# TripAdvisor Reviews Scraper - All 5 Categories (`delicious_zebu/tripadvisor-review-collector`) Actor

Scrape reviews from TripAdvisor for all 5 categories: Hotels, Restaurants, Attractions, Activities, and Cruises. Rich filters for any need: rating, traveler type, language, date range, and more. Fast API-based, no browser needed.

- **URL**: https://apify.com/delicious_zebu/tripadvisor-review-collector.md
- **Developed by:** [Zebu Data](https://apify.com/delicious_zebu) (community)
- **Categories:** Travel
- **Stats:** 340 total users, 70 monthly users, 98.4% runs succeeded, 41 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $0.40 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### What does TripAdvisor Reviews Scraper - All 5 Categories do?

**TripAdvisor Reviews Scraper - All 5 Categories** extracts review data from [TripAdvisor](https://www.tripadvisor.com) — the world's largest travel platform. It works as an unofficial TripAdvisor API alternative, letting you collect structured review data from **all 5 content categories**: Hotels, Restaurants, Attractions, Activities (Tours & Experiences), and Cruises.

Just provide one or more TripAdvisor detail page URLs, and the Actor will return clean, structured review data in JSON, CSV, Excel, or other formats — ready for analysis.

### Why use TripAdvisor Reviews Scraper - All 5 Categories?

TripAdvisor hosts **over 1 billion reviews** across millions of listings worldwide. Manually collecting this data is impractical. This Actor automates the entire process and gives you:

- 🏨 **All 5 categories** — Hotels, Restaurants, Attractions, Activities, and Cruises in one tool
- 🔍 **Rich filtering** — Filter by rating (1-5), traveler type, time of year, review language, and date range
- 🌍 **49 country sites** — Read from any localized TripAdvisor domain (US, UK, Japan, Germany, France, etc.)
- ⭐ **Rating overview** — Every row carries the page's own overall rating and Excellent-to-Terrible breakdown
- 📅 **Date control** — Scrape only recent reviews (last N days) or reviews after a specific date
- 🌐 **Auto-translate** — Automatically translate non-English reviews to English
- 📊 **37 data fields** per review — Including owner responses, user profiles, photos, and more
- ⚡ **Fast & lightweight** — Uses direct API calls, no browser needed. Low compute cost

#### Common use cases

- **Hospitality analytics** — Monitor guest sentiment and satisfaction trends for hotels and restaurants
- **Competitive analysis** — Compare review scores, traveler types, and seasonal patterns across competitors
- **Market research** — Understand traveler preferences by region, language, and trip type
- **Reputation management** — Track owner responses and identify unaddressed complaints
- **Content aggregation** — Build travel recommendation engines and review dashboards
- **Academic research** — Collect large-scale review datasets for NLP and sentiment analysis

#### Why choose this Actor over alternatives?

| Feature | This Actor                                                    | Others |
|---------|---------------------------------------------------------------|--------|
| Categories | All 5 (Hotels, Restaurants, Attractions, Activities, Cruises) | Usually 1-3 |
| Method | Direct API                                                    | Browser-based (slow, expensive) |
| Filters | Rating, traveler type, language, month, date range            | Limited or none |
| Regions | 49 localized domains                                         | US only or limited |
| Auto-translate | ✅ Built-in                                                    | ❌ Not available |
| Cost | Low (no browser overhead)                                     | Higher CU consumption |

### What data can you extract from TripAdvisor?

Each review includes **37 structured fields**:

| Field | Type | Description |
|-------|------|-------------|
| `locationId` | Text | TripAdvisor location identifier |
| `locationName` | Text | Hotel, restaurant, or attraction name |
| `locationAverageRating` | Number | Overall rating shown on the page, e.g. `4.7` |
| `locationReviewCount` | Number | Total reviews the page reports, e.g. `2522` |
| `locationRatingCounts` | Object | Rating breakdown as shown on the page, e.g. `{"Excellent": 2032, "Good": 312, "Average": 101, "Poor": 40, "Terrible": 37}` |
| `reviewId` | Text | Unique review identifier |
| `reviewUrl` | Link | Direct URL to the review |
| `title` | Text | Review title/headline |
| `text` | Text | Full review text |
| `rating` | Number | Overall rating (1-5) |
| `additionalRatings` | Object | Sub-ratings (Service, Value, Location, etc.) |
| `publishedDate` | Date | When the review was published |
| `createdDate` | Date | When the review was written - this is the date the page shows, and what `Most recent` sorts by. Differs from `publishedDate` on roughly 10% of reviews |
| `travelDate` | Text | When the reviewer traveled |
| `tripType` | Text | Family, Couples, Solo, Business, or Friends |
| `language` | Text | Review language code |
| `translationType` | Text | `MACHINE` when the text you got was machine-translated - the page shows a "Machine Translated" badge on these |
| `helpfulVotes` | Number | Number of helpful votes received |
| `photoUrls` | Array | URLs of photos attached to the review, up to 10 |
| `photoCount` | Number | How many photos the review really has, so you can tell when `photoUrls` was capped |
| `username` | Text | Reviewer's display name |
| `userLocation` | Text | Reviewer's location |
| `contributionCount` | Number | Reviewer's total contributions - the "N contributions" on the page |
| `userHelpfulVotes` | Number | Helpful votes the reviewer has collected across all their reviews - the "N helpful votes" shown next to it. Not the same as `helpfulVotes`, which counts votes on this one review |
| `userProfileUrl` | Link | Link to reviewer's profile |
| `ownerResponseRole` | Text | How the responder is described on the page, e.g. `Owner`, `General Manager` |
| `ownerResponseText` | Text | Management/owner response to the review |
| `ownerResponseDate` | Date | When the owner responded |

*Plus additional fields: `userAvatar`, `originalLanguage`, `ownerResponseName`, `cruiseLabels`, `reviewTips`, `attribution`, `page`, `crawledAt`, `detailUrl`.*

### How to scrape TripAdvisor reviews

1. **Go to the Actor page** on Apify Store and click **Try for free**
2. **Add TripAdvisor URLs** — Paste one or more detail page URLs into the **Detail Page URLs** field. Supported URL formats:
   - `https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-...`
   - `https://www.tripadvisor.com/Restaurant_Review-g298113-d1176949-Reviews-...`
   - `https://www.tripadvisor.com/Attraction_Review-g297415-d502989-Reviews-...`
   - `https://www.tripadvisor.com/AttractionProductReview-g294217-d26810431-...`
   - `https://www.tripadvisor.com/Cruise_Review-d15691669-Reviews-...`
3. **Set filters** (optional) — Choose rating, traveler type, language, date range, or sort order
4. **Click Start** — The Actor begins extracting reviews
5. **Download results** — Export data as JSON, CSV, Excel, XML, or HTML from the Output tab

### How much does it cost to scrape TripAdvisor reviews?

This Actor uses **direct API calls** instead of a browser, making it significantly cheaper than browser-based alternatives. There is no browser rendering overhead, so compute unit (CU) consumption stays low even for large-scale scraping jobs.

Cost depends on the number of reviews extracted and your Apify subscription plan. Apify offers a **free tier** to get started — you can test the Actor before committing to a paid plan.

### Input

The Actor accepts the following input parameters. Only **Detail Page URLs** is required — all other fields are optional with sensible defaults.

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `detailUrls` | Array | *(required)* | List of TripAdvisor detail page URLs (up to 10,000) |
| `maxReviews` | Number | 0 (all) | Maximum reviews per URL. Set 0 for unlimited |
| `region` | Select | United States | Country site, same as the selector at the bottom of the page. Changes TripAdvisor's own labels (place name, reviewer hometown, sub-rating names), not the reviews |
| `currency` | Select | U.S. Dollars | The currency selector at the bottom of the page (36 options). Reviews carry no prices, so this rarely changes anything in the output |
| `ratingFilter` | Multi-select | All | Filter by star rating (1-5) |
| `travelerType` | Multi-select | All | Family, Couples, Solo, Business, Friends |
| `timeOfYear` | Multi-select | All | Filter by month (1-12) |
| `reviewLanguage` | Select | All languages | The "Read reviews in" dropdown. Returns reviews available in that language, with the text in that language; `originalLanguage` still shows what it was written in |
| `startDate` | Date | None | Only scrape reviews after this date. Forces `Most recent` sorting |
| `recentDays` | Number | 0 (all) | Only scrape reviews from the last N days. Forces `Most recent` sorting |
| `sortBy` | Select | Most recent | Most insightful, Most recent, or Detailed reviews - same as the Sort by dropdown on the page. Ignored when a date filter is set |
| `searchQuery` | Text | None | Keyword search inside the reviews, same as the Search reviews box on the page |
| `autoTranslate` | Boolean | true | The "Include review content translations" switch. On, review text comes back in the language you are reading in; off, every review comes back exactly as written |

For full details, see the **Input** tab on the Actor page.

### Output

The Actor stores results in a dataset. You can download the data in multiple formats: **JSON, CSV, Excel, XML, HTML**.

Here is a simplified example of the output in JSON format:

```json
{
    "locationId": 93450,
    "locationName": "Hyatt Grand Central New York",
    "locationAverageRating": 3.5,
    "locationReviewCount": 15005,
    "locationRatingCounts": {"Excellent": 4361, "Good": 4325, "Average": 2947, "Poor": 1702, "Terrible": 1670},
    "reviewId": 1076974161,
    "reviewUrl": "https://www.tripadvisor.com/ShowUserReviews-g60763-d93450-r1076974161-Hyatt_Grand_Central_New_York-New_York_City_New_York.html",
    "title": "September 2026",
    "text": "I selected this hotel three times is because is close by to our NYC office. Others are not too great and I may try other hotel for the next time.",
    "rating": 3,
    "additionalRatings": {},  // Sub-ratings (e.g. Service, Value, Location). Empty {} when the reviewer left none
    "publishedDate": "2026-09-09",
    "createdDate": "2026-09-09",  // The date the page shows. Differs from publishedDate on ~10% of reviews
    "travelDate": "2026-09-30",
    "tripType": "",  // Family, Couples, Solo, Business or Friends. Empty when the reviewer did not say
    "language": "en",
    "originalLanguage": "en",
    "translationType": "",  // MACHINE when the text you got was machine-translated
    "helpfulVotes": 0,  // Helpful votes on THIS review
    "photoUrls": [
        "https://dynamic-media-cdn.tripadvisor.com/media/photo-o/34/87/02/ac/caption.jpg",
        "https://dynamic-media-cdn.tripadvisor.com/media/photo-o/34/87/03/40/caption.jpg",
        "https://dynamic-media-cdn.tripadvisor.com/media/photo-o/34/87/04/c7/caption.jpg"
    ],
    "photoCount": 3,  // Photos the review really has; photoUrls holds at most 10
    "username": "Tak-wai K",
    "userLocation": "Ohio",
    "contributionCount": 4,
    "userHelpfulVotes": 0,  // Helpful votes the REVIEWER has collected across all their reviews
    "userProfileUrl": "https://www.tripadvisor.com/Profile/takwaik2020",
    "userAvatar": "https://dynamic-media-cdn.tripadvisor.com/media/photo-o/1a/f6/e8/91/default-avatar-2020-62.jpg",
    "ownerResponseName": "GCC P",
    "ownerResponseRole": "Owner",
    "ownerResponseText": "Greetings from Hyatt Grand Central New York, Thank you for taking the time to share your feedback...",
    "ownerResponseDate": "2026-09-09",
    "cruiseLabels": {},  // {"Cabin Type": ..., "Type of Trip": ..., "Destination": ...}. Empty {} for non-cruise categories
    "reviewTips": [],  // Traveler tips. Empty [] for categories without tips
    "attribution": "Hyatt Hotels",  // Set when the page says "Review collected in partnership with ..."
    "page": 3,
    "crawledAt": "2026-09-13 07:23:22",
    "detailUrl": "https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html"
}
```

### Tips for best results

- **Start small** — Test with `maxReviews: 10` first to verify the URL works and the output meets your needs
- **Use date filters** — For ongoing monitoring, use `recentDays` (e.g., 30) to only scrape new reviews since your last run
- **Combine filters wisely** — You can combine rating, traveler type, and language filters to extract exactly the reviews you need
- **Languages** — `reviewLanguage` works like the "Read reviews in" dropdown on the page: pick Japanese and you get the reviews available in Japanese, with the text in Japanese (machine-translated where the review was written in another language, as long as `autoTranslate` stays on). Turn `autoTranslate` off and the same reviews come back exactly as written instead. `originalLanguage` always tells you the language it was written in. Note that a language usually has far fewer reviews than the page total - a tour with 305 reviews may have only 33 in Japanese, so combining a language with `recentDays` can legitimately return nothing. `region` is a separate thing - it only changes TripAdvisor's own labels, never the reviews
- **Sorting** — The default is `Most recent`, which returns every review. `Most insightful` is TripAdvisor's own ranking and leaves out a few reviews - mostly ones written in other languages and the newest few days - exactly as the page itself does (a tour showing 305 reviews under `Most recent` shows 293 under `Most insightful`). TripAdvisor only ranks tours & activities this way; on hotels, restaurants, attractions and cruises it gives you the same order as `Most recent`
- **Date filters override sorting** — `startDate` and `recentDays` need the reviews in date order to know when to stop, so setting either one switches the run to `Most recent`. The run log says so when it happens
- **Schedule regular runs** — Use Apify's built-in [scheduling](https://docs.apify.com/platform/schedules) to automatically collect new reviews on a daily or weekly basis
- **Integrate with your tools** — Connect the output to Google Sheets, Slack, Zapier, or your own API via [Apify integrations](https://docs.apify.com/platform/integrations)

### FAQ and support

#### Is it legal to scrape TripAdvisor?

Our Actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our Actors, when used for ethical purposes by Apify users, are safe. However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

#### How many reviews can I scrape?

There is no hard limit from the Actor side. You can scrape all available reviews for a listing by setting `maxReviews` to 0. For listings with thousands of reviews, the Actor will paginate through all pages automatically.

#### Can I scrape reviews in languages other than English?

Yes. Use the `reviewLanguage` filter to select from 17 supported languages including English, Chinese (Simplified/Traditional), Japanese, Korean, German, French, Spanish, Italian, Portuguese, Russian, and more.

#### What if a URL is not supported?

The Actor supports 5 URL types: `Hotel_Review`, `Restaurant_Review`, `Attraction_Review`, `AttractionProductReview`, and `Cruise_Review`. If you provide an unsupported URL format, the Actor will log a warning and skip it.

#### Where can I get help?

If you encounter any issues, please open an issue on the **Issues** tab of this Actor's page. You can also reach out via the Apify community on [Discord](https://discord.com/invite/jyEM2PRvMU).

# Actor input Schema

## `detailUrls` (type: `array`):

TripAdvisor detail page URLs. Works with hotels, restaurants, attractions, tours & activities, and cruises.

## `maxReviews` (type: `integer`):

Most reviews to collect per URL. 0 means all.

## `region` (type: `string`):

The country site to read from - the selector at the bottom of any TripAdvisor page. It sets the language of TripAdvisor's own labels, not of the reviews.

## `currency` (type: `string`):

The currency selector at the bottom of the page. Reviews carry no prices, so it rarely changes the output.

## `sortBy` (type: `string`):

The 'Sort by' dropdown above the reviews. 'Most recent' returns every review; 'Most insightful' is TripAdvisor's own ranking and leaves a few out. Date filters force 'Most recent'.

## `searchQuery` (type: `string`):

The 'Search reviews' box above the reviews. English keywords only - TripAdvisor's review search does not work in other languages. Leave empty for all reviews.

## `ratingFilter` (type: `array`):

The 'Traveler rating' bubbles in the filter panel. Leave empty for all.

## `travelerType` (type: `array`):

The 'Traveler type' chips in the filter panel. Leave empty for all.

## `timeOfYear` (type: `array`):

The 'Time of year' chips in the filter panel, by month of the visit. Leave empty for all.

## `reviewLanguage` (type: `string`):

The 'Read reviews in' dropdown above the reviews. Text comes back in the language you pick; 'originalLanguage' shows what it was written in.

## `startDate` (type: `string`):

Only reviews published on or after this date. Overrides Recent Days, and forces 'Most recent' sorting.

## `recentDays` (type: `integer`):

Only reviews from the last N days. 0 means no limit. Ignored when Start Date is set.

## `autoTranslate` (type: `boolean`):

The 'Include review content translations' switch in the filter panel. Turn it off to get every review exactly as written instead of translated.

## Actor input object example

```json
{
  "detailUrls": [
    "https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html",
    "https://www.tripadvisor.com/Restaurant_Review-g298113-d1176949-Reviews-Yamatake_Shoten-Takayama_Gifu_Prefecture_Tokai_Chubu.html",
    "https://www.tripadvisor.com/Attraction_Review-g297415-d502989-Reviews-Shenzhen_Window_of_the_World-Shenzhen_Guangdong.html",
    "https://www.tripadvisor.com/AttractionProductReview-g294217-d26810431-Hong_Kong_Street_Food_Tour_with_Locals_Dim_Sum_Wonton_more-Hong_Kong.html",
    "https://www.tripadvisor.com/Cruise_Review-d15691669-Reviews-Norwegian_Bliss"
  ],
  "maxReviews": 0,
  "region": "United States",
  "currency": "U.S. Dollars",
  "sortBy": "Most recent",
  "searchQuery": "",
  "ratingFilter": [],
  "travelerType": [],
  "timeOfYear": [],
  "reviewLanguage": "",
  "recentDays": 0,
  "autoTranslate": true
}
```

# Actor output Schema

## `overview` (type: `string`):

Table view of reviews using the 'overview' view from dataset_schema.json.

## `rawResults` (type: `string`):

All review items from the default dataset without a specific view.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "detailUrls": [
        "https://www.tripadvisor.com/AttractionProductReview-g294217-d26810431-Hong_Kong_Street_Food_Tour_with_Locals_Dim_Sum_Wonton_more-Hong_Kong.html"
    ],
    "searchQuery": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("delicious_zebu/tripadvisor-review-collector").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "detailUrls": ["https://www.tripadvisor.com/AttractionProductReview-g294217-d26810431-Hong_Kong_Street_Food_Tour_with_Locals_Dim_Sum_Wonton_more-Hong_Kong.html"],
    "searchQuery": "",
}

# Run the Actor and wait for it to finish
run = client.actor("delicious_zebu/tripadvisor-review-collector").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "detailUrls": [
    "https://www.tripadvisor.com/AttractionProductReview-g294217-d26810431-Hong_Kong_Street_Food_Tour_with_Locals_Dim_Sum_Wonton_more-Hong_Kong.html"
  ],
  "searchQuery": ""
}' |
apify call delicious_zebu/tripadvisor-review-collector --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,delicious_zebu/tripadvisor-review-collector"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ubDATpeTfUbDKTH8j/builds/7AtHftGGnpUPsLRy3/openapi.json