Hotel Review Sentiment Scraper with AI Analysis (TripAdvisor + Booking.com + Google Maps)

The most comprehensive hotel review scraper and hotel sentiment analysis tool on Apify. Scrape reviews from TripAdvisor, Booking.com, and Google Maps in a single run, then get instant AI review analysis with cross-platform reputation insights. Built for hotel reputation monitoring, this Actor combines multi-platform data collection with intelligent sentiment scoring — so you can stop guessing and start managing your online reputation with data.

What makes it different

Most hotel scrapers pull reviews from one platform. This Actor scrapes three major travel platforms simultaneously and then runs AI-powered analysis across all reviews combined:

• 3 platforms, 1 run — TripAdvisor, Booking.com, and Google Maps reviews collected together. No need to run separate scrapers and merge data manually.
• Cross-platform AI analysis — Sentiment scores are calculated per platform AND overall, so you can see exactly where your reputation is strong or weak.
• Platform consistency detection — The unique platformConsistency field tells you whether your reviews tell the same story across platforms, or if there are red flags like rating divergence that need attention.

Who is it for

• Hotel managers & GMs — Monitor your property's online reputation across all major booking platforms from a single dashboard-ready dataset.
• Hospitality brands & chains — Track sentiment trends across your portfolio. Identify properties that need attention before small issues become PR problems.
• Travel agencies — Vet hotel partners with real review data. Recommend properties backed by cross-platform sentiment analysis, not just star ratings.
• OTA analysts — Compare review sentiment across platforms to detect rating manipulation, review gating, or inconsistent guest experiences.
• Reputation management firms — Deliver data-driven reports to hospitality clients with executive summaries, risk scores, and trend analysis included automatically.

Input fields

Field	Type	Description	Default
hotelName	String	Name of the hotel to analyze. Used for labeling results.	—
hotelUrls	Array	Direct URLs to the hotel on TripAdvisor, Google Maps, and/or Booking.com. One URL per platform.	[]
platforms	Array	Which platforms to scrape: tripadvisor, googlemaps, booking.	All three
maxReviewsPerPlatform	Integer	Maximum reviews to collect from each platform (1–500).	50
language	String	Filter reviews by language code (e.g. en, es, de). Leave empty for all.	All languages
dateRange	Enum	Time window for reviews: 30days, 90days, 365days, or all.	all

Output fields

Each run produces a dataset item with the hotel's reviews and a complete AI-generated sentiment analysis:

Review data (per review)

Field	Description
reviewText	Full review text as written by the guest
rating	Star rating (1–5 scale)
date	Review date (ISO format)
platform	Source platform: tripadvisor, googlemaps, or booking
authorName	Reviewer's display name
isVerified	Whether the review is marked as verified
companyResponse	Hotel management's response to the review (if any)

AI sentiment analysis (per hotel, across all platforms)

Field	Type	Description
overallSentimentScore	Number (1–10)	Overall sentiment across all platforms. 10 = most positive.
sentimentByPlatform	Object	Individual sentiment score for each platform scraped (e.g. {"tripadvisor": 7, "booking": 8}).
topComplaints	Array	Up to 5 most common complaint themes identified across all reviews.
topPraises	Array	Up to 5 most common praise themes identified across all reviews.
trendDirection	String	improving, declining, or stable — based on chronological sentiment shift.
reputationRisk	Number (1–10)	Risk score indicating how vulnerable the hotel's reputation is. 10 = highest risk.
responseRateScore	Number (1–10)	How actively the hotel responds to guest reviews. 10 = excellent engagement.
executiveSummary	String	2–3 sentence summary of the hotel's overall reputation, ready for reports.
platformConsistency	String	Assessment of whether reviews are consistent across platforms or show significant divergence.

Pricing

$0.06 per review analyzed — pay only for results, not for runtime.

Reviews	Cost
50 reviews (default per platform)	$3.00
150 reviews (50 x 3 platforms)	$9.00
500 reviews	$30.00

The AI analysis is included in the per-review price. No hidden charges, no platform fees.

How it works

1. Provide hotel URLs — Paste the hotel's TripAdvisor, Booking.com, and/or Google Maps page URLs. Select which platforms to scrape.
2. Reviews are collected — The Actor visits each platform, navigates to the reviews section, and extracts review text, ratings, dates, author info, and management responses.
3. AI analyzes the reviews — All collected reviews are sent to an AI engine that performs cross-platform sentiment analysis, identifies complaint and praise patterns, calculates risk scores, and generates an executive summary.
4. Get structured results — The output is a clean JSON dataset with every review plus the full AI enrichment object, ready for dashboards, reports, or further processing.

Supported platforms

Platform	What's extracted	URL format
TripAdvisor	Review text, rating, date, author, management response	tripadvisor.com/Hotel_Review-*
Booking.com	Positive/negative text, rating (converted to 1–5), date, author, verified status, management response	booking.com/hotel/*
Google Maps	Review text, rating, relative date (parsed), author, management response	google.com/maps/place/*

Tips for best results

• Always provide direct URLs — The scraper works best with direct hotel page URLs rather than search result pages. Copy the URL from your browser when you're on the hotel's review page.
• Start with a small test — Run with maxReviewsPerPlatform: 5 first to verify the URLs work and the output format meets your needs.
• Use date ranges for monitoring — Set dateRange to 30days or 90days for regular reputation monitoring. Use all for initial audits.
• Compare platforms — The sentimentByPlatform and platformConsistency fields are most valuable when you scrape at least 2 platforms. A big score gap between platforms often reveals actionable insights.
• Feed results into dashboards — The structured JSON output is designed for direct import into BI tools, Google Sheets, or custom dashboards.
• Schedule regular runs — Set up a weekly or monthly schedule to track trendDirection over time and catch reputation shifts early.

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Built by data-runner.dev