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Booking Review Scraper

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from $2.28 / 1,000 reviews

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Booking Review Scraper

Booking Review Scraper

Read Booking reviews as a source-linked corpus. Start with Booking review or listing URLs; each returned review keeps overall reviews count, review identifiers, reviewed at, author names, and author country code.

Pricing

from $2.28 / 1,000 reviews

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0.0

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Developer

ReapX

ReapX

Maintained by Community

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0

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2

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1

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an hour ago

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Read Booking reviews as a source-linked corpus. Start with Booking review or listing URLs; each returned review keeps overall reviews count, review identifiers, reviewed at, author names, and author country code.

Booking Review Scraper source page and returned record

What it returns

Each row keeps the Booking source record beside the fields needed to use it. The opening set is requestedUrl, propertyName, propertyId, reviewId, authorName, isAnonymous, authorCountryCode, score, title, positiveText, negativeText, and language. The complete schema is declared before the run, and dataset views keep related fields together without changing the underlying row.

Input

Booking Review Scraper published input controls

Booking Review Scraper input-to-run walkthrough

Booking Review Scraper accepts source URLs. Run controls stay in the same form.

FieldWhat it controlsStarting value
startUrlsPaste exact Booking URLs, one per line.["https://www.booking.com/hotel/gb/the-savoy.html"]
maxItemsStop after this many dataset rows.12
maxSecondsStop after this many seconds and keep completed rows.240

Example input

{
"startUrls": [
"https://www.booking.com/hotel/gb/the-savoy.html"
],
"maxItems": 3,
"maxSeconds": 240
}

No field is required. Start with the filled example, then replace only the target values needed for the job. Run controls can stay at their starting values for the first collection.

Dataset fields

Booking Review Scraper declared output schema

FieldType
requestedUrlstring
propertyNamestring
propertyIdinteger
reviewIdinteger
authorNamestring
isAnonymousboolean
authorCountryCodestring
scorenumber
titlestring
positiveTextstring
negativeTextstring
languagestring
travelerTypestring
purposeTypestring
reviewedAtstring
overallScorenumber
overallReviewsCountinteger
routestring

Dataset views

Views are working surfaces for review and export. They select and order fields while leaving the stored row unchanged.

ViewOpening fields
overviewpositiveText, negativeText, reviewId, score, reviewedAt, overallScore, overallReviewsCount, and propertyName
contentpropertyName, propertyId, requestedUrl, positiveText, and negativeText
engagementpropertyName, propertyId, requestedUrl, reviewId, score, reviewedAt, overallScore, and overallReviewsCount
identitypropertyName, propertyId, authorName, title, travelerType, and purposeType
timingpropertyName, propertyId, requestedUrl, scrapedAt, and language
provenancerequestedUrl, route, and wireBytes

Output and exports

OutputTypeDestination
resultsstring{{links.apiDefaultDatasetUrl}}/items
jsonstring{{links.apiDefaultDatasetUrl}}/items?clean=true&format=json
csvstring{{links.apiDefaultDatasetUrl}}/items?clean=true&format=csv
excelstring{{links.apiDefaultDatasetUrl}}/items?clean=true&format=xlsx
jsonlstring{{links.apiDefaultDatasetUrl}}/items?clean=true&format=jsonl

Completed rows are available in the Apify dataset as JSON, CSV, Excel, and JSONL exports. The run output also carries the declared links above for API clients and automations.

Pricing

$4 per 1,000 dataset items on the Free plan. Other Apify plans use the rates shown in the Pricing tab.

Console, API, schedules, and MCP

Booking Review Scraper API and MCP invocation

Runs can begin in Apify Console, from a saved task, or through the Actor API. A schedule can reuse the same input, and a run-finished webhook can pass the dataset or run ID to the next system.

POST https://api.apify.com/v2/acts/dujaaD2WmUN1OeYc1/runs
GET https://api.apify.com/v2/datasets/{datasetId}/items

For MCP selection, use Booking Review Scraper. Its machine entry carries the same description, input field names, no-required-field contract, output types, dataset fields, views, and pricing facts as this document.

Saved tasks

Twenty saved-task products cover distinct lookup, comparison, research, operations, automation, and export jobs:

  • Booking Review ID core response readout: buyer-job; opens overview.
  • Booking Review content property name copy review: buyer-job; opens content.
  • Booking Review rating and review ranking: buyer-job; opens engagement.
  • Booking Review identity property ID match file: buyer-job; opens identity.
  • Booking Review collection times and property ID timeline: buyer-job; opens timing.
  • Booking Review requested URLs routes source: buyer-job; opens provenance.
  • Booking Review collection times review dates core: buyer-job; opens overview.
  • Booking Review positive text content brief: buyer-job; opens content.
  • Booking Review response signal ranking: buyer-job; opens engagement.
  • Booking Review property ID identity roster: buyer-job; opens identity.
  • Booking Review timing collection times recency view: buyer-job; opens timing.
  • Booking Review routes and requested URLs trace file: buyer-job; opens provenance.
  • Booking Review property ID property name core: buyer-job; opens overview.
  • Booking Review negative text property name content: buyer-job; opens content.
  • Booking Review overall score rating view: buyer-job; opens engagement.
  • Booking Review property ID titles identity: buyer-job; opens identity.
  • Booking Review collection times timing publishing file: buyer-job; opens timing.
  • Booking Review source requested URLs origin check: buyer-job; opens provenance.
  • Booking Review title and copy review: buyer-job; opens overview.
  • Booking Review property name reading list: buyer-job; opens content.

Integrations

Use the dataset API from any HTTP client, export rows to a spreadsheet, or send the run ID through an Apify webhook. Saved tasks give schedules and automation tools a stable input without changing the Actor contract.

When a run needs attention

  • No rows: Open the target in a browser, check spelling and source visibility, then retry the saved example before widening the input.
  • A field is empty: Check the field beside its source URL. A missing source value stays empty instead of being replaced with a guess.
  • A target fails: Keep successful targets in the dataset, then retry only the affected input.
  • An automation cannot find results: Read the dataset ID from the run and request its items endpoint directly.

FAQ

What do I get back from one run?

One row per review with 20 declared fields, opening on requestedUrl, propertyName, propertyId and reviewId. The schema is published before the run, so you know the shape before you spend anything.

Do I need a booking account or login?

No. The run works from the booking sources you supply in the input. Nothing is posted, changed or accessed on your behalf.

What does a run cost?

The current rate is shown on the Pricing tab and is charged per row you receive, so a run that finds nothing costs close to nothing. Cap the run with the item limit when you want a predictable ceiling.

Can I try it before committing budget?

Yes. Cap the run with the item limit in the input and inspect the first rows. The cap is enforced before charging, so a trial run stays a trial.

What do I put in the input?

The staged input is already usable: startUrls, maxItems and maxSeconds. Replace the staged target with your own list when you are ready to run for real.

Are any fields required?

No field is required. Every input carries a working default, so the Actor can be started as-is and refined afterwards.

Can I schedule this to run on its own?

Yes. Save the input as an Apify task and attach a schedule. Keep separate tasks when different teams need different targets or delivery paths.

Do I need to configure proxies?

No. Network access is handled inside the Actor and needs no proxy configuration from you.

How fresh is the data?

Every row is collected during the run you start, not served from a cache. Re-run the same input whenever you need the current state of a booking review.

Can I use the results commercially?

The Actor collects publicly accessible booking information. You remain responsible for how you use it, including any privacy or contractual obligations that apply to your business.

How do I compare two runs?

Keep requestedUrl and propertyName as your join key and diff the exports. The identity fields stay stable across runs, which is what makes a comparison meaningful.

What happens if a source fails mid-run?

The run continues through the remaining sources and finishes with what it collected. Partial results are still written to the dataset rather than discarded.

Can I limit how long a run takes?

Yes. The maxItems input caps the run. Use it when you need a predictable cost and a predictable finish time.

How do I report a problem?

Open an issue on the Actor with the run ID, the input you used and the field or row that needs attention. The run ID lets the exact execution be inspected.

How is this different from Booking Travel Data Scraper?

Booking Review Scraper answers one job: Read Booking reviews as a source-linked corpus.. Booking Travel Data Scraper covers a different question on the same platform. Run both when you need both sides.

How do I read the output without scrolling through JSON?

Open the overview view on the Output tab. 6 views ship with the Actor (overview, content, engagement, identity, timing and provenance), each grouping the fields that belong to one question.

How do I get the data into my own tools?

Export the dataset as JSON, CSV, Excel or XML, call the dataset API directly, or attach a run-finished webhook and collect the dataset reference as soon as the run ends.

Can an agent or LLM call this?

Yes. Booking Review Scraper is exposed over MCP with the same description, no-required-field input contract and output types shown here, so an agent can select and call it without a human in the loop.

Why is a value empty on some rows?

booking does not expose every field on every review. An absent value stays empty rather than being filled with a guess, so a row never invents a fact it did not receive.

A run returned fewer rows than I expected. Why?

The usual causes are a narrow source list, an item cap still set low, or a source that genuinely holds less than expected. Widen the input or raise the cap and run again.

Support

Use the Actor issue form for product questions, broken source routes, schema mismatches, and feedback. Include the smallest input that reproduces the problem. That is enough to locate the run and its dataset without sharing an entire working list.

Use this Actor only for data you are allowed to collect. Follow source terms, privacy law, and your own retention policy.