TripAdvisor Reviews Scraper - Bulk Hotels, Restaurants

Pull up to 500 reviews per property from a list of TripAdvisor hotels, restaurants or attractions in one run. Hand the actor a batch of TripAdvisor location IDs or URLs, and get each individual review as its own clean dataset row - ready for CSV, Excel, sentiment analysis or a dashboard. No subscription, you only pay per review.

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Why this Actor?

Most TripAdvisor tools stop at the first page of reviews and hand you a single blob per place. This one is a reviews specialist: it paginates deep, slices by traveler segment and star rating, and returns one flat row per review across up to 200 properties in a single run.

Feature	This Actor (Data Forge)	Typical competitors
Reviews per property per run	Up to 500 (200 properties x 500 = up to 100,000 rows)	First page only (about 10)
Star-rating filter	Yes - any of the 5 levels (1-5)	No
Traveler-type filter	Families, couples, solo, business, friends	No
Keyword search inside reviews	Yes - server-side text match	No
Sort modes	5 - relevant, newest, oldest, highest, lowest	Default order only
Language selection	17 - all languages plus 16 codes	Site default
Trip type on every row	Yes	Not exposed
Pricing	Pay per result - $0.0008 / review	Monthly subscription

What this actor does

• Bulk by design - up to 200 locations per run, up to 500 reviews each (200 x 500 = up to 100,000 review rows in one run).
• IDs or URLs, mixed - paste numeric location_ids or full Hotel_Review / Restaurant_Review / Attraction_Review URLs; IDs are auto-extracted.
• Real filters - language (or all languages), star rating, traveler type (families / couples / solo / business / friends), free-text keyword search, and 5 sort orders.
• One flat row per review - rating, title, full text, reviewer, dates, trip type, helpful votes, photos. No nested objects to flatten.
• You only pay for reviews - locations that fail get a single free diagnostic row; the run keeps going.

Input modes

Each mode below maps 1:1 to a published example task. All use Le Bristol Paris; swap in your own IDs or URLs.

1. Bulk export - build a full review dataset for a property

Data analyst assembling a review corpus for one hotel:

{
    "locations": ["https://www.tripadvisor.com/Hotel_Review-g187147-d188729-Reviews-Le_Bristol_Paris-Paris_Ile_de_France.html"],
    "maxReviewsPerLocation": 500
}

2. Negative mining - pull only 1-2 star complaints

CX or operations team hunting for recurring problems to fix:

{
    "locations": ["https://www.tripadvisor.com/Hotel_Review-g187147-d188729-Reviews-Le_Bristol_Paris-Paris_Ile_de_France.html"],
    "ratingFilter": ["1", "2"],
    "maxReviewsPerLocation": 500
}

3. Family travelers only - segment by who wrote the review

Marketer studying how families experience the property:

{
    "locations": ["https://www.tripadvisor.com/Hotel_Review-g187147-d188729-Reviews-Le_Bristol_Paris-Paris_Ile_de_France.html"],
    "travelerType": "families",
    "maxReviewsPerLocation": 500
}

4. Newest first - track the latest sentiment

Reputation monitor watching the freshest feedback:

{
    "locations": ["https://www.tripadvisor.com/Hotel_Review-g187147-d188729-Reviews-Le_Bristol_Paris-Paris_Ile_de_France.html"],
    "sort": "newest",
    "maxReviewsPerLocation": 500
}

5. Keyword search - find every mention of a topic

Product researcher pulling only reviews that talk about breakfast:

{
    "locations": ["https://www.tripadvisor.com/Hotel_Review-g187147-d188729-Reviews-Le_Bristol_Paris-Paris_Ile_de_France.html"],
    "textSearch": "breakfast",
    "maxReviewsPerLocation": 500
}

Input

Field	Type	Required	Description
locations	array	Yes	Up to 200 TripAdvisor location_ids or review-page URLs (mixed).
maxReviewsPerLocation	integer	No	Reviews to pull per property. Default 500 (accepts 1 to 10000).
language	string	No	Review language, or all for every language (default en). 17 options total.
sort	string	No	most_relevant / newest / oldest / highest / lowest (5 modes).
ratingFilter	array	No	Keep only chosen star ratings (1-5). Empty = all.
travelerType	string	No	families / couples / solo / business / friends.
textSearch	string	No	Keep only reviews containing this text.
stopOnLocationError	boolean	No	Abort the whole run on the first failing location (default off).

Example input: { "locations": ["1218720", "https://www.tripadvisor.com/Restaurant_Review-g187147-d12947099-Reviews-Septime-Paris.html"], "maxReviewsPerLocation": 100, "language": "en", "sort": "most_relevant" }

Output

One row per review. Columns: location_id, location_input, review_id, rating, title, text, language, published_date, stay_date, trip_type, reviewer_name, reviewer_location, helpful_votes, photos. Diagnostic rows (a whole location failed) carry error_code + error_message instead. A run-summary OUTPUT record reports locations succeeded/failed, reviews pulled and the estimated cost. Live per-review pricing is shown on this actor's Apify Store page.

Example output row

{
    "location_id": "188729",
    "location_input": "https://www.tripadvisor.com/Hotel_Review-g187147-d188729-Reviews-Le_Bristol_Paris-Paris_Ile_de_France.html",
    "review_id": 987654321,
    "rating": 5,
    "title": "Faultless Parisian luxury",
    "text": "Staff remembered our names and the breakfast was the best of the whole trip.",
    "language": "en",
    "published_date": "2026-07-04",
    "stay_date": "2026-06",
    "trip_type": "couples",
    "reviewer_name": "TravellerJane",
    "reviewer_location": "London, UK",
    "helpful_votes": 12,
    "photos": ["https://media-cdn.tripadvisor.com/media/photo-s/2a/00/11/22/room-with-a-view.jpg"]
}

Who uses it

• Hotel and restaurant ops - mine 1-2 star reviews to fix recurring complaints before they spread.
• Brand and reputation teams - track newest-first sentiment across a portfolio of properties.
• Market researchers - segment by traveler type and keyword to compare how families and couples talk about the same place.
• Data teams - export a flat, CSV-ready review corpus in one click, with no JSON to flatten.

FAQ

Is scraping TripAdvisor reviews legal? The actor only reads publicly visible review pages that any visitor can open, and returns the same facts TripAdvisor already shows on screen. You stay responsible for using the output in line with TripAdvisor's terms and your local data rules. No account setup is needed on your side.

How fresh are the reviews? Reviews are pulled live on every run. In a verified run against Le Bristol Paris the dataset included reviews published as recently as 2026-07-04, so you get same-week feedback rather than a stale cache.

What location inputs are accepted? Two forms, mixed freely in one run: a numeric TripAdvisor location_id (for example 188729), or a full review-page URL whose path contains a -d{ID}- segment (a Hotel_Review, Restaurant_Review or Attraction_Review page). The location_id is auto-extracted from the URL, so you never have to parse it yourself.

Can I schedule this to run automatically? Yes. Use the Apify Scheduler to run the actor daily or weekly, or trigger it from your own code through the Apify API and webhooks. Each run appends fresh review rows to the dataset.

How many reviews can I get per property? Up to 500 per property per run by default, and you can raise maxReviewsPerLocation. Large properties hold thousands of reviews, so set the cap to the sample size you actually need. You only pay for rows delivered, at $0.0008 per review.

What happens when a location fails? That single property gets one free diagnostic row (error_code + error_message) and the run continues to the next location. You are never charged for a failed location.

Related actors

Part of the Data Forge TripAdvisor suite - pick the actor that fits the job:

• TripAdvisor Hotels, Restaurants & Cruises Scraper - search a whole destination across 5 verticals (hotels, restaurants, things to do, vacation rentals, cruises).
• TripAdvisor URL Parser - paste any TripAdvisor URLs and get the parsed payload per URL.
• Booking.com Reviews Scraper - the same bulk-reviews workflow for Booking.com properties.

Support

I personally support every customer - drop a message on any channel.