kestrel actors › Guest & customer reviews

TripAdvisor Restaurant Reviews Scraper — Diner Reviews

This TripAdvisor restaurant reviews scraper turns any restaurant or attraction page into structured rows: 1-5 bubbles, the full text, the diner's food / service / value / atmosphere sub-ratings, the visit date, the tips they left for the next guest, their photos and the owner's reply. Point it at tripadvisor restaurant reviews by URL, by location id or by name, pick your languages, and you get clean restaurant reviews and attraction reviews as a dataset, a CSV, an Excel file or a JSON API response. It is a restaurant reviews scraper built for restaurant groups, franchises, delivery platforms, POS and reputation vendors and city tourism boards — the people who need diner reviews at scale rather than one page at a time.

Run TripAdvisor Restaurant Reviews Scraper on Apify →

TripAdvisor restaurant reviews scraper pricing: what one row costs

maxReviewsPerLocation is the main cost control. 0 means every review the site lists for that location in that language; a number means the newest N. TripAdvisor lists 15 reviews per page on a restaurant and 10 on an attraction, newest first, and the actor reads the page-size straight off the page's own paging links — so no page is fetched twice and none is skipped.

Rough sizing for bulk restaurant reviews: a busy city-centre restaurant has 2,000-12,000 reviews in English alone, an attraction like a famous food market can pass 25,000, and each page is around a megabyte of residential proxy traffic. Start with 100-200 per location, look at the total column in the status rows, then decide.

Billing is per delivered review row at $0.005. Locations with no reviews, unknown ids, wrong-family ids, duplicate targets and filtered-out reviews are all free, and the actor never delivers a row it could not charge for.

TripAdvisor restaurant reviews scraper input reference

Every field of TripAdvisor Restaurant Reviews, straight from the actor's input schema.

Example input

{
  "startUrls": [
    "https://www.tripadvisor.com/Restaurant_Review-g189158-d2703366-Reviews-Cervejaria_Ramiro-Lisbon_Lisbon_District_Central_Portugal.html",
    "https://www.tripadvisor.com/Attraction_Review-g189158-d3214902-Reviews-Time_Out_Market_Lisboa-Lisbon.html"
  ],
  "maxReviewsPerLocation": 200,
  "languages": ["en"]
}

{
  "locationIds": ["2703366", "d429386", "3214902"],
  "locationType": "any",
  "maxReviewsPerLocation": 100
}

TripAdvisor restaurant reviews scraper output fields

One row per diner or visitor review (charged), plus a free location context row and a free status row per location and language.

Dataset views in the Apify Console: Overview, Reviews, Complaints, Restaurants, Status.

TripAdvisor restaurant reviews scraper in Python, JavaScript and curl

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("kestrel/tripadvisor-restaurant-reviews").call(run_input={
    "locationIds": ["2703366"],
    "locationType": "restaurant",
    "languages": ["en"],
    "maxReviewsPerLocation": 200,
    "maxRating": 3,
    "requireText": True,
})

for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    if row["type"] != "review":
        continue
    subs = row["sub_ratings"] or {}
    print(row["rating"], subs.get("food"), subs.get("service"), row["review_date"], row["title"])

JavaScript (Node)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('kestrel/tripadvisor-restaurant-reviews').call({
    startUrls: ['https://www.tripadvisor.com/Restaurant_Review-g35805-d429386-Reviews-The_Cheesecake_Factory-Chicago_Illinois.html'],
    maxReviewsPerLocation: 100,
    sinceDate: '90 days',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const reviews = items.filter((i) => i.type === 'review');
console.log(reviews.length, 'reviews,', reviews.filter((r) => r.response).length, 'answered by the owner');

curl

curl -X POST "https://api.apify.com/v2/acts/kestrel~tripadvisor-restaurant-reviews/run-sync-get-dataset-items?token=$APIFY_TOKEN&format=csv" \
  -H 'Content-Type: application/json' \
  -d '{"locationIds":["2703366"],"maxReviewsPerLocation":50,"languages":["en"]}' \
  -o restaurant-reviews.csv

The same three calls are runnable files in the examples repository: examples/tripadvisor-restaurant-reviews.py|.js|.sh.

Limits and honest notes

FAQ

How much does it cost to scrape 10,000 restaurant reviews?

$50 in event charges — 10,000 rows at $0.005 — plus Apify platform usage for the compute and the residential traffic. Filtered reviews are free, so a maxRating: 2 run over the same restaurants costs a fraction of that because only the rows you keep are billed.

Does it get every review, or only the first page?

Every one, if you ask for it. maxReviewsPerLocation: 0 walks the whole list in steps of 15 (restaurants) or 10 (attractions) until TripAdvisor stops returning new ids. The total field in the status row tells you how many that domain lists, and reviews_listed on the location row repeats it, so you can always confirm you got them all.

Can I scrape attraction reviews with the same run?

Yes. Attractions are first-class here: set locationType to attraction or leave it at any and mix restaurant and attraction targets freely. Tripadvisor attraction reviews come back with the same fields minus sub_ratings (attractions have none) and plus category_tags on the location row.

What happens if I pass a hotel URL or a hotel id?

A hotel URL is rejected up front with a message pointing at kestrel/tripadvisor-reviews-scraper. A hotel id cannot be recognised before it is fetched, so the actor reads one page, sees that it is a hotel, emits a free wrong_type status row naming the sibling actor, and stops. You are never charged for it and it is never scraped here.

How fresh are the reviews?

They are read live at run time; fetched_at on every row is the UTC timestamp of the request. There is no cache and no stored snapshot, so a review published a minute ago appears in the next run.

Can I get restaurant reviews with photos?

Yes — photos is an array of 1200 px image URLs and photo_count is the number of them. Roughly one review in five carries at least one photo, and food photos cluster in the reviews that also mention dishes.

Does it handle restaurants that reply to reviews?

That is one of the strongest fields in the output. response, response_date and response_id carry the owner's or manager's reply. Some chains reply to essentially every review, which makes them a clean benchmark for how fast your own locations answer.

Why do the review counts differ between TripAdvisor domains?

Because each domain lists a different set: natively written reviews in that language plus the machine translations TripAdvisor has produced for it. review_count on the location row is the global total across all languages; total in the status row and reviews_listed on the location row are what that one domain lists. reviews_by_language breaks the global number down per language so you can plan which domains to read.

Can I run this on a schedule?

Yes — use Apify Schedules with a relative sinceDate so the input never goes stale, and the run reads only the new pages. Pair it with a webhook to push new rows into your warehouse as they land. This is the normal setup for ongoing review monitoring and it is cheap, because a top-up run reads one or two pages per location.

Is there a limit on how many locations per run?

No hard limit. Practically, keep a run under an hour: with two sessions at one request per second, that is a few thousand pages. For very large fleets, split the list across several runs — they are independent, and location_id makes the results trivial to merge.

What does status: "no_reviews" mean?

The location exists and was read, but the domain you asked for lists no reviews for it — a brand-new restaurant, or a language nobody has reviewed it in. It is a free row, and it is deliberately distinct from error, which means the page could not be read at all.

Can I use the output commercially?

The actor is a tool; what you may do with the data depends on your use case, your jurisdiction and TripAdvisor's terms. Analysis, benchmarking and internal reporting are the normal uses. Verbatim republication of review text and retention of reviewer identities are the two areas where you should take your own legal advice — see the legality section above.

How to use TripAdvisor Restaurant Reviews Scraper

• How to Scrape TripAdvisor Reviews Without Getting Blocked — TripAdvisor blocks datacenter IPs and Chrome-shaped clients. The measured setup that works, the paging trap that loses reviews, and working Python.

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Run TripAdvisor Restaurant Reviews Scraper

The actor lives on the Apify Store, where the pricing tab carries the live per-row price and the input form has every field above: apify.com/kestrel/tripadvisor-restaurant-reviews