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🌍 Tripadvisor Reviews Scraper — Most Comprehensive

Scrape TripAdvisor reviews for any hotel, restaurant, or attraction — full review text, bubble rating, sub-ratings (Value / Service / Food / Atmosphere / Rooms…), date of stay, trip type, helpful votes, photos, language & translation flag, and the complete owner/management response — at $0.30 per 1,000 reviews. Then schedule it as a new-review monitor that pings Slack, email, or a webhook the moment a fresh review lands.

Why this one? (honest comparison)

Competitor capabilities/prices as listed on their store pages, June 2026 — verify current state there.

What does Tripadvisor Reviews Scraper do?

It extracts every review of any TripAdvisor place — hotels (Hotel_Review-… URLs), restaurants (Restaurant_Review-…), attractions (Attraction_Review-…) — and returns clean, structured rows you can export to JSON, CSV, Excel, or pull via API:

• ⭐ Rating & sub-ratings — overall bubble rating plus per-category scores (Value, Service, Food, Atmosphere, Rooms, Cleanliness, Sleep Quality, Location…).
• 📝 Full review text + title — not truncated, HTML-clean.
• 📅 All three dates — published date, written date, and the actual date of stay/visit.
• 🧳 Trip type — FAMILY / COUPLES / SOLO / BUSINESS / FRIENDS.
• 💬 Owner & management responses — full response text, response date, and the responder's role (e.g. "Guest Services / Front Office").
• 👍 Helpful votes, 📷 review photos (full-size CDN URLs), 🌐 language + translated-or-not flag.
• 🏨 Place context on every row — place name, ID, type, overall rating, total review count.
• 🗃️ raw sub-object — the complete source record, so a TripAdvisor field we didn't flatten is still yours.
• 🔔 Monitor mode — run it on a schedule and get only the reviews that are new since last run, with Slack/email/webhook alerts.

Who is it for?

• 🏨 Hotel & restaurant operators tracking their own (and competitors') reputation — sub-ratings show what slipped, owner-response fields show who's answering.
• 📊 Hospitality analysts & revenue managers building review datasets across portfolios.
• 🤖 AI/NLP teams that need full-text review corpora with ratings and dates for sentiment models.
• 🛎️ Agencies running reputation dashboards — monitor mode + webhooks pipes new reviews straight into your stack.
• 🔎 Travelers & researchers pulling complaint patterns (minRating:1, maxRating:2) before booking.

How to use it (step by step)

1. Click Try for free.
2. Paste one or more TripAdvisor place URLs (the page you'd send a friend — hotel, restaurant, or attraction).
3. (Optional) set maxReviewsPerPlace (default 50; 0 = every review), date range, keyword, rating band, language.
4. Click Start, open the Dataset tab, export.
5. (Optional) turn on monitorMode, attach an Apify Schedule, add a Slack/webhook/email channel — get pinged on every new review.

Quick start

{
"startUrls":["https://www.tripadvisor.com/Hotel_Review-g60763-d93589-Reviews-The_Michelangelo_Hotel-New_York_City_New_York.html"],
"maxReviewsPerPlace":50
}

Pull every review since a date (early-stops = you stop paying)

{
"startUrls":["https://www.tripadvisor.com/Restaurant_Review-g31979-d477015-Reviews-R_Landry_s_New_Orleans_Cafe-Van_Buren_Arkansas.html"],
"maxReviewsPerPlace":0,
"dateFrom":"2026-01-01"
}

Reputation monitor (the recurring play)

{
"startUrls":["https://www.tripadvisor.com/Hotel_Review-g45963-d91703-Reviews-Bellagio-Las_Vegas_Nevada.html"],
"monitorMode":true,
"slackWebhookUrl":"https://hooks.slack.com/services/…"
}

Output example (truncated)

{
"review_id":"935650210",
"review_url":"https://www.tripadvisor.com/ShowUserReviews-g31979-d477015-r935650210-R_Landry_s_New_Orleans_Cafe-Van_Buren_Arkansas.html",
"place_id":"477015",
"place_name":"R. Landry's New Orleans Cafe",
"place_type":"EATERY",
"place_rating":4.5,
"place_review_count":146,
"title":"Top Notch Cajun Cuisine!",
"text":"My husband and I got the cajun trio tonight. We didn't realize it was a drive through establishment…",
"rating":5,
"subratings":{"Value":4,"Service":5,"Food":5,"Atmosphere":2},
"published_date":"2024-01-27",
"stay_date":"2024-01-31",
"trip_type":"FAMILY",
"helpful_votes":0,
"language":"en",
"is_translated":false,
"owner_response":{
"text":"Every member of our team is smiling reading this…",
"published_date":"2026-06-10",
"responder":"Hotel Manager",
"responder_role":"Guest Services / Front Office"
},
"photos":["https://dynamic-media-cdn.tripadvisor.com/media/photo-o/26/d8/29/ce/caption.jpg?w=1200&h=-1&s=1"],
"reviewer_hometown":"Van Buren, Arkansas",
"reviewer_contributions":685
}

With stripPersonalData: false you additionally get reviewer_name, reviewer_username, reviewer_profile_url, reviewer_avatar.

Input

How much does it cost?

Pay-per-event — you pay for what you pull, no subscription:

That's $0.30 per 1,000 reviews — the same 1,000 reviews cost $5.00 on the leading per-result competitor. A daily monitor on one hotel costs ~$0.60/month plus a fraction of a cent per new review. (Plus standard Apify platform usage; final prices are set on the actor's pricing page.)

Is it legal? (read this)

• Review text, ratings, and owner responses are publicly visible without any login. This actor only reads public pages — no login, no API key, no paywall circumvention.
• Reviewer identities are personal data. That's why stripPersonalData defaults to ON, removing names, usernames, profile links and avatars. Only disable it if you have a lawful basis (GDPR/CCPA) to process reviewer identities, and expect to honor deletion requests.
• Scraping may conflict with TripAdvisor's Terms of Service. Republishing TripAdvisor content commercially is restricted by their terms. You are responsible for your use of the data — for most users that means internal analysis, not republication.

FAQ

Which TripAdvisor pages does it support? Hotels (Hotel_Review-…), restaurants (Restaurant_Review-…), attractions (Attraction_Review-…), and vacation rentals. Paste the normal page URL; redirects to the canonical page are followed automatically.

Do I need a TripAdvisor account or API key? No. The data is read from the public page itself.

How many reviews can I get per place? All of them — set maxReviewsPerPlace: 0. Hotels/attractions paginate 10 per page, restaurants 15; the actor walks the pages until your cap, your date floor, or the end.

Does it return owner/management responses? Yes — full text, response date, responder display name and role, on every review that has one.

Does it return sub-ratings like Value, Service, Rooms? Yes, as a subratings object whenever the reviewer filled them in.

Can I get only negative reviews? Yes — minRating: 1, maxRating: 2 returns only 1–2-bubble reviews.

Can I get only new reviews on a schedule? Yes — monitorMode: true + an Apify Schedule. State is kept in a named key-value store, so each run emits only reviews it hasn't seen before, and can alert Slack/email/webhook per review.

What about non-English reviews and translations? Each review carries language, original_language, and is_translated. TripAdvisor's US pages serve mostly English; use the language filter to keep a single language.

Why residential proxies? TripAdvisor is protected by DataDome. The actor's browser-fingerprint HTTP client plus residential rotation gets through reliably; datacenter IPs get blocked much more often. Failed pages are retried with a fresh session and never kill the run.

How fast is it? About 1–2 seconds per page of 10–15 reviews — 1,000 reviews in a few minutes.

Is the reviewer's name included? Only if you set stripPersonalData: false (see the legal section). By default the output is anonymized but keeps hometown and contribution count for weighting.

Can I export to Excel / CSV / my backend? Yes — Dataset tab exports JSON/CSV/Excel/HTML/RSS, or use the Apify API / webhooks to pipe rows anywhere.

A field I need isn't flattened — am I stuck? No. Every row carries the complete raw source object from TripAdvisor's own data layer.

You might also like

• 🛏️ Hotel price & availability scrapers
• 🍽️ Restaurant directory & menu scrapers
• ⭐ Google Maps / Yelp review scrapers

Feedback

Found a missing field or want a new filter? Open an issue on the actor — fast fixes and feature requests welcome.