🥃 TripAdvisor Reviews Scraper API: Extract Hotel, Restaurant & Attraction Reviews With Sub-Ratings at Scale

The TripAdvisor Reviews Scraper by The Wolves is an Apify Actor that extracts guest reviews, star ratings, sub-category ratings (Value, Rooms, Location, Cleanliness, Service, Sleep Quality), trip details, owner responses, reviewer profiles, and location metadata from any TripAdvisor listing. This tripadvisor review scraper api delivers 30+ structured fields per review at speeds of 100-200 reviews per second — with no proxy configuration required on your end.

$0.50 per 1,000 reviews. Scrape TripAdvisor reviews from hotels, restaurants, attractions, and vacation rentals at scale. Filter by language, rating, or date range. Every review includes sub-category quality ratings that most scrapers miss entirely — giving you granular insight into what guests actually value.

Built by The Wolves — a pack of data scientists with over a decade of experience in web scraping and API development. We build scrapers the way data teams actually need them: rich output, precise filtering, and pricing that scales.

Pricing: $0.50 per 1,000 reviews | 100-200 reviews/second | No proxy setup required

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Table of Contents

1. What Does the TripAdvisor Reviews Scraper Do?
2. Features and Capabilities
3. Pricing
4. Input Parameters
5. Output Format and Data Fields
6. Custom Map Function
7. AI Agent Integration via MCP
8. The Wolves Scraper Pack
9. Demo Mode and Free Testing
10. Automated Scheduling and Monitoring
11. Quick Start Guide
12. Use Cases and Industries
13. Troubleshooting
14. Frequently Asked Questions
15. Contact

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What Does the TripAdvisor Reviews Scraper Do?

TripAdvisor review extraction is the automated process of collecting guest reviews, ratings, sub-ratings, trip metadata, and reviewer profiles from TripAdvisor listings. TripAdvisor hosts over 1 billion reviews across 8 million listings — making it the single largest source of structured guest feedback for the travel and hospitality industry.

The Wolves TripAdvisor Reviews Scraper extracts deeply structured review data that goes far beyond basic text and ratings. Each review includes sub-category ratings (Value, Rooms, Location, Cleanliness, Service, Sleep Quality), trip context (travel dates, trip type), owner/management responses, and full reviewer profiles with contribution history and hometown data.

This level of data granularity is what separates this scraper from basic alternatives. You don't just get "4 stars" — you get a breakdown of why the guest gave 4 stars, which categories scored high, and which fell short.

What You Get From Every Review

Review Content and Ratings

• Full review text and title
• Overall rating (1–5) plus sub-category ratings (Value, Rooms, Location, Cleanliness, Service, Sleep Quality)
• Publication date and creation date
• Review language and original language
• Room tip (if provided)

Trip Context

• Stay date or visit date
• Trip type (Business, Couples, Family, Friends, Solo)
• Direct review URL on TripAdvisor

Location and Business Data

• Location name, ID, and place type (Accommodation, Restaurant, Attraction)
• Parent geographic region ID
• Owner/management response to the review

Reviewer Profile

• Username, display name, and profile URL
• Hometown (city and region)
• Total contributions count and helpful vote count
• Verified status and avatar images

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Features and Capabilities

Input Flexibility

Input Type	Example	Best For
TripAdvisor URL	https://www.tripadvisor.com/Hotel_Review-g187801-d1469038-Reviews-Hotel_Cosmopolitan_Bologna.html	Targeting a specific hotel, restaurant, or attraction

Core Capabilities

• High-Speed Extraction — 100-200 reviews per second
• Sub-Category Ratings — Value, Rooms, Location, Cleanliness, Service, Sleep Quality per review
• Language Filtering — Extract reviews in specific languages only
• Rating Filtering — Target specific star ratings (e.g., only 1-star reviews)
• Date Filtering — Use the since parameter to extract only reviews after a specific date
• Page Offset — Start extraction from a specific review page
• Owner Responses — Capture management replies to guest reviews
• Trip Metadata — Stay dates and trip types for each review
• Reviewer Profiles — Full profile data including hometown, contribution counts, and verification status
• No Proxy Required — The scraper handles proxy rotation internally
• Custom Map Function — Transform output with custom JavaScript before saving
• Multiple Export Formats — JSON, CSV, Excel direct download
• API Integration — RESTful API for Python, Node.js, or any HTTP client

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Pricing

Pay-Per-Review Pricing

No subscriptions, no rentals, no minimum commitments. You pay only for the reviews you extract:

Metric	Price
Per 1,000 reviews	$0.50
Per review	$0.0005
Per 100,000 reviews	$50.00

Example: Extracting 5,000 reviews from a hotel chain across 10 properties costs $2.50. Monitoring all reviews from your top 50 competitor hotels monthly — with date filtering to avoid duplicates — keeps costs predictable and low.

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Input Parameters

Field	Type	Description	Default
startUrls	array	TripAdvisor place URLs. Paste the URLs directly and retrieve results immediately	[]
since	string	Returns only reviews published after this date	null
startPage	integer	Start page number for review extraction (useful for resuming or paginating)	1
ratings	string	Filter by specific star ratings (e.g., 5 for 5-star reviews only)	[]
locale	string	Requests TripAdvisor to auto-translate reviews. Results may vary — TripAdvisor's translation is unreliable.	null
languages	array	Filter by review language (e.g., ["en"] for English only)	[]
maxItems	number	Maximum number of reviews to output	Infinity
customMapFunction	string	JavaScript function to transform each review object (transformation only, not filtering)	null

Input Examples

Single Hotel — All Reviews:

{
    "startUrls": [
        "https://www.tripadvisor.com/Hotel_Review-g187801-d1469038-Reviews-Hotel_Cosmopolitan_Bologna.html"
    ],
    "maxItems": 1000
}

English Reviews Only — 1-Star Negative Feedback:

{
    "startUrls": [
        "https://www.tripadvisor.com/Hotel_Review-g187801-d1469038-Reviews-Hotel_Cosmopolitan_Bologna.html"
    ],
    "languages": ["en"],
    "ratings": "1",
    "maxItems": 500
}

Date-Filtered — Reviews Since January 2025:

{
    "startUrls": [
        "https://www.tripadvisor.com/Hotel_Review-g187801-d1469038-Reviews-Hotel_Cosmopolitan_Bologna.html"
    ],
    "since": "2025-01-01",
    "maxItems": 1000
}

Multiple Properties — Competitive Analysis:

{
    "startUrls": [
        "https://www.tripadvisor.com/Hotel_Review-g187801-d1469038-Reviews-Hotel_Cosmopolitan_Bologna.html",
        "https://www.tripadvisor.com/Hotel_Review-g187791-d195210-Reviews-Grand_Hotel_Majestic.html"
    ],
    "languages": ["en", "fr", "de"],
    "maxItems": 2000
}

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Output Format and Data Fields

Each extracted review is a deeply structured JSON object with 30+ fields. Here is a trimmed sample showing the key data points:

{
    "id": 939155097,
    "createdDate": "2024-02-21",
    "publishedDate": "2024-02-21",
    "rating": 5,
    "title": "Incredible find!",
    "text": "This place was an incredible find. The staff is amazing. The cost is reasonable and the food in the restaurant was out of this world.",
    "language": "en",
    "originalLanguage": "en",
    "roomTip": null,
    "url": "/ShowUserReviews-g187801-d1469038-r939155097-Hotel_Cosmopolitan_Bologna.html",
    "absoluteUrl": "https://www.tripadvisor.com/ShowUserReviews-g187801-d1469038-r939155097-Hotel_Cosmopolitan_Bologna.html",
    "helpfulVotes": 0,
    "photos": [],
    "ownerResponse": null,
    "tripInfo": {
        "stayDate": "2024-02-29",
        "tripType": "BUSINESS"
    },
    "location": {
        "locationId": 1469038,
        "placeType": "ACCOMMODATION",
        "parentGeoId": 187801,
        "name": "Hotel Cosmopolitan Bologna"
    },
    "additionalRatings": [
        { "rating": 5, "ratingLabel": "Value" },
        { "rating": 5, "ratingLabel": "Rooms" },
        { "rating": 5, "ratingLabel": "Location" },
        { "rating": 5, "ratingLabel": "Cleanliness" },
        { "rating": 5, "ratingLabel": "Service" },
        { "rating": 5, "ratingLabel": "Sleep Quality" }
    ],
    "userProfile": {
        "displayName": "mkabakian",
        "username": "mkabakian",
        "isVerified": false,
        "contributionCounts": {
            "sumAllUgc": 1,
            "helpfulVote": 0
        },
        "hometown": {
            "location": {
                "name": "Los Angeles",
                "additionalNames": { "long": "Los Angeles, California" }
            }
        },
        "route": { "url": "/Profile/mkabakian" }
    }
}

Complete Field Reference

Field	Type	Description
id	number	Unique TripAdvisor review identifier
createdDate	string	Date the review was created
publishedDate	string	Date the review was published
rating	number	Overall star rating (1-5)
title	string	Review title/headline
text	string	Full review body text
language	string	Language code of the review
originalLanguage	string	Original language before any translation
roomTip	string	Room-specific tip from the reviewer (hotels)
url	string	Relative TripAdvisor review URL
absoluteUrl	string	Full TripAdvisor review URL
helpfulVotes	number	Number of helpful votes from other users
photos	array	Review photos uploaded by the reviewer
ownerResponse	object	Management/owner reply to the review
tripInfo.stayDate	string	Date of stay or visit
tripInfo.tripType	string	Trip type: BUSINESS, COUPLES, FAMILY, FRIENDS, SOLO
location.name	string	Name of the hotel, restaurant, or attraction
location.placeType	string	ACCOMMODATION, RESTAURANT, or ATTRACTION
location.locationId	number	TripAdvisor location identifier
location.parentGeoId	number	Parent geographic region ID
additionalRatings	array	Sub-category ratings (Value, Rooms, Location, Cleanliness, Service, Sleep Quality)
userProfile.displayName	string	Reviewer's display name
userProfile.username	string	Reviewer's TripAdvisor username
userProfile.isVerified	boolean	Whether the reviewer is verified
userProfile.contributionCounts	object	Total reviews and helpful votes by this reviewer
userProfile.hometown	object	Reviewer's stated hometown location
userProfile.route.url	string	Reviewer's TripAdvisor profile URL

Data Fields by Use Case

Use Case	Key Fields
Hospitality Sentiment Analysis	text, title, rating, additionalRatings, language
Sub-Category Quality Benchmarking	additionalRatings (Value, Rooms, Cleanliness, Service, Location, Sleep Quality)
Competitive Hotel Analysis	location.name, rating, additionalRatings, text, publishedDate
Travel Trend Research	tripInfo.tripType, tripInfo.stayDate, rating, location.placeType
Reputation Management	rating, text, ownerResponse, publishedDate, helpfulVotes
Reviewer Credibility Analysis	userProfile.contributionCounts, userProfile.isVerified, userProfile.hometown
NLP Training Data	text, title, rating, additionalRatings, language

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Custom Map Function

Transform each review before it's saved to the dataset. The customMapFunction parameter accepts a JavaScript function that reshapes every review object. Use this to flatten nested structures, extract specific sub-ratings, or compute derived values.

Important: The custom map function is for transformation only, not filtering. Do not use it for filtering purposes.

Example: Flatten for Sentiment Analysis

(review) => ({
    id: review.id,
    hotel: review.location?.name,
    rating: review.rating,
    title: review.title,
    text: review.text,
    date: review.publishedDate,
    language: review.language,
    tripType: review.tripInfo?.tripType,
    stayDate: review.tripInfo?.stayDate,
    valueRating: review.additionalRatings?.find(r => r.ratingLabel === "Value")?.rating,
    roomsRating: review.additionalRatings?.find(r => r.ratingLabel === "Rooms")?.rating,
    cleanlinessRating: review.additionalRatings?.find(r => r.ratingLabel === "Cleanliness")?.rating,
    serviceRating: review.additionalRatings?.find(r => r.ratingLabel === "Service")?.rating,
    locationRating: review.additionalRatings?.find(r => r.ratingLabel === "Location")?.rating,
    hasOwnerResponse: !!review.ownerResponse
})

Example: Extract for Cross-Platform Comparison

(review) => ({
    platform: "TripAdvisor",
    propertyName: review.location?.name,
    propertyType: review.location?.placeType,
    rating: review.rating,
    text: review.text,
    date: review.publishedDate,
    reviewer: review.userProfile?.displayName,
    reviewerHometown: review.userProfile?.hometown?.location?.name
})

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AI Agent Integration via MCP

Apify provides a hosted Model Context Protocol (MCP) server at mcp.apify.com that allows AI agents and LLM-based applications to discover and run Apify Actors as tools — including this TripAdvisor Reviews Scraper.

What This Means

If you're building AI agents using Claude Desktop, VS Code with MCP support, or any framework that implements the MCP specification, you can give your agent the ability to extract TripAdvisor reviews autonomously. The agent can call this scraper as a tool, receive structured JSON results with sub-ratings and trip metadata, and use them in downstream analysis — all without manual intervention.

How to Connect

Add this scraper to your MCP client configuration:

https://mcp.apify.com?tools=thewolves/tripadvisor-reviews-scraper

Or use the CLI for local development:

$npx @apify/actors-mcp-server --tools thewolves/tripadvisor-reviews-scraper

Use Cases for AI Agent Integration

• Automated reputation monitoring — An AI agent extracts new reviews weekly, summarizes sentiment by sub-category, and flags properties where Cleanliness or Service scores are dropping.
• Competitive intelligence pipelines — Extract reviews from competitor hotels, run comparative analysis on sub-ratings, and generate strategic reports automatically.
• Multi-step research workflows — Extract reviews, classify by topic (food, staff, amenities), compute weighted satisfaction scores, and produce actionable hospitality insights — all in a single agent session.

For full setup instructions, see the Apify MCP documentation.

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The Wolves Scraper Pack

All tools below are built and maintained by The Wolves — a pack of data scientists with over a decade of experience building high-performance scrapers and APIs. Powerful, easy to use, and priced for scale.

Tool	Platform	Price	Best For
TripAdvisor Reviews Scraper	TripAdvisor	$0.50/1K reviews	Hospitality intelligence (You are here)
Google Play Reviews Scraper	Google Play	$0.10/1K reviews	Android app intelligence
App Store Reviews Scraper	Apple App Store	$0.10/1K reviews	iOS app intelligence
Trustpilot Reviews Scraper	Trustpilot	$0.50/1K reviews	SaaS/B2B reputation analysis

What Makes The Wolves TripAdvisor Scraper Different

Capability	This Tool	Basic Alternatives
Sub-category ratings	6 categories per review	Overall rating only
Trip metadata	Stay date + trip type	Not available
Owner responses	Full management replies	Not available
Reviewer profiles	Hometown, contributions, verified status	Username only
Language filtering	Built-in parameter	Manual post-processing
Rating filtering	Built-in parameter	Manual post-processing
Proxy management	Handled internally	User must configure
Speed	100-200 reviews/second	Varies

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Demo Mode and Free Testing

Free plan users can test this TripAdvisor reviews scraper in Demo Mode with a maximum of 10 items per run. Demo Mode is designed to validate the output format, data quality, and field coverage before committing to larger runs.

Demo Mode Limitations:

• Maximum 10 reviews per run
• API access not available on Free plan
• Full functionality requires a paid Apify plan

How to Test:

• Run the scraper with maxItems: 10 to preview the output structure
• Verify that sub-category ratings, trip info, and reviewer profiles are present
• Test language and rating filters with a small sample
• Confirm the customMapFunction works with your transformation logic

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Automated Scheduling and Monitoring

Guest reviews on TripAdvisor appear continuously. For hotel managers, restaurant owners, and reputation management agencies, automated recurring runs ensure you never miss new feedback.

Why Schedule Review Extraction?

• Reputation monitoring — Detect negative reviews within hours, not days
• Competitive tracking — Monitor competitor properties weekly for rating changes and guest complaints
• Seasonal analysis — Track how sentiment shifts across high and low seasons
• Post-renovation tracking — Measure whether facility improvements translate to better sub-ratings
• Agency reporting — Automated data collection for client hospitality dashboards

How to Set Up Scheduled Runs

1. Open the Actor in Apify Console
2. Configure your input parameters (startUrls, since, languages)
3. Click Schedule and set frequency (daily, weekly, monthly)
4. Optionally add a webhook to push new data to your pipeline

Webhook Integration

Combine scheduled runs with webhooks to build fully automated review monitoring:

Scheduled Run -> New Reviews Extracted -> Webhook fires -> Your system receives data

Use webhooks to trigger:

• Slack alerts for 1-star reviews requiring immediate attention
• Database updates with new review data and sub-ratings
• Dashboard refreshes for hospitality analytics platforms
• Email digests summarizing weekly guest sentiment trends

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Quick Start Guide

For Non-Technical Users (Apify Console)

1. Go to TripAdvisor Reviews Scraper on Apify
2. Click Try for free
3. Paste a TripAdvisor hotel, restaurant, or attraction URL into the startUrls field
4. Set maxItems and any language or rating filters
5. Click Start and wait for results
6. Export TripAdvisor reviews to CSV from the Storage tab

For Developers (Python API)

from apify_client import ApifyClient
client = ApifyClient("YOUR_TOKEN")
run = client.actor("thewolves/tripadvisor-reviews-scraper").call(run_input={
    "startUrls": [
        "https://www.tripadvisor.com/Hotel_Review-g187801-d1469038-Reviews-Hotel_Cosmopolitan_Bologna.html"
    ],
    "languages": ["en"],
    "maxItems": 1000
})
items = client.dataset(run["defaultDatasetId"]).list_items().items

For Data Scientists (Sub-Rating Analysis)

Use the customMapFunction to flatten sub-ratings for immediate statistical analysis:

{
    "startUrls": ["YOUR_TRIPADVISOR_URL"],
    "maxItems": 2000,
    "customMapFunction": "(r) => ({ id: r.id, rating: r.rating, date: r.publishedDate, tripType: r.tripInfo?.tripType, value: r.additionalRatings?.find(x => x.ratingLabel === 'Value')?.rating, rooms: r.additionalRatings?.find(x => x.ratingLabel === 'Rooms')?.rating, cleanliness: r.additionalRatings?.find(x => x.ratingLabel === 'Cleanliness')?.rating, service: r.additionalRatings?.find(x => x.ratingLabel === 'Service')?.rating, location: r.additionalRatings?.find(x => x.ratingLabel === 'Location')?.rating })"
}

This produces a flat CSV-ready dataset where each row has the overall rating plus individual sub-category scores — ready for correlation analysis, regression modeling, or dashboard visualization.

For Reputation Managers (Negative Review Monitoring)

{
    "startUrls": ["YOUR_HOTEL_URL", "YOUR_RESTAURANT_URL"],
    "ratings": "1",
    "since": "2025-01-01",
    "maxItems": 500
}

Schedule weekly. Filter for 1-star reviews only. Combine with webhook alerts to notify your team immediately when critical negative feedback appears.

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Use Cases and Industries

Hotel and Resort Sentiment Analysis

Extract thousands of TripAdvisor hotel reviews with sub-category ratings for Value, Rooms, Location, Cleanliness, Service, and Sleep Quality. This granular data enables hospitality teams to pinpoint exactly which operational areas drive guest satisfaction or dissatisfaction — going far beyond a single star rating.

Key fields: rating, additionalRatings, text, title, tripInfo, publishedDate

Restaurant Performance Monitoring

Track restaurant reviews across multiple locations. Analyze how food quality, service speed, and ambiance are perceived over time. Identify seasonal patterns in guest satisfaction and measure the impact of menu changes or management decisions on review sentiment.

Key fields: rating, text, ownerResponse, publishedDate, language

Competitive Benchmarking in Hospitality

Extract reviews from competing hotels or restaurants in the same geographic area. Compare sub-category ratings across properties to identify competitive advantages and weaknesses. A hotel might score highly on Location but poorly on Cleanliness relative to competitors — data that drives targeted operational improvements.

Key fields: location.name, rating, additionalRatings, text, publishedDate

Academic Tourism Research

TripAdvisor is one of the most widely used data sources in academic hospitality and tourism research. Extract large datasets for studies on consumer behavior, destination image, service quality perception, and cross-cultural differences in travel expectations. The tripInfo.tripType field enables segmentation by traveler type (Business, Family, Couples, Solo).

Key fields: text, rating, tripInfo, userProfile.hometown, language, additionalRatings

Travel Agency Market Intelligence

Understand emerging destination trends by analyzing review volume and sentiment across regions. Identify which destinations are gaining positive momentum and which are experiencing service quality declines. Use language filtering to focus on specific source markets.

Key fields: location.name, rating, publishedDate, language, tripInfo.tripType

Online Reputation Management

Monitor client properties across TripAdvisor with scheduled extractions. Track response rates (via ownerResponse), identify recurring complaints, and measure how management responses affect subsequent review sentiment. The helpfulVotes field indicates which negative reviews are most visible to potential guests.

Key fields: rating, text, ownerResponse, helpfulVotes, publishedDate, userProfile

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Troubleshooting

Common Issues and Solutions

Issue	Cause	Solution
Getting more results than requested	High-speed extraction overshoots slightly	You are billed only for the number you requested, not the extra results delivered
Fewer results than expected	Listing has fewer reviews in the specified language/rating	Try removing language or rating filters, or check the listing directly
Missing data in output	Results stored in Apify dataset	Navigate to the "Storage" tab and select "Download the results" or "Open in a New Tab"
Empty results	Invalid or unsupported URL format	Ensure you're using a valid TripAdvisor listing URL (Hotel_Review, Restaurant_Review, or Attraction_Review)
No sub-ratings in output	Not all reviewers provide sub-ratings	Sub-category ratings are optional on TripAdvisor — some reviews only include an overall rating

Performance Tips

• Start small: Test with maxItems: 10 (Demo Mode) to validate your setup before scaling
• Use date filtering: Set the since parameter to avoid re-extracting old reviews on scheduled runs
• Filter strategically: Combine languages and ratings to extract exactly the subset you need
• Use page offset: Set startPage to resume extraction from where a previous run left off
• Flatten nested data: Use the customMapFunction to produce flat, analysis-ready output

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Frequently Asked Questions

What TripAdvisor review data can I extract?

Extract review text, titles, overall ratings (1-5), sub-category ratings (Value, Rooms, Location, Cleanliness, Service, Sleep Quality), trip details (stay date, trip type), owner/management responses, reviewer profiles (username, hometown, contribution count, verified status), review photos, helpful votes, review language, and direct URLs — all in structured JSON or CSV format.

Can I export TripAdvisor reviews to CSV?

Yes. Download TripAdvisor reviews directly from Apify Console in JSON, CSV, or Excel format. Use the customMapFunction to flatten nested objects (like sub-ratings and reviewer profiles) into a single-row-per-review format ideal for spreadsheets and databases.

Can I filter reviews by language?

Yes. Use the languages parameter with an array of language codes (e.g., ["en", "fr", "de"]). This is particularly useful for analyzing reviews from specific source markets or preparing language-specific training datasets.

Can I filter reviews by star rating?

Yes. Use the ratings parameter to extract only reviews with a specific star rating (e.g., "1" for 1-star reviews only). This is ideal for negative review monitoring or targeted sentiment analysis.

What are the sub-category ratings?

TripAdvisor allows reviewers to provide additional quality ratings beyond the overall score. For hotels, these typically include Value, Rooms, Location, Cleanliness, Service, and Sleep Quality — each rated 1-5. These sub-ratings are returned in the additionalRatings array. Not all reviewers provide them, but when present, they offer granular quality insights unavailable from the overall rating alone.

Does this extract owner/management responses?

Yes. The ownerResponse field contains the full management reply to a review, when one exists. This is valuable for reputation management analysis — tracking response rates, response tone, and correlation between management engagement and subsequent review sentiment.

What trip types are available?

The tripInfo.tripType field contains the reviewer's self-reported trip type. Common values include: BUSINESS, COUPLES, FAMILY, FRIENDS, and SOLO. This enables segmentation of reviews by traveler demographic.

Can I scrape reviews from hotels, restaurants, AND attractions?

Yes. This scraper works with any TripAdvisor listing URL — hotels (Hotel_Review), restaurants (Restaurant_Review), and attractions (Attraction_Review). The location.placeType field in the output identifies the category.

Can I use Python to scrape TripAdvisor reviews?

Yes. Full Python support via the Apify Client library. See the Quick Start Guide for Python integration with client.actor("thewolves/tripadvisor-reviews-scraper").

Can AI agents use this scraper?

Yes. Through Apify's Model Context Protocol (MCP) server, AI agents built with Claude Desktop, VS Code, or any MCP-compatible framework can call this scraper as a tool. This enables automated hospitality monitoring, competitive analysis, and multi-step research pipelines. See AI Agent Integration via MCP for setup details.

How fast is the extraction?

100-200 reviews per second, depending on the listing and TripAdvisor's response times. This scraper is optimized for maximum throughput with internal proxy management — you don't need to configure any proxies.

Do I need to set up proxies?

No. This scraper handles all proxy rotation internally. You don't need to configure, purchase, or manage any proxy infrastructure.

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Contact

Built by The Wolves — a pack of data scientists with over a decade of experience building scrapers and APIs. We build them powerful, we build them fast, and we price them fair.

For questions, feature requests, or support:

• Discord: Reach out to Tommy the Wolf — always available

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Ready to extract TripAdvisor review data at scale? At $0.50 per 1,000 reviews with 100-200 reviews/second, sub-category quality ratings, trip metadata, owner responses, and full reviewer profiles, this TripAdvisor Reviews Scraper API by The Wolves delivers the deepest hospitality review data on the market. Start scraping today.