":
    asyncio.run(run())

This graphQL request might appear complicated, but we mostly use values copied from our browser. Let's note a few points used in the above code:

• The headers Referer and Origin are required to not be blocked by TripAdvisor
• The header X-Requested-By is a tracking ID header and in this case, we just generate a bunch of random numbers.

We're also using httpx with http2 enabled to make our requests faster and less likely to get blocked.

Web Scraping Graphql with Python

For more details on how to scrape graphql powered websites see our introduction tutorial which covers what is graphql, how to scrape it and common tools, tips and tricks.

Let's run our TripAdvisor scraper and see what it finds for "Malta" keyword:

  {
    "localizedName": "Malta",
    "localizedAdditionalNames": {
      "longOnlyHierarchy": "Europe"
    },
    "streetAddress": {
      "street1": null
    },
    "locationV2": {
      "placeType": "COUNTRY",
      "hierarchy": {
        "parentId": 4,
        "parent": {
          "names": {
            "longOnlyHierarchyTypeaheadV2": ""
          }
        },
        "naturalParentId": 4,
        "naturalParent": {
          "names": {
            "name": "Europe"
          },
          "placeType": "CONTINENT"
        }
      },
      "names": {
        "longOnlyHierarchyTypeaheadV2": "Europe"
      },
      "vacationRentalsRoute": {
        "url": "/VacationRentals-g190311-Reviews-Malta-Vacation_Rentals.html"
      }
    },
    "url": "/Tourism-g190311-Malta-Vacations.html",
    "HOTELS_URL": "/Hotels-g190311-Malta-Hotels.html",
    "ATTRACTIONS_URL": "/Attractions-g190311-Activities-Malta.html",
    "RESTAURANTS_URL": "/Restaurants-g190311-Malta.html",
    "placeType": "COUNTRY",
    "latitude": 35.892,
    "longitude": 14.42979,
    "isGeo": true,
    "thumbnail": {
      "photoSizeDynamic": {
        "maxWidth": 5218,
        "maxHeight": 3576,
        "urlTemplate": "https://dynamic-media-cdn.tripadvisor.com/media/photo-o/2a/e5/e9/e5/caption.jpg?w={width}&h={height}&s=1"
      }
    }
  }

We can see that we get URLs to Hotel, Restaurant and Attraction searches! We can use these URLs to scrape search results themselves.

Scraping Tripadvisor Search

We figured out how to use TripAdvisor's Search suggestions to find search pages, now let's scrape these pages for hotel preview data like links and names.

Let's take a look at how we can do that by extending our scraping code:

import json
import asyncio
import math

from loguru import logger as log
from urllib.parse import urljoin
from typing import List, Dict, Optional
from scrapfly import ScrapeConfig, ScrapflyClient, ScrapeApiResponse

SCRAPFLY = ScrapflyClient(key="Your Scrapfly API key")

BASE_CONFIG = {
    # Tripadvisor.com requires Anti Scraping Protection bypass feature:
    "asp": True,
    # set the proxy location to US
    "country": "US",
}


def parse_search_page(result: ScrapeApiResponse) -> List[Dict]:
    """parse result previews from TripAdvisor search page"""
    log.info(f"parsing search page: {result.context['url']}")
    parsed = []
    # Search results are contain in boxes which can be in two locations.
    # this is location #1:
    for box in result.selector.xpath("//div[@data-test-target='hotels-main-list']//ol/li"):
        title = box.xpath(".//div[@data-automation='hotel-card-title']/a/h3/text()").getall()[1]
        url = box.css("div[data-automation=hotel-card-title] a::attr(href)").get()
        parsed.append(
            {
                "url": urljoin(result.context["url"], url),  # turn url absolute
                "name": title,
            }
        )
    if parsed:
        return parsed
    # location #2
    for box in result.selector.css("div.listing_title>a"):
        parsed.append(
            {
                "url": urljoin(result.context["url"], box.xpath("@href").get()),  # turn url absolute
                "name": box.xpath("text()").get("").split(". ")[-1],
            }
        )
    return parsed


async def scrape_search(query: str, max_pages: Optional[int] = None) -> List[Dict]:
    """scrape search results of a search query"""
    # first scrape location data and the first page of results
    log.info(f"{query}: scraping first search results page")
    try:
        location_data = (await scrape_location_data(query))[0]  # take first result
    except IndexError:
        log.error(f"could not find location data for query {query}")
        return
    except NameError:
        log.error("scrape_location_data is not defined. You can define it from the earlier snippet")
        return
    hotel_search_url = "https://www.tripadvisor.com" + location_data["HOTELS_URL"]

    log.info(f"found hotel search url: {hotel_search_url}")
    first_page = await SCRAPFLY.async_scrape(ScrapeConfig(hotel_search_url, **BASE_CONFIG))

    # parse first page
    results = parse_search_page(first_page)
    if not results:
        log.error("query {} found no results", query)
        return []

    # extract pagination metadata to scrape all pages concurrently
    page_size = len(results)
    total_results = first_page.selector.xpath("//div[@data-test-target='hotels-main-list']//span").re(r"(\d[\d,]*)")[0]
    total_results = int(total_results.replace(",", ""))
    next_page_url = first_page.selector.css('a[aria-label="Next page"]::attr(href)').get()
    next_page_url = urljoin(hotel_search_url, next_page_url)  # turn url absolute
    total_pages = int(math.ceil(total_results / page_size))
    if max_pages and total_pages > max_pages:
        log.debug(f"{query}: only scraping {max_pages} max pages from {total_pages} total")
        total_pages = max_pages

    # scrape remaining pages
    log.info(f"{query}: found {total_results=}, {page_size=}. Scraping {total_pages} pagination pages")
    other_page_urls = [
        # note: "oa" stands for "offset anchors"
        next_page_url.replace(f"oa{page_size}", f"oa{page_size * i}")
        for i in range(1, total_pages)
    ]
    # we use assert to ensure that we don't accidentally produce duplicates which means something went wrong
    assert len(set(other_page_urls)) == len(other_page_urls)

    to_scrape = [ScrapeConfig(url, **BASE_CONFIG) for url in other_page_urls]
    async for result in SCRAPFLY.concurrent_scrape(to_scrape):
        results.extend(parse_search_page(result))
    return results

async def run():
    search_result = await scrape_search("Malta", max_pages=2)
    with open("search_results.json", "w", encoding="utf-8") as file:
        json.dump(search_result, file, indent=2, ensure_ascii=False)


if __name__ == "__main__":
    asyncio.run(run())

Here, we create our scrape_search() function that takes in a query and finds the correct search page. Then, we scrape the whole search page, which contains multiple paginated pages. Here are what the extracted results look like:

[
  {
    "id": "573828",
    "url": "/Hotel_Review-g230152-d573828-Reviews-Radisson_Blu_Resort_Spa_Malta_Golden_Sands-Mellieha_Island_of_Malta.html",
    "name": "Radisson Blu Resort & Spa, Malta Golden Sands"
  },
  ...
]

With preview results in hand, we can scrape information, pricing and review data of each TripAdvisor hotel listing - let's do that in the following section.

Scraping Tripadvisor Hotel Data

To scrape hotel information we'll have to collect each hotel page we found using the search.

But before we start scraping, let's take a look at the individual hotel page to see where is the data located in the hotel page itself.

For example, let's see this 1926 Hotel & Spa hotel. If we take a look at the page source of this page in our browser we can see a few amount of JavaScript cache data:

This data is the same on the page but before rendering into the HTML, often knnown as hidden web data.

How to Scrape Hidden Web Data

For more on hidden web data scraping see our full introduction article which explains what is hidden web data and the many methods of scraping it.

Let's scrape Triadvisor hotels data by extracting this hidden data alongside other data on the page HTML:

import json
import asyncio

from loguru import logger as log
from typing import Dict, List
from scrapfly import ScrapeConfig, ScrapflyClient, ScrapeApiResponse

SCRAPFLY = ScrapflyClient(key="Your Scrapfly API key")

BASE_CONFIG = {
    # Tripadvisor.com requires Anti Scraping Protection bypass feature:
    "asp": True,
    # set the proxy location to US
    "country": "US",
}


def parse_hotel_page(result: ScrapeApiResponse) -> Dict:
    """parse hotel data from hotel pages"""
    selector = result.selector
    basic_data = json.loads(selector.xpath("//script[contains(text(),'aggregateRating')]/text()").get())
    description = selector.xpath("//div[@data-automation='aboutTabDescription']/div/div/div/text()").get()
    amenities = []
    for feature in selector.xpath("//div[contains(@data-test-target, 'amenity')]/text()"):
        amenities.append(feature.get())

    return {
        "basic_data": basic_data,
        "description": description,
        "featues": amenities,
    }


async def scrape_hotel(urls: List[str]) -> Dict:
    """Scrape hotel data and reviews"""
    hotel_data = []
    to_scrape = [
        ScrapeConfig(url, **BASE_CONFIG)
        for url in urls
    ]    

    async for response in SCRAPFLY.concurrent_scrape(to_scrape):
        hotel_data.append(parse_hotel_page(response))
    
    log.success(f"successfully scraped {len(hotel_data)} hotel pages")
    return hotel_data

async def run():
    hotel_data = await scrape_hotel(
        urls=[
            "https://www.tripadvisor.com/Hotel_Review-g190327-d264936-Reviews-1926_Hotel_Spa-Sliema_Island_of_Malta.html"
        ]
    )

    with open("hotel_data.json", "w", encoding="utf-8") as file:
        json.dump(hotel_data, file, indent=2, ensure_ascii=False)


if __name__ == "__main__":
    asyncio.run(run())

In the above code, we define two main functions:

• parse_hotel_page: For parsing the hotel data from the HTML using XPath selectors.
• scrape_hotel: For scraping Tripadvisor hotel pages by sending requests the hotel page URL and then parsing the HTML.

Here is the result we got:

  {
    "basic_data": {
      "@context": "https://schema.org",
      "@type": "LodgingBusiness",
      "name": "1926 Le Soleil Hotel & Spa",
      "url": "https://www.tripadvisor.com/Hotel_Review-g190327-d264936-Reviews-1926_Le_Soleil_Hotel_Spa-Sliema_Island_of_Malta.html",
      "priceRange": "$$ (Based on Average Nightly Rates for a Standard Room from our Partners)",
      "aggregateRating": {
        "@type": "AggregateRating",
        "ratingValue": "4.3",
        "reviewCount": 1187
      },
      "address": {
        "@type": "PostalAddress",
        "streetAddress": "Thornton Street",
        "addressLocality": "Sliema",
        "postalCode": "SLM 3143",
        "addressCountry": {
          "@type": "Country",
          "name": "Malta"
        }
      },
      "image": "https://dynamic-media-cdn.tripadvisor.com/media/photo-o/15/db/ba/a1/hotel-1926.jpg?w=500&h=-1&s=1"
    },
    "description": "Inspired by the life and passions of one man and featuring a touch of the roaring twenties, 1926 Le Soleil Hotel & Spa offers luxurious rooms and suites in the central city of Sliema. The hotel is located 200 meters from the seafront and also offers a splendid 1926 La Plage Beach Club on the water’s edge as well as a luxury SPA. The beach club is located 200 meters away from the hotel and is a seasonal operation. Our concept of ‘Lean Luxury’ includes the following: • Luxury rooms at affordable prices • Uncomplicated comfort and a great sleep • Smart design technology • Raindance showerheads • Flat screens • SuitePad Tablets • Self check in and check out (if desired) • Coffee & tea making facilities",
    "featues": [
      "Free internet",
      "Airport transportation",
      "Meeting rooms",
      "Paid private parking nearby",
      "Street parking",
      "Wifi",
      "Fitness / spa locker rooms",
      "Sauna",
      "Pool / beach towels",
      "Infinity pool",
      "Pool with view",
      "Outdoor pool",
      "Heated pool",
      "Saltwater pool",
      "Shallow end in pool",
      "Coffee shop",
      "Restaurant",
      "Breakfast available",
      "Breakfast buffet",
      "Complimentary Instant Coffee",
      "Complimentary tea",
      "Complimentary welcome drink",
      "Outdoor dining area",
      "Vending machine",
      "Poolside bar",
      "Taxi service",
      "Steam room",
      "24-hour security",
      "Baggage storage",
      "Non-smoking hotel",
      "Sun deck",
      "Sun loungers / beach chairs",
      "Sun terrace",
      "Doorperson",
      "First aid kit",
      "Umbrella",
      "24-hour check-in",
      "24-hour front desk",
      "Express check-in / check-out",
      "Dry cleaning",
      "Laundry service",
      "Ironing service",
      "Shoeshine",
      "Bathrobes",
      "Air conditioning",
      "Desk",
      "Housekeeping",
      "Interconnected rooms available",
      "Refrigerator",
      "Cable / satellite TV",
      "Walk-in shower",
      "Telephone",
      "Wardrobe / closet",
      "Bottled water",
      "Private bathrooms",
      "Tile / marble floor",
      "Wake-up service / alarm clock",
      "Flatscreen TV",
      "Hair dryer",
      "Non-smoking rooms"
    ]
  }

Our TripAdvisor scraper got the essential hotel data. However, we are missing the hotel reviews data - let's scrape them next!

Scraping Tripadvisor Hotel Reviews

Reviews data are found on the same hotel page. We'll extend our parse_hotel_page function to capture this data. And since we have the total number of reviews, we'll use it get the total number of review pages and crawl over them. Let's apply this within our previous Tripadvisor scraper code:

import asyncio
import json
import math
from typing import List, Dict, Optional
from httpx import AsyncClient, Response
from parsel import Selector

client = AsyncClient(
    headers={
        # use same headers as a popular web browser (Chrome on Windows in this case)
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36",
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
        "Accept-Language": "en-US,en;q=0.9",
    },
    follow_redirects=True
)

def parse_hotel_page(result: Response) -> Dict:
    """parse hotel data from hotel pages"""
    selector = Selector(result.text)
    basic_data = json.loads(selector.xpath("//script[contains(text(),'aggregateRating')]/text()").get())
    description = selector.css("div.fIrGe._T::text").get()
    amenities = []
    for feature in selector.xpath("//div[contains(@data-test-target, 'amenity')]/text()"):
        amenities.append(feature.get())
    reviews = []
    for review in selector.xpath("//div[@data-reviewid]"):
        title = review.xpath(".//div[@data-test-target='review-title']/a/span/span/text()").get()
        text = "".join(review.xpath(".//span[contains(@data-automation, 'reviewText')]/span/text()").extract())
        rate = review.xpath(".//div[@data-test-target='review-rating']/span/@class").get()
        rate = (int(rate.split("ui_bubble_rating")[-1].split("_")[-1].replace("0", ""))) if rate else None
        trip_data = review.xpath(".//span[span[contains(text(),'Date of stay')]]/text()").get()
        reviews.append({
            "title": title,
            "text": text,
            "rate": rate,
            "tripDate": trip_data
        })

    return {
        "basic_data": basic_data,
        "description": description,
        "featues": amenities,
        "reviews": reviews
    }


async def scrape_hotel(url: str, max_review_pages: Optional[int] = None) -> Dict:
    """Scrape hotel data and reviews"""
    first_page = await client.get(url)
    assert first_page.status_code == 403, "request is blocked"
    hotel_data = parse_hotel_page(first_page)

    # get the number of total review pages
    _review_page_size = 10
    total_reviews = int(hotel_data["basic_data"]["aggregateRating"]["reviewCount"])
    total_review_pages = math.ceil(total_reviews / _review_page_size)

    # get the number of review pages to scrape
    if max_review_pages and max_review_pages < total_review_pages:
        total_review_pages = max_review_pages
    
    # scrape all review pages concurrently
    review_urls = [
        # note: "or" stands for "offset reviews"
        url.replace("-Reviews-", f"-Reviews-or{_review_page_size * i}-")
        for i in range(1, total_review_pages)
    ]
    for response in asyncio.as_completed(review_urls):
        data = parse_hotel_page(await response)    
        hotel_data["reviews"].extend(data["reviews"])
    print(f"scraped one hotel data with {len(hotel_data['reviews'])} reviews")
    return hotel_data

async def run():
    result_hotel = await scrape_hotel(
        "https://www.tripadvisor.com/Hotel_Review-g190327-d264936-Reviews-1926_Hotel_Spa-Sliema_Island_of_Malta.html",
        max_review_pages=3
    )

    with open("hotel_data.json", "w", encoding="utf-8") as file:
        json.dump(result_hotel, file, indent=2, ensure_ascii=False)


if __name__ == "__main__":
    asyncio.run(run())

Here, we add the reviews parsing logic to the parse_hotel_page funciton to get all the reviews on each page. Next, we update the scrape_hotel function by adding three additional steps. First, it gets the number of review pages available and the actual review page to scrape. Next, it adds the review page URLs to a scraping list. Finally, it scrapes the remaining review pages concurrently. Here is an illustration of how this pagination log works:

The data we got is the same as the hotel data we got earlier, but with additional review data:

[
    {
      "title": "Superb hotel❤️",
      "text": "Visited last October and had an amazing time! Le soleil has everything you need from the hotel.\nStaff were so nice, professional and welcoming, they are the pride of the hotel!❤️\nFood in the beach club - delicious and always fresh. \nRooms - spacious, serene. Everything, from pools and sun loungers to rooms was sparkling clean. Indoor pool with bubbles - therapeutic! \nThe rooftop pool had best loungers I've ever came across\nMalta is a place to fall in love with and Im already planning to return this autumn.",
      "rate": 5.0,
      "tripDate": "Oct 2024",
      "tripType": "Family"
    },
    ....
  ]
}

With this final feature, we have our full TripAdvisor scraper ready to scrape hotel information and reviews. We can easily apply the same scraping logic to scrape other TripAdvisor details like activities and restaurant data, as the underlying web technology is the same.

However, to successfully scrape TripAdvisor at scale we need to enhance our scraper to avoid blocking and captchas. For that, let's take a look at ScrapFly web scraping API service, which can easily allow us to achieve this by adding a few minor modifications to our scraper code.

Bypass Tripadvisor Blocking with Scrapfly

Scraping TripAdvisor.com data doesn't seem to be too difficult. However, our scraper is very likely to get blocked or requested to solve captchas when scraping at scale, resisting our web scraping process.

For scraping tripadvisor using scrapfly, we'll be using scrapfly-sdk python package. First, let's install scrapfly-sdk using pip:

$ pip install scrapfly-sdk

To take advantage of ScrapFly's API in our TripAdvisor web scraper all we need to do is modify our httpx session code with the scrapfly-sdk client requests:

from scrapfly import ScrapflyClient, ScrapeConfig

client = ScrapflyClient(key="Your ScrapFly API key")
result = client.scrape(ScrapeConfig(
    url="some tripadvisor URL",
    asp=True, # enable Anti Scraping Protection
    country="US", # select a specific country location
    render_js=True # enable JavaScript rendering if needed, similar to headless browsers
))

html = result.content  # get the page HTML
selector = result.selector # use the built-in parsel selector

FAQ

To wrap this guide up let's take a look at some frequently asked questions about web scraping tripadvisor.com:

Is it legal to scrape tripadvisor.com?

Yes. TripAdvisor's data is publicly available, and we're not extracting anything personal or private. Scraping tripadvisor.com at slow, respectful rates would fall under the ethical scraping definition. That being said, for scraping reviews we should avoid collecting personal information such as users' names in GDRP-compliant countries (like the EU). For more, see our Is Web Scraping Legal? article.

Why scrape TripAdvisor instead of using TripAdvisor's API?

Unfortunately, TripAdvisor's API is difficult to use and very limited. For example, it provides only 3 reviews per location. By scraping public TripAdvisor pages we can collect all of the reviews and hotel details, which we couldn't get through TripAdvisor API otherwise.

TripAdvisor Scraping Summary

In this tutorial, we've taken a look at scraping Tripadvisor.com for hotel overview, review and pricing data. We've also explained how to discover hotel listings using Tripadvisor's search.

For our scraper, we used Python with popular community packages like httpx and parsel. To scrape tripadvisor we used the classic HTML parsing as well as modern hidden web data scraping techniques.

Finally, to avoid being blocked and to scale up our scraper we've taken a look at Scrapfly web scraping API through Scrapfly-SDK package.