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Tripadvisor Reviews Scraper

What travellers said,
and what trip they were on.

Give it a Tripadvisor hotel, restaurant or attraction review URL and get that listing's reviews back as rows: the author, the rating, the date, the headline, the full review, the kind of trip it was, and the language it was written in. The trip type and the language are their own columns, so a business traveller's complaint and a family's are never averaged into the same sentence.

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one-time 500 free rows$0.002 per row after8 columnsCSV · XLSX · JSON
How it works

A list of listings in,
their reviews out.

The input is the place, not the review - you name the hotels, restaurants or attractions you want feedback on and the job collects what their visitors wrote.

  1. STEP 1Sign in to the platform.
  2. STEP 2Open the Tripadvisor Reviews Scraper.
  3. STEP 3Paste Tripadvisor restaurant, hotel or attraction review URLs, one per line - or upload a CSV, XLSX, TXT or Parquet file.
  4. STEP 4Set a limit per query, or set it to zero to take everything.
  5. STEP 5Choose whether to keep the listing's default language or take all of them, and whether to start from the oldest review.
  6. STEP 6Click Get Data.

One row per review, each tagged with the query it came from - so a run across fifty listings still reconciles back to your input list.

Why teams use it

The trip type is
the whole point.

Who was travelling, in its own column

Tripadvisor asks reviewers what kind of trip they were on, and that answer arrives as trip_type rather than buried in prose. A hotel that works for one kind of traveller and fails another shows up as exactly that, instead of as a rating that sits stubbornly in the middle.

The DataDome wall is our problem

Tripadvisor sits behind DataDome, which is why a hand-rolled script on an office IP comes back with a challenge page instead of data. Residential routing, rate limiting and IP rotation happen on our side; from yours it is a form or an API call, and there is no proxy pool to run.

All languages, and a column that says which

Keep the listing's default language or take every language it has - and either way each row carries the language it was written in, so a multilingual pull stays sortable instead of turning into one undifferentiated pile of text.

What you get back

Eight columns,
one row per review.

Each row carries the review as Tripadvisor displays it: who wrote it, the rating, the date, the headline, the full text, the kind of trip it was and the language it is in.

Review text comes back verbatim, with no preprocessing applied, so downstream sentiment work reads the traveller's own wording rather than ours. The column list below is the header row of real run exports rather than a specification - and it is worth reading the note under it, because those runs are the reason this page describes the schema and not the value formats.

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Data dictionary

Eight columns,
and what each one holds.

Taken from the header row of real run exports, identical across every one of them and identical to the column list the platform ships. Read the note underneath before you write a parser: these names are verified, and the value formats are not.

query
The review URL you submitted, repeated on every row that came from it - so a run across many listings still reconciles against your input list.
author
Who wrote the review, as Tripadvisor publishes it. The site shows a member name rather than a full identity.
rating
The score the traveller gave the place.
date
When the review was posted.
title
The review headline.
review
The free-text review body, verbatim and unprocessed.
trip_type
The kind of trip the reviewer said they were on. This is the column that makes the dataset worth pulling - it is what tells two identical-looking ratings apart when the travellers behind them were nothing alike.
language
The language the review was written in. Useful when the run was set to take all languages rather than the listing’s default, and the last column in the file.

One thing to know before you write code against this. Tripadvisor is behind DataDome, and the free demo pool does not get past its challenge. An export header row still names its columns, which is why the eight column names above are trustworthy. What we have not seen is the shape of the values, so this page deliberately claims no date pattern, no rating scale, no list of trip types and no format for the language column - whether that is a code or a name. Pull a free run and read the first rows before you assume any of them.

Run controls

Set on the job,
not in the spreadsheet.

A per-query limit decides what a run costs and how much lands in the file. The language setting and the direction are chosen alongside it, before the job starts.

Hotel review URL Restaurant review URL Attraction review URL Limit per query Zero for everything Default language All languages From the oldest Paste one per line CSV upload XLSX upload TXT upload Parquet upload
Common workflows

Three jobs people
most often run here.

A few examples of how teams use travel review data to answer a question they actually have.

Hospitality

Find out which guest you are failing

Pull your own property and split the reviews by trip type. A hotel that scores well with couples and badly with business travellers has a specific problem - a desk, a kettle, the wifi - that an overall rating will never name for you.

Ops · Hotels
Competitive

Benchmark the street, not the star rating

Export every restaurant or hotel in an area and compare what visitors actually wrote side by side. Useful before opening, and useful afterwards for knowing who you are really losing bookings to.

Strategy
Research

Read the complaints as a corpus

Verbatim reviews across dozens of listings make recurring failures visible as repeated language - noisy, dated, overpriced - instead of as a rating that drifted a tenth of a point. Take all languages and the language column keeps the corpus sortable.

Research
Get started
Pricing

Pay only for the reviews
you actually pull.

No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.

Free tier

500 free rows - $0

Every new account, one-time. No credit card required. Per-query limits, the language setting, file upload and every export format included.

$0 forever
Pay-as-you-go

$0.002 per row, after the free tier

Roughly $2 per 1,000 reviews. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.

Most popular
Volume

Custom · high volume

Volume pricing, dedicated workers and an SLA for continuous monitoring or very large historical pulls. Tell us your numbers and we will quote.

Talk to us
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10% off your first paid run.Use code LIVESCRAPER10 at checkout.
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Pairs well with

The same stay,
on a different site.

Airbnb Reviews Scraper

The other half of where people sleep. Running both across a destination shows whether a complaint is about the property or about the category.

Explore

Google Maps Reviews Scraper

The same hotels and restaurants as the locals see them, rather than as travellers do. Same places, a different audience writing.

Explore
The legal bit

Is it legal to scrape
Tripadvisor reviews?

Short answer: yes for the public review content - and this export carries nothing about the booking behind it.

Reviews on Tripadvisor are published to be read. The rating, the headline, the review text, the date, the trip type and the language are shown to anyone who opens the listing, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login, a booking or a paywall.

The author field carries what the site itself publishes, which is a member name rather than a full identity - there is no email and no address in this output. Nothing about the stay behind a review is collected either: not what was paid, not the dates, not the room. If you are processing the review text in the EU, the usual rules still apply to what you do with it downstream.

Tripadvisor's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the platform, check it. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.

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livescraper.app · principles
Public review content only✓
No logins, no accounts touched✓
No booking details in the export✓
GDPR-aligned by default✓
Exports auto-delete (30 days)✓
Check Tripadvisor's own terms before scaling.
Common questions

Things people
ask before signing up.

The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.

How do I scrape Tripadvisor reviews?+
Using the Tripadvisor Reviews Scraper:
  1. Sign in to the platform.
  2. Open the Tripadvisor Reviews Scraper.
  3. Paste Tripadvisor restaurant, hotel or attraction review URLs, one per line - or upload a CSV, XLSX, TXT or Parquet file.
  4. Set a limit per query, or set it to zero to take everything.
  5. Choose whether to keep the listing's default language or take all of them, and whether to start from the oldest review.
  6. Click Get Data.
What kind of Tripadvisor URL do I paste in?+
A review URL for a restaurant, a hotel or an attraction, one per line. The input is the listing rather than a search term, so paste the page whose reviews you want. If your list is already in a file, upload it as CSV, XLSX, TXT or Parquet instead of pasting.
What comes back for each review?+
Eight columns: the query you submitted, the author, the rating, the date, the headline, the full review text, the trip type the reviewer selected, and the language the review was written in.
What makes the trip type column useful?+
It is what tells two identical-looking ratings apart. Tripadvisor asks reviewers what kind of trip they were on, and having that answer as its own column lets you split a listing by traveller instead of reading one averaged score - a hotel can work for one kind of guest and fail another and still land in the middle.
Can I get reviews in every language?+
Yes. The language setting is a choice between the listing's default and all of them, and it is made before the run. Whichever you pick, every row carries the language it was written in as its own column, so a multilingual pull stays sortable.
Can I control how many reviews come back, and in what order?+
Yes, and both are set before the run rather than afterwards. Set a limit per query so credits go on the slice you actually want, or set the limit to zero to take everything a listing has. Reviews arrive most recent first unless you tick the From (oldest) option, which starts at the other end.
Will I get blocked or see a challenge page?+
No. Tripadvisor is behind DataDome, which is exactly why a hand-rolled script on an office or datacenter IP gets a challenge rather than data. Residential routing, rate limiting and IP rotation are handled on our side - from your end it is a form or an API call, and you never manage a proxy pool.
How much does it cost?+
The first 500 rows are free and one-time, with no credit card. After that it is $0.002 per row - about $2 per 1,000 reviews - which is the same flat rate as every other scraper on the platform. The estimator shows the cost of a run before it starts.

Your first 500 reviews,
on the house.

500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.

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Scrape Tripadvisor hotel and restaurant reviews at scale

Livescraper's Tripadvisor Reviews Scraper turns a list of listings into review data. You submit Tripadvisor restaurant, hotel or attraction review URLs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - cap the rows per query if you want to, choose whether to take every language, and download what travellers wrote as a clean CSV, Excel or JSON file.

Each row carries the review as Tripadvisor displays it: the author, the rating, the date, the headline, the full review text, the kind of trip the reviewer said they were on, and the language it was written in. Those last two are what make this dataset different from most review exports - a business stay and a family holiday at the same hotel stop being the same data point, and a multilingual pull stays sortable instead of arriving as one undifferentiated pile of text.

Hotels pull their own property and split the reviews by trip type to find the specific thing they are failing at. Strategy teams export every listing in an area and benchmark what visitors actually wrote rather than the star rating. Researchers read verbatim reviews across dozens of listings, where recurring failures appear as repeated language instead of as a rating that drifted a tenth of a point.

One practical note up front: Tripadvisor sits behind DataDome, which is why a hand-rolled script tends to return a challenge page rather than data. Residential routing, rate limiting and IP rotation are handled on our side, so from your end it is a form or an API call and there is no proxy pool to run. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.