🗺️ TripAdvisor Scraper – Hotels, Restaurants & Reviews

What does this TripAdvisor Scraper do?

This actor extracts structured data from TripAdvisor — hotels, restaurants, attractions, and vacation rentals — including full listing details and user reviews, all in a single run. No need to chain two separate actors. Pass a search query like "hotels in Miami" or paste direct TripAdvisor URLs and get back clean, structured JSON ready for your pipeline, CRM, or AI agent.

Why use this TripAdvisor Scraper?

• All-in-one — listings + reviews in a single actor run (competitors require two)
• 40% cheaper than the market leader's free tier at $0.003/listing and $0.001/review
• B2B-ready output — includes phone, website, price range, ranking, coordinates, and amenities
• MCP-optimized — semantic field names and consistent output shape make it work seamlessly with Claude and other AI agents
• No login required — scrapes publicly available data without credentials

What data does this TripAdvisor Scraper extract?

Listing data (record_type: "listing")

Field	Type	Description
name	string	Hotel / restaurant / attraction name
category	string	Hotel, Restaurant, Attraction, Vacation Rental
address	string	Full street address
city	string	City
country	string	Country
phone	string	Contact phone number
website	string	External website URL (decoded from TA redirect)
rating	number	TripAdvisor rating (1.0–5.0)
review_count	integer	Total number of reviews
price_range	string	$, $$, $$$, or $$$$
price_level	integer	1–4 numeric price level
ranking	string	Full ranking text, e.g. "#3 of 233 Hotels in Miami Beach"
ranking_position	integer	Numeric ranking within location
ranking_total	integer	Total properties in category + location
description	string	Property description
amenities	array	List of amenities (Pool, WiFi, Spa, etc.)
cuisines	array	Cuisine types (restaurants only)
awards	array	TripAdvisor awards (Travelers' Choice, etc.)
latitude	number	GPS latitude
longitude	number	GPS longitude
photos_count	integer	Number of photos on the listing
tripadvisor_id	string	TripAdvisor location ID
tripadvisor_url	string	Direct link to the TripAdvisor listing
scraped_at	string	ISO 8601 timestamp
source_url	string	Page URL scraped

Review data (record_type: "review")

Field	Type	Description
listing_name	string	Name of the reviewed property
listing_url	string	TripAdvisor URL of the property
rating	integer	Star rating (1–5)
title	string	Review headline
text	string	Full review body (including expanded "read more")
date_of_stay	string	When reviewer visited, e.g. "March 2024"
travel_type	string	Couples, Families, Solo, Business, Friends
reviewer_name	string	TripAdvisor username
reviewer_location	string	Reviewer's home location
helpful_votes	integer	How many found this review helpful
owner_response	string	Management's response to the review
scraped_at	string	ISO 8601 timestamp
source_url	string	Page URL scraped

How to use this TripAdvisor Scraper

Option 1: Search by query

{
    "searchQueries": ["hotels in Miami Beach", "restaurants in Paris 1st arrondissement"],
    "scrapeMode": "listings_and_reviews",
    "maxResults": 20,
    "maxReviewsPerListing": 15
}

Option 2: Scrape specific URLs

{
    "startUrls": [
        { "url": "https://www.tripadvisor.com/Hotel_Review-g294471-d234567-Reviews-The_Setai-Miami_Beach.html" },
        { "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d1234567-Reviews-Eleven_Madison_Park-New_York_City.html" }
    ],
    "scrapeMode": "listings_and_reviews",
    "maxReviewsPerListing": 50
}

Option 3: Reviews only

{
    "startUrls": [{ "url": "https://www.tripadvisor.com/Hotel_Review-g294471-d234567-Reviews-MyHotel.html" }],
    "scrapeMode": "reviews_only",
    "maxReviewsPerListing": 200
}

Via API (Node.js)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('khadinakbar/tripadvisor-scraper').call({  // replace with your username after first push
    searchQueries: ['hotels in Barcelona'],
    scrapeMode: 'listings_and_reviews',
    maxResults: 10,
    maxReviewsPerListing: 20,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const listings = items.filter(i => i.record_type === 'listing');
const reviews  = items.filter(i => i.record_type === 'review');
console.log(`${listings.length} listings, ${reviews.length} reviews`);

Via API (Python)

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("khadinakbar/tripadvisor-scraper").call(run_input={  # replace with your username after first push
    "searchQueries": ["restaurants in Rome"],
    "scrapeMode": "listings_and_reviews",
    "maxResults": 15,
    "maxReviewsPerListing": 10,
})

items = client.dataset(run["defaultDatasetId"]).list_items().items
listings = [i for i in items if i["record_type"] == "listing"]
reviews  = [i for i in items if i["record_type"] == "review"]

Pricing

This actor uses pay-per-event pricing with two event types:

Event	Price	Description
Listing scraped	$0.003	Each hotel / restaurant / attraction page scraped
Review scraped	$0.001	Each individual user review extracted

Cost examples

Task	Listings	Reviews	Total cost
Quick test	5	50	~$0.065
City hotel survey	30	0	~$0.09
Competitive review audit	10	200	~$0.23
Monthly monitoring	100	1,000	~$1.30

Compared to the market leader (up to $0.005/listing + $0.005/review), this actor saves you 40–80% on the same data.

Input configuration

Parameter	Type	Default	Description
searchQueries	string[]	—	Search terms, e.g. "hotels in Tokyo"
startUrls	URL[]	—	Direct TripAdvisor listing URLs
scrapeMode	string	listings_and_reviews	listings_only, reviews_only, or listings_and_reviews
maxResults	integer	10	Max listing pages to scrape
maxReviewsPerListing	integer	20	Reviews per listing (0 = skip reviews)
proxyConfiguration	object	Apify residential	Proxy settings — residential recommended for TripAdvisor

Proxy requirements

TripAdvisor uses anti-bot measures. Residential proxies are strongly recommended and are pre-configured by default. Using datacenter proxies may result in CAPTCHAs or blocks. The Apify residential proxy pool is included in your plan.

FAQ

Can I scrape all TripAdvisor categories?
Yes — hotels, restaurants, attractions, and vacation rentals are all supported. The actor auto-detects the listing type from the URL.

Why do some fields come back as null?
TripAdvisor doesn't display every field for every listing type. Restaurants don't have hotel amenities; not all hotels list a website. Missing values are always null, never empty strings, so your code can filter cleanly.

How many results can I scrape per run?
The actor supports up to 1,000 listings and up to 500 reviews per listing. For very large runs, schedule multiple runs with different search queries.

Will it break if TripAdvisor updates their design?
The actor uses multiple CSS selector fallbacks per field plus __NEXT_DATA__ JSON extraction, making it more resilient than single-selector scrapers. If TripAdvisor makes breaking changes, the actor will be updated promptly.

Is this actor MCP-compatible?
Yes. The output uses semantic field names and a consistent structure across all records, making it ideal for use with Claude via the Apify MCP server.

Can I run it on a schedule?
Yes — use Apify Schedules to monitor competitor pricing, track review sentiment over time, or maintain a fresh hotel database.

Use cases

Hospitality competitive intelligence — Track competitor ratings, review counts, and amenities across your market. Identify weaknesses in competitor listings to target in your marketing.

Market research — Benchmark accommodation prices and quality in new markets before expansion decisions.

Reputation management — Monitor your property's reviews in real time. Feed reviews into a sentiment analysis pipeline for early issue detection.

Travel content creation — Populate travel guides, blog posts, or recommendation engines with structured property data.

Lead generation — Extract business contact information (phone, website) for hospitality-sector B2B outreach.

AI agent pipelines — Use with Claude via Apify MCP to answer questions like "find me the top 10 hotels in Lisbon under $$$ with a pool."

Works great with

• khadinakbar/shopify-all-in-one-scraper — Scrape hotel gift shops or destination retail alongside your property research.
• khadinakbar/google-trends-scraper — Combine with travel search trend data for demand forecasting.

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This actor extracts publicly available data from TripAdvisor. Users are responsible for compliance with applicable laws, TripAdvisor's Terms of Service, and relevant data protection regulations (GDPR, CCPA). Use responsibly and only for lawful purposes.

Export scraped data, run the scraper via API, schedule and monitor runs, or integrate with other tools.