TripAdvisor Reviews Scraper
Get actionable business data, find customers, and track competitors with our TripAdvisor Reviews dataset.
- Full review history per venue — not just the most-recent page
- Reviewer profile, traveller type, helpful votes and photos per review
- Management responses captured alongside the original review
How we extract tripadvisor reviews data
The full pipeline from your brief to the final delivered file — no black box, no surprises.
1. Lock the target venues
Provide the venues you want reviews for — by TripAdvisor URL, by venue ID (e.g. g255060-d258751), or as a category sweep ('all 5★ hotels in Sydney'). For broader scopes we paginate through TripAdvisor's listing pages first, then attach the review pull. Projected review count per venue is confirmed before any scraping starts.
2. Walk the entire review timeline
Our pipeline scrolls through TripAdvisor's review feed all the way back to the very first review per venue, not the 5–10 reviews you see by default on a listing page. Sort orders captured: most recent, highest rated, lowest rated, traveller-type filtered — so you get a complete archive regardless of how TripAdvisor ranks them today.
3. Extract long-tail review fields
Each review row carries: full untruncated text (we expand 'Read more' on every multi-paragraph review), star rating, posted date, trip date, language, traveller type (couples / family / solo / business / friends), reviewer name + profile URL + total reviews authored + Local Expert level, helpful-vote count, photos uploaded with the review, and TripAdvisor's stable review_id (for deduplication and citation).
4. Capture management responses
Where the venue management has replied to a review, we extract the response text + the response timestamp on the same row. Handy for measuring response rate, response time and tone of brand engagement — useful for hotel chains tracking property-level reputation behaviour.
5. Deduplicate by review_id
TripAdvisor sometimes serves the same review across different sort orders. We collapse duplicates by stable review_id so each traveller voice appears exactly once in the final file.
6. Optional: sentiment + topic tagging
Add per-row sentiment score (positive/neutral/negative + confidence), topic tags (cleanliness, staff, food, value, location, etc.) and entity extraction. Powered by an LLM pass over each review — flat fee per 1,000 rows.
7. Deliver as CSV / XLSX / JSON
Default delivery is a single ZIP with CSV (UTF-8), XLSX (with a schema sheet) and a README. JSON or NDJSON for ML pipelines, direct push to BigQuery / Snowflake / Postgres available via our automation service.
8. Optional: scheduled incremental pulls
Re-run weekly or monthly and we deliver only the new + changed reviews since the last run. Critical for live reputation dashboards — you stay in sync without re-importing the full archive every time.
Every field captured per business
38 data points per record, grouped into 7 categories. Each is a real column in your delivered CSV/XLSX.
Venue identity
Stable identifiers tying every review back to the TripAdvisor venue it belongs to.
venue_idg255060-d258751TripAdvisor's stable per-venue identifiervenue_namePark Hyatt Sydneyvenue_typeHOTELHOTEL / RESTAURANT / ATTRACTION / VACATION_RENTAL / TOURvenue_categoryHotelreview_id898342176Stable per-review identifier — primary key for dedupreview_urlhttps://www.tripadvisor.com/ShowUserReviews-g255060-d258751-r898342176.htmlDirect deep-link to the review
Review content
The review itself — text, score, language, when it was written and when the trip happened.
rating51–5 star scorereview_textBest harbour-view room in Sydney — staff went above and beyond.Full text, untruncated, no character limitreview_titleBest stay we've had in AustraliaHeadline TripAdvisor asks reviewers to givereview_date2026-03-22ISO date the review was postedtrip_date2026-03ISO month the reviewer says they visitedreview_languageenISO 639-1 language code, auto-detectedreview_length247Character count — handy for filtering thin reviews
Reviewer profile
Who left the review — their public TripAdvisor profile, history and credibility signals.
reviewer_nameSarah J.reviewer_idUID5b1dEStable contributor identifierreviewer_profile_urlhttps://www.tripadvisor.com/Profile/sarahj147reviewer_avatar_urlhttps://media-cdn.tripadvisor.com/media/photo-l/...avatar.jpgreviewer_total_reviews147Lifetime reviews authored across TripAdvisorreviewer_total_photos82reviewer_total_helpful_votes412Cumulative helpful-vote count on their reviewsreviewer_local_expert_level5TripAdvisor 'Local Expert' badge level (1–6)reviewer_home_locationSydney, AustraliaWhere the reviewer publicly says they're from
Traveller context
Who they were travelling with + venue-specific context (room tip flag for hotels).
traveller_typeCouplesCouples / Family / Solo / Business / Friendsis_room_tipfalseHotels only — flagged when the review is tagged as a room tipservice_typeLunchRestaurants only — Breakfast / Lunch / Dinner / BrunchsubcategorySpa HotelVenue subtype the reviewer was rating
Management response
How the venue engaged with the review — text, timing, and response-rate signal.
has_management_responsetrueBoolean — quick filter for engaged vs unanswered reviewsmanagement_responseThank you Sarah, we're so glad you enjoyed your stay.management_response_date2026-03-23management_response_lag_days1Days between the review and the management responseresponder_roleGeneral ManagerTitle the management responder gives themselves on TripAdvisor
Engagement
Helpful-vote count and uploaded photos — proxy for which reviews other travellers find most useful.
helpful_votes412Number of users who marked the review as helpfulphoto_count3Number of photos the reviewer uploaded with this reviewphoto_urlshttps://media-cdn...photo-1.jpg ; https://media-cdn...photo-2.jpgSemicolon-separated full-size photo URLs
Optional add-ons
Sentiment + topic tagging available as a paid LLM pass over the review text.
sentimentpositivepositive / neutral / negative — LLM-generatedsentiment_score0.940.0–1.0 confidence score from the modeltopicsharbour view, staff, room comfort, breakfastComma-separated topic tags extracted from the reviewentitiesSarah J. → reviewer; Park Hyatt Sydney → venueEntity extraction (people, locations, products, services)
Need a custom field that's not listed? Mention it in the quote request and we'll confirm whether the source page exposes it.
Download a sample of our TripAdvisor Reviews dataset
Find new clients and close more deals with the world's best business leads provider. Grab a 25-row sample CSV — same schema as the paid extracts, real records, no card required.
- · 25 real records with the full schema
- · UTF-8 CSV — opens in Excel, Sheets, Airtable
- · Documented fields and data types
- · No credit card · sent to your inbox
Why choose us for your business
The same operating principles every project, regardless of scope: flexible, secure, scalable.
Flexible
Custom-built per project. Tell us the source, the fields, the volume, the cadence — we deliver to that exact spec.
Secure
Stripe-secured checkout, GDPR-aware delivery, signed download URLs that expire. Your data and your buyers' privacy are protected end-to-end.
Scalable
From a single suburb pull to a daily multi-million-record pipeline. Same infrastructure, scaled to whatever volume you need.
How B2B Connection helps businesses with tripadvisor reviews
TripAdvisor's review feed is one of the richest sources of traveller voice on the open web — but the platform truncates it heavily on listing pages and there's no public API. Our scraper walks the full review history per venue (hotels, restaurants, attractions, vacation rentals, tours), capturing every review TripAdvisor has ever published — not just the most-recent page.
Each review row carries: full untruncated text, star rating, posted date, language, reviewer name + profile URL + total reviews authored + Local Expert badge, helpful-vote count, photos uploaded with the review, traveller type (couples / family / solo / business / friends), trip date, room tip flag for hotels, and management responses with response timestamps.
What's included
- Complete review history per venue (not page-1 truncation)
- Full untruncated review text — every multi-paragraph review captured in full
- Reviewer profile: name, total reviews, total photos, Local Expert level
- Traveller-type segmentation (couples, family, solo, business, friends)
- Trip date + room tip flag for hotel reviews
- Management responses with response date + lag-in-days computed
- Photo URLs uploaded with each review
- Sentiment + topic tagging available as a paid LLM add-on
Common use cases
- Hotel reputation monitoring — daily / weekly review tracking
- Restaurant competitive benchmarking — sentiment trends per dish or service
- Attraction analysis for tour operators and OTAs
- Mining traveller voice for hospitality marketing copy
- Sentiment dashboards across multiple TripAdvisor properties
- LLM training corpora — structured traveller voice across millions of venues
Why enterprises use B2B Connection
Six things our buyers consistently mention when they renew or refer us.
1,500+ clients
From SaaS vendors to global recruiters and hospitality groups, across Australia, the US and Europe.
500M+ records scraped
180M phones, 100M+ emails, deduplicated and verified across our pipelines.
Stripe-secured checkout
Card data never touches our servers. Refunds processed inside Stripe's standard 5-business-day window.
GDPR-aware delivery
Optional PII stripping for EU-bound deliveries. Data retention defaults to 30 days post-handover.
Same-day quotes
Project briefs quoted within one business day. First sample within five.
Spam Act 2003 compliant
All B2B records sourced from publicly listed business pages — inferred-consent safe under Australian and US/UK rules.
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Ready to get a quote for tripadvisor reviews scraper?
Tell us your source, fields and timeline. We'll respond within one business day.
Frequently asked questions
How far back can you pull TripAdvisor reviews?
All the way to the very first review on the listing. TripAdvisor's UI defaults to the most-recent 10 but the underlying review feed is paginated infinitely — our scraper walks the entire history regardless of how old the listing is. For a venue with 6,000+ reviews going back 15 years, you get all of them.
Do I get the full review text or just an excerpt?
Full text, untruncated. We click 'Read more' on every multi-paragraph review so you get the complete content rather than the 200-character preview TripAdvisor shows by default.
Are management responses included?
Yes — when the venue management has replied to a review, you get both the response text, the response timestamp, the responder's title (e.g. 'General Manager'), and we compute response lag in days so you can analyse engagement velocity per property.
What's the traveller-type segmentation?
Every review row carries a traveller_type column — Couples / Family / Solo / Business / Friends — based on what the reviewer indicated during posting. Useful for segmenting sentiment by audience (e.g. families care about pool access, business travellers care about Wi-Fi).
Can you tag sentiment and topics?
Yes, as a paid add-on. We run each review through an LLM pass that tags sentiment (positive/neutral/negative + confidence), extracts topic tags (cleanliness, staff, food, value, location, etc.) and identifies named entities. Flat fee per 1,000 rows.
Which TripAdvisor venue types do you support?
All of them — hotels, restaurants, attractions, vacation rentals, tours, and things-to-do. The schema is the same; the venue_type column lets you filter by category. You can mix venue types in a single delivery.
Is it legal to scrape TripAdvisor reviews?
Yes — every field we capture is publicly visible on TripAdvisor's listing pages. We respect rate limits, never extract data from behind a login, and never include private user information (just the public reviewer profile that TripAdvisor shows to anyone). Output complies with the same public-data principles as our other scraping services.
What format do I get the data in?
Default is a ZIP containing CSV (UTF-8, header row), XLSX (with a second sheet documenting the schema) and a README. JSON / NDJSON available on request — recommended when you're piping the reviews into an ML or sentiment-analysis pipeline.
How quickly can you deliver?
1–3 business days for a one-shot pull from up to a few thousand venues, including the full review history per venue. Larger or scheduled extracts quoted on a per-volume basis. We share a sample within 24 hours so you can verify the schema before the full extract runs.
Can you run this on a schedule for ongoing reputation monitoring?
Yes. We re-run weekly or monthly and deliver only the new + changed reviews since the last run, so your sentiment dashboard or reputation monitoring tool stays in sync without re-importing the entire archive. This is part of our automation service.