TripAdvisor Scraper

Scrape reviews, ratings, and business details from TripAdvisor hotels, restaurants, and attractions. Search by name or paste URLs -- no need to find TripAdvisor links manually.

What does TripAdvisor Scraper do?

TripAdvisor Scraper extracts review data from any TripAdvisor listing. It supports two input modes:

• Search by name -- type a hotel, restaurant, or city name (e.g., "Hilton New York", "Best pizza Rome") and the scraper finds matching places on TripAdvisor automatically
• Direct URLs -- paste one or more TripAdvisor URLs for hotels, restaurants, or attractions

You can combine both modes in a single run. The scraper returns structured reviews with ratings, text, dates, trip types, and full business metadata. Export as JSON, CSV, or Excel.

Built on a pure HTTP architecture (no Playwright), it uses only 256 MB of memory -- 8x less than browser-based alternatives -- keeping your platform costs low.

Who is it for?

• 🏨 Hospitality managers -- monitor guest reviews and ratings for your properties and competitors
• 📈 Market researchers -- analyze traveler sentiment across hotels, restaurants, and destinations
• 🏢 Tourism boards -- track visitor feedback and destination popularity trends
• 💼 Hotel investors -- evaluate property performance and reputation before acquisition
• 📊 Competitive intelligence teams -- benchmark review scores against industry rivals
• 🔍 SEO and reputation agencies -- aggregate and report on client review profiles at scale
• 💻 Developers -- integrate TripAdvisor review data into apps and dashboards via API

Why use this scraper?

• ✅ Search by name -- no need to hunt for TripAdvisor URLs; just type a place name
• ✅ 40% cheaper -- $0.003 per review vs. $0.005 charged by the market leader
• ✅ 8x lower memory -- 256 MB vs. 2048 MB, meaning lower platform costs on top of lower per-review pricing
• ✅ Pure HTTP -- no Playwright or browser overhead; faster and more reliable
• ✅ All place types -- hotels, restaurants, attractions, and city/location pages
• ✅ Language filtering -- get reviews in English, Spanish, French, or any supported language
• ✅ Combine modes -- mix search queries and direct URLs in the same run
• ✅ Scalable -- scrape 10 reviews or 10,000 in a single run

What data can you extract?

Field	Description
Review title	Headline of the review
Review text	Full review content
Rating	Star rating (1-5)
Published date	When the review was posted
Travel date	Date of stay or visit
Trip type	Couples, Family, Friends, Business, Solo
Username	Reviewer's TripAdvisor username
Helpful votes	Number of helpful votes received
Owner response	Management reply text
Business name	Hotel, restaurant, or attraction name
Business rating	Overall TripAdvisor rating
Price range	Listed price range
Ranking	TripAdvisor ranking in the area
Address	Full street address
Phone	Phone number
Website	Business website URL
Cuisine	Restaurant cuisine types
Coordinates	Latitude and longitude

How much does it cost to scrape TripAdvisor reviews?

This actor uses pay-per-event pricing -- you only pay for what you extract.

Event	Price	Description
Run started	$0.005	One-time charge per actor run
Review scraped	$0.003	Per review extracted

Cost examples:

Reviews	Cost
100	$0.31
500	$1.51
1,000	$3.01
5,000	$15.01
10,000	$30.01

Apify's free plan includes $5/month of platform credits, so you can scrape approximately 1,600 reviews per month at no cost.

Comparison with alternatives: The most popular TripAdvisor scraper on Apify charges $0.005 per review and requires 2048 MB of memory. This scraper is 40% cheaper per review and uses 8x less memory (256 MB), which further reduces your platform compute costs.

How to scrape TripAdvisor reviews

Option A: Search by name (recommended)

1. Go to TripAdvisor Scraper on Apify Store and click Try for free
2. Type a place name into the Search Queries field (e.g., "Hilton New York" or "Best pizza Rome")
3. Select the Place Type -- Hotels & Lodging, Restaurants, or Locations & Cities
4. Set Max Places per Query to control how many matching places to scrape
5. Set Max Reviews per Place to limit the number of reviews
6. Click Start and wait for your data
7. Download results as JSON, CSV, or Excel

Option B: Direct URLs

1. Go to TripAdvisor Scraper on Apify Store and click Try for free
2. Find the hotel, restaurant, or attraction on TripAdvisor
3. Copy the URL from your browser
4. Paste one or more URLs into the TripAdvisor URLs field
5. Set Max Reviews per Place to limit the number of reviews
6. Click Start and wait for your data
7. Download results as JSON, CSV, or Excel

You can use both search queries and direct URLs in the same run.

Input parameters

Parameter	Type	Description	Default
searchQueries	array of strings	Search for places by name (e.g., "Hilton New York", "Best pizza Rome")	--
urls	array of strings	Direct TripAdvisor URLs to scrape reviews from	--
placeType	string	Type of place to search for: lodging, restaurants, or geos	lodging
maxPlacesPerQuery	integer	Max places to scrape per search query (1-10)	5
maxReviewsPerPlace	integer	Max reviews per place. Set 0 for all reviews.	100
reviewLanguage	string	Filter reviews by language code (e.g., "en", "es", "fr"). Empty = all.	all
maxRequestRetries	integer	Number of retry attempts for failed requests (1-10)	5

Input example: Search by name

{
    "searchQueries": ["Hilton New York Times Square"],
    "placeType": "lodging",
    "maxPlacesPerQuery": 1,
    "maxReviewsPerPlace": 50
}

Input example: Search for restaurants

{
    "searchQueries": ["Best pizza Rome", "Sushi Tokyo"],
    "placeType": "restaurants",
    "maxPlacesPerQuery": 3,
    "maxReviewsPerPlace": 20
}

Input example: Direct URLs

{
    "urls": [
        "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html",
        "https://www.tripadvisor.com/Restaurant_Review-g60763-d457808-Reviews-Joe_s_Pizza-New_York_City_New_York.html"
    ],
    "maxReviewsPerPlace": 100
}

Input example: Combined search + URLs

{
    "searchQueries": ["Best hotels Bali"],
    "placeType": "lodging",
    "maxPlacesPerQuery": 3,
    "urls": [
        "https://www.tripadvisor.com/Restaurant_Review-g60763-d457808-Reviews-Joe_s_Pizza-New_York_City_New_York.html"
    ],
    "maxReviewsPerPlace": 50
}

Output example

Each review is returned as a structured JSON object:

{
    "id": "1052611391",
    "locationId": "208453",
    "locationName": "Hilton New York Times Square",
    "title": "Best service",
    "text": "I had a wonderful stay at the New York Hilton Midtown. The staff was incredibly friendly and the rooms were spotless. Location is unbeatable for Times Square access.",
    "rating": 5,
    "publishedDate": "2026-03-10T00:00:00.000Z",
    "travelDate": "February 2026",
    "tripType": "Couples",
    "username": "traveler123",
    "helpfulVotes": 3,
    "url": "https://www.tripadvisor.com/ShowUserReviews-d208453-r1052611391.html",
    "ownerResponse": "Thank you for your kind review. We look forward to welcoming you back!",
    "scrapedAt": "2026-03-14T15:30:00.000Z"
}

Tips for best results

• 💡 Start small -- test with 10-20 reviews first, then scale up once you confirm the output format works for your pipeline
• 💡 Use search mode for quick exploration -- no need to browse TripAdvisor to find URLs
• 💡 Filter by language -- set reviewLanguage to "en", "es", "fr", etc. to get reviews in a specific language only
• 💡 Set maxPlacesPerQuery to 1 if you want exactly the top-matching place for each search
• 💡 Combine modes -- use search queries for discovery and direct URLs for known properties in the same run
• 💡 Schedule runs for ongoing monitoring -- use Apify Schedules to track review trends weekly
• 💡 All place types work -- hotels, restaurants, attractions, and location/city pages are all supported

Integrations

Connect TripAdvisor Scraper with your tools and workflows:

• Google Sheets -- export reviews to a spreadsheet for sentiment tracking and analysis
• Slack -- get notified when new review data is ready
• Zapier -- trigger workflows when reviews are scraped (e.g., alert on negative reviews)
• Make -- build automated review monitoring pipelines
• Webhooks -- send results to your own API endpoint
• Amazon S3 -- store large review datasets in cloud storage
• Google BigQuery -- load reviews into a data warehouse for advanced analytics

See all integrations for the full list.

Programmatic access via API

Node.js

import { ApifyClient } from 'apify-client';

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

// Search by name -- no TripAdvisor URL needed
const run = await client.actor('automation-lab/tripadvisor-scraper').call({
    searchQueries: ['Hilton New York Times Square'],
    placeType: 'lodging',
    maxPlacesPerQuery: 1,
    maxReviewsPerPlace: 50,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => console.log(`${item.rating}/5 -- ${item.title}`));

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

# Search by name -- no TripAdvisor URL needed
run = client.actor("automation-lab/tripadvisor-scraper").call(run_input={
    "searchQueries": ["Hilton New York Times Square"],
    "placeType": "lodging",
    "maxPlacesPerQuery": 1,
    "maxReviewsPerPlace": 50,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['rating']}/5 -- {item['title']}")

cURL

curl -X POST "https://api.apify.com/v2/acts/automation-lab~tripadvisor-scraper/runs?token=YOUR_API_TOKEN&waitForFinish=120" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQueries": ["Hilton New York Times Square"],
    "placeType": "lodging",
    "maxPlacesPerQuery": 1,
    "maxReviewsPerPlace": 50
  }'

Use with AI agents via MCP

TripAdvisor Scraper is available as a tool for AI assistants that support the Model Context Protocol (MCP).

Setup for Claude Code

$claude mcp add --transport http apify "https://mcp.apify.com"

Setup for Claude Desktop, Cursor, or VS Code

Add this to your MCP config file:

{
    "mcpServers": {
        "apify": {
            "url": "https://mcp.apify.com"
        }
    }
}

Example prompts

Once connected, try asking your AI assistant:

• "Search for the top 5 hotels in Paris and get their TripAdvisor reviews"
• "Scrape reviews for Hilton New York Times Square"
• "Get restaurant reviews for the best pizza places in Rome"
• "Compare TripAdvisor ratings for hotels in Bali vs. Maldives"

Learn more in the Apify MCP documentation.

Legality

Scraping publicly available data is generally legal according to the US Court of Appeals ruling (HiQ Labs v. LinkedIn). This actor only accesses publicly available information and does not require authentication. Always review and comply with the target website's Terms of Service before scraping. For personal data, ensure compliance with GDPR, CCPA, and other applicable privacy regulations.

FAQ

How much does it cost to scrape TripAdvisor reviews?

At $0.003 per review plus a $0.005 run start fee, scraping 1,000 reviews costs about $3.01. Apify's free plan includes $5/month of platform credits, so you can scrape approximately 1,600 reviews per month for free.

Can I search for places by name instead of pasting URLs?

Yes. Use the searchQueries field to type a hotel, restaurant, or city name. The scraper searches TripAdvisor and finds matching places automatically. You do not need to browse TripAdvisor or copy URLs.

What place types can I search for?

Set placeType to one of three options: lodging (hotels, B&Bs, resorts), restaurants (restaurants, cafes, bars), or geos (cities, regions, destinations).

Can I use search queries and direct URLs together?

Yes. You can provide both searchQueries and urls in the same run. The scraper will process all of them and output reviews from every matched place.

Can I scrape any TripAdvisor listing?

Yes -- hotels, restaurants, and attractions are all supported. Just paste the TripAdvisor URL or search by name.

How fast is the scraper?

It scrapes about 10 reviews per page request. A typical run of 50 reviews completes in under 20 seconds.

What formats can I export?

JSON, CSV, Excel, XML, or HTML table. You can also access data programmatically via the Apify API.

Can I filter reviews by language?

Yes. Set the reviewLanguage parameter to a language code (e.g., "en", "es", "fr") to only get reviews written in that language. Leave it empty to get reviews in all languages.

Can I schedule regular review monitoring?

Yes. Use Apify Schedules to run the scraper on a daily or weekly basis. Combine with a Google Sheets integration to track review trends over time.

Is it legal to scrape TripAdvisor?

This actor is provided for educational and research purposes. Users are responsible for ensuring their use complies with TripAdvisor's Terms of Service and applicable laws. See the Legality section above.

The scraper returned no reviews. What's wrong?

Check the following:

• If using direct URLs, make sure you are using a full TripAdvisor listing URL (e.g., https://www.tripadvisor.com/Hotel_Review-...). Search result pages, city overview pages, and shortened URLs are not supported.
• If using search queries, try a more specific name or check the placeType setting matches what you are looking for.
• The listing must have publicly visible reviews.
• Some listings may have very few or no reviews.

How is this different from other TripAdvisor scrapers?

This scraper is 40% cheaper per review ($0.003 vs. $0.005) and uses 8x less memory (256 MB vs. 2048 MB) compared to the market leader. It also supports search-by-name, so you do not need to find TripAdvisor URLs manually. Pure HTTP architecture means no browser overhead and faster, more reliable runs.

How do I scrape TripAdvisor reviews for a hotel or restaurant?

There are two ways to scrape TripAdvisor reviews with this actor. The easiest is to search by name — just type the hotel or restaurant name into the searchQueries field (e.g., "Marriott Times Square" or "Nobu London"). The scraper finds the matching TripAdvisor listing automatically, so you never need to browse TripAdvisor or copy a URL.

If you already have the TripAdvisor URL, paste it into the urls field instead. Both methods can be used in the same run — for example, combining a name search for new properties with direct URLs for ones you already track. Results include the full review text, star rating, travel date, trip type (Couples, Family, Solo, etc.), and any owner response from the property.

How can hotels use TripAdvisor reviews for reputation management?

Your TripAdvisor reputation directly affects booking rates — studies consistently show that a 1-point increase in average rating on travel platforms correlates with a 5–9% increase in room revenue. Effective reputation management requires monitoring reviews regularly, not just checking them once a week manually.

With TripAdvisor Scraper you can build an automated review monitoring pipeline:

1. Schedule a weekly run for all your properties using the search-by-name mode.
2. Export new reviews to Google Sheets or a Slack channel for your customer service team.
3. Flag low-rated reviews (rating ≤ 2) automatically using a Make or Zapier workflow so they receive a priority response.
4. Track your rolling average rating over time to measure whether response programs are improving guest sentiment.

At $0.003 per review, monitoring 50 new reviews per week across 10 properties costs under $2/month.

How do I analyze TripAdvisor sentiment for competitive benchmarking?

Comparing your TripAdvisor ratings and review themes against competitors helps you understand where you outperform and where you fall short in the eyes of travelers. A structured sentiment analysis workflow:

1. Scrape 200–500 reviews each for your property and 3–5 direct competitors using the searchQueries or urls input.
2. Export to CSV and load into a spreadsheet or Python/R environment.
3. Compare average ratings, rating distributions (% of 5-star vs. 1-star), and tripType breakdowns (do competitors get stronger ratings from business travelers?).
4. Run keyword frequency analysis on text to identify recurring praise or complaints — words like "noisy", "slow check-in", or "incredible breakfast" repeat patterns reveal operational strengths and weaknesses.

This analysis takes minutes to set up but provides hotel managers and investors with actionable data that normally requires expensive reputation management software.

How can travel agencies and tourism boards use TripAdvisor data?

TripAdvisor is the world's largest travel platform with hundreds of millions of reviews, making it a rich dataset for understanding traveler preferences and destination performance. Tourism boards and travel agencies use this data in several ways:

• Destination health monitoring: Scrape reviews for all major hotels, restaurants, and attractions in a destination using the geos place type. Track average ratings over time to detect shifts in visitor satisfaction — a declining average across multiple properties often signals infrastructure or safety issues before they appear in traditional surveys.
• Seasonal trend analysis: Filter reviews by travelDate to understand how visitor sentiment changes by season. Properties that score well in summer but poorly in winter reveal seasonal operational gaps.
• Attraction discovery: Search for the top-ranked attractions in a city or region using searchQueries with placeType: "geos" to build comprehensive destination guides with real visitor feedback.
• Language-specific insights: Set reviewLanguage to "es", "de", or "zh" to analyze feedback from specific traveler nationalities — useful when targeting marketing campaigns at particular source markets.

How do hotel investors use TripAdvisor reviews for due diligence?

Before acquiring or investing in a hospitality property, savvy investors look beyond the financials to understand the guest experience track record. TripAdvisor reviews provide a longitudinal record of how a property has performed over time — including under previous ownership.

Key signals to analyze during due diligence:

• Rating trend: Has the average rating improved, declined, or stayed flat over the past 2–3 years? A declining trend under current ownership may indicate deferred maintenance or management issues.
• Review volume: A sudden drop in new reviews may indicate declining bookings. A recent spike may reflect a management change or renovation.
• Owner response rate: Properties where management actively responds to reviews (especially negative ones) tend to perform better operationally. Check the ownerResponse field to assess engagement.
• Recurring complaints: Mine the review text for repeated mentions of specific issues (HVAC, cleanliness, noise). These patterns often signal capital expenditure needs not visible on a balance sheet.

Scrape the full review history for a target property (set maxReviewsPerPlace: 0 for all reviews) and cross-reference with the financial performance data from the seller.

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