We benchmarked 4 web scraping providers on Tripadvisor review pages with 2,000 total requests, measuring success rate, completion time, and data extraction quality.
Tripadvisor reviews benchmark
You can read more about our Tripadvisor benchmark methodology.
Pros and cons and benchmark results of the best Tripadvisor scrapers
Oxylabs led on Tripadvisor with a 91% success rate and the fastest completion time at 7s per request. Reviews were parsed from rendered HTML using CSS selectors. It handled the largest share of test URLs without failure, and the low latency makes it a practical option for high-volume Tripadvisor scraping where speed matters.
Decodo completed Tripadvisor at a 76% success rate and 16s average completion time. While it handled the majority of URLs, some pages did not render fully enough for the CSS selectors to match, which accounted for most of the failures. Its completion time was comparable to Zyte, making it a reasonable alternative where a slightly lower success rate is acceptable.
SerpApi offers a dedicated Tripadvisor Search Engine Results API that returns structured JSON from Tripadvisor search pages, including listings for hotels, restaurants, and attractions. The API is purpose-built for search pages rather than individual review extraction, making it a different fit from the review scraping APIs tested in this benchmark. For users who primarily need search result data from Tripadvisor alongside other search engines, SerpAPI provides a unified structured interface without requiring HTML parsing.
Zyte came in at 86% success on Tripadvisor, averaging 15s per request. It delivered stable results throughout the test with no major gaps across the URL set. Like all providers on this domain, extraction relied on CSS selector parsing of browser-rendered HTML. The consistent performance across different page types and review counts suggests reliable rendering under varied conditions.
Nimble finished at 73% on Tripadvisor with the slowest average completion time at 38s. The gap in both speed and success rate compared to the other three providers points to differences in how its rendering engine processes Tripadvisor’s dynamic page structure. Pages with longer review threads or heavy JavaScript appeared to cause the most extraction failures.
How does Tripadvisor compare to other review platforms for scraping?
Tripadvisor sits in the middle of the difficulty scale among the platforms in our reviews scraping benchmark. The highest success rate on Tripadvisor was 91%, which falls between the results we saw in our Amazon review scraping benchmark (96%) and and our Yelp review scraping benchmark (77%) at the lower end.
Unlike Amazon and Trustpilot, where some providers offer structured JSON APIs that return parsed review data with 10-39 fields, no provider returned structured JSON for Tripadvisor in our benchmark. All extraction relied on HTML rendering and CSS selector parsing.
Tripadvisor was more accessible than Yelp and Google Maps. Every provider in this benchmark extracted at least some data from Tripadvisor, which was not the case which was not the case on Yelp or Google Maps, where multiple providers recorded 0% success rates.
What data fields can you extract from Tripadvisor?
For Tripadvisor reviews benchmark, we focused on restaurant review pages and extracted the following fields per review:
- Reviewer name: The display name of the reviewer
- Rating: Star rating (1-5), extracted from the rating element’s class or aria-label
- Review text: The full review body
- Review date: When the review was posted
- Review title: The headline of the review
Tripadvisor review pages also display additional data extractable with more advanced selectors or dedicated APIs, including trip type (family, couples, solo, business), visit date, reviewer location, helpful vote count, management responses, and attached photos. None of the providers in this benchmark returned these as structured fields.
Beyond reviews, Tripadvisor exposes richer data fields for other listing types:
Hotel listings include:
- Amenities (parking, WiFi, fitness center) and room features by category
- Room types (non-smoking, family rooms, etc.)
- Price range, number of rooms, and languages spoken
- GPS coordinates, walk score, and nearby airports and attractions
- AI-generated review summary and highlights broken down by category (Value, Rooms, Atmosphere) with supporting guest quotes
Restaurant listings include:
- Cuisine tags and price level
- Aggregate rating and total review count
- Nearby restaurant suggestions with the same structured fields
- Thumbnail images per listing
Add as preferred sourceHow to scrape Tripadvisor reviews
Scraping Tripadvisor review pages requires a rendering-capable provider because review content is loaded via JavaScript. The steps below apply to any of the providers covered in this benchmark.
1. Collect the review page URLs
You can copy individual URLs directly from Tripadvisor, or collect them at scale by running a keyword-based search scrape first. Searching by location name or category returns a list of hotel or restaurant pages with their URLs, which you can then feed into a review scraping pipeline without manual lookup.
2. Send the URLs to a rendering provider
Pass each URL to your chosen provider with JavaScript rendering enabled. Each provider in this benchmark exposes this through a different parameter: render: true for Nimble, browserHtml: true for Zyte, the X-SU-Headless header for Decodo, and the Web Unblocker endpoint for Oxylabs.
3. Define your CSS selectors
Once the provider returns rendered HTML, you need to identify the CSS selectors that target the fields you want. Open the rendered HTML in a browser or inspector, locate the review card container, and map selectors to the fields you plan to extract. Different use cases require different fields, so this step depends on what data matters for your project.
If the provider returns structured JSON instead of raw HTML, this step is not needed as the fields are already parsed and labeled in the response.
4. Store your output
Write extracted reviews to a structured format such as JSON or CSV as you collect them. Storing incrementally rather than at the end avoids losing progress if the run fails partway through a large location.
Tripadvisor reviews benchmark methodology
We ran 500 Tripadvisor review page URLs through 4 web scraping providers, producing 2,000 total requests. Providers were selected from web scraping companies with at least 100 employees. Each provider received an identical URL set, and we evaluated three metrics: success rate, completion time, and available metadata fields.
All four providers returned rendered HTML on Tripadvisor, which we parsed using CSS selectors to extract five standard review fields: reviewer_name, review_text, rating, review_date, and review_title. No provider returned structured JSON for this domain.
Validation
Responses were validated in three stages:
- Submission: The provider had to return an HTTP status code between 200-399, or 404.
- Execution: For providers with asynchronous processing, the job had to finish without timeout or error.
- Data check: The response had to include extractable review data, meaning at least one CSS selector had to return review content.
We pre-tested each provider with broken URLs, known 404 pages, and pages with no reviews to understand how they report these cases. When a provider correctly signaled a missing or empty page, the result was counted as valid.
A cross-provider check was then applied: if one provider returned no data on a URL where another provider successfully extracted reviews, that empty result was marked as a failure. This allowed us to separate pages with no reviews from cases where the provider failed to extract available data.
Completion time
We measured wall-clock time from the initial request to the final response, including any rendering or queue time.
URL selection
The 500 URLs were drawn from Tripadvisor attraction and restaurant review pages across a range of review counts and location types. Invalid formats and duplicates were removed before testing.
Provider configurations
Oxylabs used its Web Unblocker proxy, which returns rendered HTML. Review data was extracted using CSS selectors.
Zyte used its Extract API with browserHtml enabled, rendering pages through a headless browser. Review data was extracted from the returned HTML using CSS selectors.
Decodo used its web unblocker proxy with the X-SU-Headless header for JavaScript rendering. Review data was extracted from the returned HTML using CSS selectors.
Nimble used its Web API with render: true, which processes pages through a headless browser. Review data was extracted from the returned HTML using CSS selectors.
Test conditions
All providers operated under the same constraints:
- One request at a time, no parallel execution
- 2-second delay between requests
- HTTP 429 handled with 30-second backoff and up to 3 retries
- 300-second submission timeout
- 600-second execution timeout
- Single run per URL per provider
FAQs
Tripadvisor uses JavaScript rendering, CAPTCHAs, and request fingerprinting to detect automated access. All four providers in our benchmark used headless browser rendering to handle these protections. Adding delays between requests and handling HTTP 429 responses with retries helps maintain stable extraction.
Yes, Tripadvisor displays reviews in their original language by default. The same URLs and provider configurations work across all languages. Some reviews include a translated version which can also be extracted if the translation element is rendered on the page.
Both use a similar page structure with the same review card format. The CSS selectors used in this benchmark worked across hotel, restaurant, and attraction review pages without modification. The main difference is that hotel reviews may include sub-ratings (cleanliness, service, location, value) which require additional selectors to extract.
Can I scrape Tripadvisor for free?
Direct HTTP requests to Tripadvisor without a proxy or rendering layer will be blocked in most cases. Free proxies and unmanaged headless browsers produce low success rates on Tripadvisor due to its bot detection. The providers in this benchmark offer paid plans; costs depend on request volume and the level of rendering required.
Can I extract the overall rating and total review count for a place, not just individual reviews?
Yes. The aggregate rating and review count are displayed in the page header and can be extracted with separate CSS selectors targeting the summary section. These fields were not part of the core benchmark extraction set but are available from the same rendered HTML returned by all four providers.
How do I spot fake Tripadvisor reviews?
Fake reviews tend to follow detectable patterns at scale. Generic or unusually short text, clusters of 5-star reviews posted within a narrow time window, and reviewer profiles with no review history are common signals. Bulk reviews originating from the same location or posted on the same day also stand out. When collecting review data through scraping, fields like review date, reviewer profile link, and per-author review count can be extracted and used to flag these patterns systematically.
Does anyone still use Tripadvisor?
Tripadvisor continues to be one of the largest travel review platforms in the world, with coverage spanning hotels, restaurants, and attractions across hundreds of thousands of locations. It remains a primary source for hospitality reputation monitoring and is widely used by travel platforms that aggregate reviews from multiple sources. The volume and depth of its review data make it a frequent target for sentiment analysis and competitive research.
Cite this research
Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.
@misc{ipi2026,
author = {Şipi, Nazlı},
title = {{5 Best Tripadvisor Reviews Scraping APIs}},
year = {2026},
month = jun,
howpublished = {\url{https://aimultiple.com/scraping-tripadvisor}},
note = {AIMultiple. Retrieved June 2, 2026}
}
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