Give it your Booking.com hotels once - a URL or just the slug - and it re-scans their reviews on a schedule you pick, from once a day to once every three months. Set the score at or below which a review counts as negative, give it an address, and the report comes to you. Eleven columns per review, including Booking's own split of what the guest liked and disliked.
The input is the property and the schedule - you name the hotels, say how often to look and what counts as a bad review, and the monitor does the rest.
One row per review, each tagged with the hotel it came from - so a watch list of fifty properties still reconciles back to your input.
Once a day, once a week, once every three weeks, once a month, or once every three months - chosen when you set the monitor up, and a week is what it starts on. A property in the middle of a refurbishment and one you check on out of habit do not need the same cadence.
Booking scores reviews out of ten, and you set the line: any review at or below your threshold is flagged negative, with three as the starting point. That turns a vague intention to watch reviews into a specific standing question, and it is the same number the report is built around.
An email address is required rather than optional, because the point of a monitor is that nobody has to remember to check it. The scan runs on your schedule through the proxy pool and a report arrives; your real IP is never used for any of it.
Each row is one guest review as Booking publishes it - who wrote it and where they are from, the score, what they liked and what they did not, and the details of the stay behind it.
Booking does not ask for one block of review text. It asks what the guest liked and what they disliked, and this export keeps those as two separate columns rather than gluing them together - which is what lets you read complaints on their own without a sentiment model guessing where the praise stopped. Read the note under the table before you write a parser: the names are solid, and this page deliberately makes no promise about what arrives in them.
The platform's own column list, in the order the download writes them. Descriptions say what each field is for; they claim nothing about its format or whether it arrives filled - see the note underneath.
Read this dictionary as a list of names and purposes, not a promise about contents. The eleven names and their order come from the platform's published column list and are confirmed by the header row of archived runs carrying this shape. What this page will not tell you is what arrives in them, and the reason is specific: the Booking Reviews Scraper uses a byte-identical column list, so a run stored under this shape cannot be attributed to the monitor rather than to that scraper - which means no value in it may be presented here as this service's output. One formatting note does survive that, because it is a trap worth flagging: the liked text arrives padded with whitespace and can contain line breaks, so trim it. Beyond that, set a monitor on one hotel, let the first scan land, and read your own first rows before you build against any column.
The hotels, how often to look, what counts as negative, and where the report goes. There is nothing to run afterwards.
A few examples of how teams use scheduled review monitoring to answer a question that does not go away.
Set the threshold where your own standard sits and let the daily scan do the watching. One low score is noise; three in a fortnight about the same thing is a problem, and the point of a monitor is that you find out while it is still fixable.
A watch list is not limited to properties you own. Put the hotels you compete with on a weekly cadence and the same report tells you what their guests are complaining about - which is the cheapest product research available to a hotel.
Because liked and disliked are their own columns, a quarterly export gives you a clean corpus of criticism without a model having to guess where the praise ended. Group on the disliked column and the recurring words are the roadmap.
No subscription, no minimum, no recurring bill - a monitor costs what its scans cost. Your first 500 rows are on us, then pay-as-you-go at the same flat rate as every other service here.
Every new account, one-time. No credit card required. Every schedule, the negative threshold, email reports and every export format included.
Roughly $2 per 1,000 reviews. The pre-flight estimator shows the row count and credit cost before a scan starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for large watch lists or continuous monitoring across a portfolio. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.The same standing watch pointed at Tripadvisor. Guests who book on one site often review on the other, so running both is how a portfolio gets covered rather than sampled.
For the business behind the property rather than the property itself. Useful when the complaint is about booking, billing or support rather than the room.
Short answer: yes for the public review content - and this export carries nothing about the booking behind it.
Guest reviews on Booking.com are published to be read. The score, what the guest liked and disliked, the dates and the room type are shown to anyone who opens the property page, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login, a reservation or a payment.
The reviewer field carries what the site itself publishes - a display name and a country, not a full identity. There is no email and no address in this output, and nothing about the reservation behind a review is collected: not what was paid, not the booking reference, not the card. If you are processing the review text in the EU, the usual rules still apply to what you do with it downstream.
Booking.com'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.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
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.
Livescraper's Booking Reviews Monitoring watches a list of hotels instead of exporting them once. You submit Booking.com hotel URLs or bare slugs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - choose how often to re-scan, set the score at or below which a review counts as negative, and give it an address for the report. From then on it runs on its own and the results come to you.
The schedule runs from once a day to once every three months, with a week as the starting cadence, and the negative threshold turns a vague intention to watch reviews into a specific standing question. Booking scores out of ten, so a threshold of three flags the genuinely bad nights while a higher one catches the merely disappointing - and because a scan pulls rows, the frequency you choose is also the bill you choose.
Each row is one guest review: who wrote it and the country shown against them, the score, the headline, what they liked, what they disliked, the review date, the date of the stay, the room type and the traveller type. Booking splits praise and criticism at the source and this export keeps them apart, which is what makes a quarterly pull a clean corpus of complaints rather than a pile of mixed text a model has to sort.
Two practical notes. The scan runs in a headless browser through the proxy pool and the tool's own guidance is that Booking blocks free and datacenter IPs, so a residential PROXY_URL is best; your real IP is never used. And this page describes what the eleven columns are for without promising what arrives in them - the Booking Reviews Scraper uses a byte-identical column list, so archived rows cannot be attributed to this service, and the honest move is to set a monitor on one hotel and read your own first scan. Your first 500 rows cost nothing and need no credit card.