# TripAdvisor Reviews \[Only $0.45šŸ’°] Scraper (`memo23/tripadvisor-scraper`) Actor šŸ’°$0.45 per 1,000 Scrape TripAdvisor hotel reviews: title, rating, language, text, dates, owner response, photos, sub-ratings, and optional reviewer profiles. Each review is enriched with place metadata (rating, address, geo, website, histogram). Filter by rating, language, date and per-place limit - **URL**: https://apify.com/memo23/tripadvisor-scraper.md - **Developed by:** [Muhamed Didovic](https://apify.com/memo23) (community) - **Categories:** Travel, Automation, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.45 / 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. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### TripAdvisor Reviews Scraper **Turn TripAdvisor hotel pages into structured review datasets.** Pull every public review for a property ā€” title, rating, language, text, travel date, owner response, photos, subā€‘ratings ā€” already enriched with the host place's full metadata (address, geo, ranking, ratingHistogram). One run, one clean dataset. #### How it works ![How TripAdvisor Reviews Scraper Works](https://raw.githubusercontent.com/muhamed-didovic/muhamed-didovic.github.io/main/assets/how-it-works-tripadvisor.png) #### āœØ Why use this scraper? Manually opening hotel pages and copying reviews? Stitching together separate "reviews" and "place details" scrapes? Getting blocked by DataDome the moment you scale? - šŸØ **Reviews + place metadata in the same row.** Every review already carries `placeInfo` (rating, address, lat/lng, ranking, histogram). No followā€‘up enrichment. - šŸŽÆ **Serverā€‘side filters wired through TripAdvisor's GraphQL.** Star ratings, languages, perā€‘place limits and dates are pushed down to the API ā€” you get back what you asked for. - šŸ“… **Absolute or relative date cutoff.** `"2026-01-01"` or `"22 days"`, `"3 weeks"`, `"6 months"`, `"1 year"` ā€” all valid for `lastReviewDate`. - šŸ‘¤ **Optional reviewer profiles.** Flip `scrapeReviewerInfo` to switch from the lean reviewā€‘centric output to a reviewerā€‘centric output with username, hometown, contributions, avatar, profile link. - šŸ›”ļø **Hardened antiā€‘bot path.** Mobileā€‘Safari UA fallback through `undici` for HTML, real browser fingerprinting via `ImpitHttpClient` for the GraphQL endpoint, singleā€‘shot DataDome detection. - šŸ“‘ **Perā€‘location failure dataset.** Skipped or blocked hotels land in a side dataset (`tripadvisor-failures`) instead of getting buried in logs. - āš” **Parallel pagination.** Each hotel pages through GraphQL in concurrent batches (default 3), respecting your global and perā€‘place caps. #### šŸŽÆ Use cases | Team | What they build | |------|----------------| | **Hotel ops** | Daily review monitoring + ownerā€‘response SLA tracking | | **Reputation managers** | Multiā€‘property reputation dashboards with ratingHistogram drift over time | | **Market analysts** | Competitive benchmarks across cities or chains using `placeInfo.rating` + `numberOfReviews` | | **Content / NLP teams** | Multilingual review corpora for sentiment and topic models, filtered by language and rating | | **Travel media** | Curated "best of" articles backed by recent verified reviews | | **Data teams** | Oneā€‘shot dataset exports for BI, lake or warehouse ingestion (JSON, CSV, Excel) | #### šŸ”§ How it works (pipeline) 1. **Detect location ID** from each `*_Review-...` URL (`-d{id}-`) ā€” works for hotels, restaurants and attractions. 2. **Fetch the place HTML** with a mobileā€‘Safari Userā€‘Agent. Falls back to a direct `undici` fetch when DataDome blocks the desktop fingerprint, and to URLā€‘derived `placeInfo` when even that is blocked (reviews still come through over GraphQL). 3. **Extract `placeInfo`** from the page's JSONā€‘LD + meta description (rating, review count, address, geo, ranking position). 4. **Page through GraphQL reviews** at `/data/graphql/ids` in concurrent batches, with `reviewRatings` / `reviewsLanguages` / `maxItemsPerQuery` pushed into the filter payload. 5. **Apply `lastReviewDate`** clientā€‘side after each page, and break early once an entire page is older than the cutoff. 6. **Map and push** each review enriched with `placeInfo` to the default dataset; skipped or blocked places go to `tripadvisor-failures`. #### šŸ“„ Supported inputs Currently supported start URLs (mix and match in a single run): | Pattern | Example | |---------|---------| | `Hotel_Review-g{geoId}-d{locationId}-Reviews-{slug}.html` | `https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html` | | `Restaurant_Review-g{geoId}-d{locationId}-Reviews-{slug}.html` | `https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html` | | `Attraction_Review-g{geoId}-d{locationId}-Reviews-{slug}.html` | `https://www.tripadvisor.com/Attraction_Review-g60763-d105123-Reviews-Statue_of_Liberty-New_York_City_New_York.html` | All three share the same GraphQL endpoint and review schema ā€” only `placeInfo.type` differs (`HOTEL`, `EATERY`, `ATTRACTION`). Not currently supported: - Search / list pages (`FindRestaurants?...`, `Hotels-gā€¦`, `Attractions-gā€¦`). - `tripadvisor.co.*` country domains (use `.com`). - Nonā€‘TripAdvisor hosts. #### āš™ļø Input parameters ##### Required | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `startUrls` | array of `{ url }` | ā€” | One or more TripAdvisor place URLs (`Hotel_Review-ā€¦`, `Restaurant_Review-ā€¦`, or `Attraction_Review-ā€¦`). Run them in parallel up to `maxConcurrency`. | ##### Filters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItemsPerQuery` | integer | `50` | Max reviews to fetch per place. `0` = unlimited (paginate to the end). | | `reviewRatings` | array | `[]` (all) | Star ratings to keep. Values: `"1"`, `"2"`, `"3"`, `"4"`, `"5"`, or `"ALL_REVIEW_RATINGS"`. Pushed down into the GraphQL filter payload. | | `reviewsLanguages` | array | `[]` (all) | ISO 639ā€‘1 codes (e.g. `["en", "de", "fr"]`) or `"ALL_REVIEW_LANGUAGES"`. Pushed down into the GraphQL filter payload. | | `lastReviewDate` | string | ā€” | Skip reviews published before this date. Accepts an **absolute** date `YYYY-MM-DD` or a **relative** duration: `"22 days"`, `"2 weeks"`, `"3 months"`, `"1 year"` (singular or plural). | | `scrapeReviewerInfo` | boolean | `true` | When `true`, the `user` object on each review is populated with the reviewer's profile (username, display name, avatar, hometown, contributions, profile link). When `false`, `user` is `null`. The rest of the review fields (`id`, `lang`, `helpfulVotes`, `tripType`, `subratings`, `photos`, ā€¦) are emitted in **both** modes. | ##### Advanced | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | `10000` | Hard ceiling on the total number of items written to the dataset across all places. `0` = no global cap. | | `maxConcurrency` | integer | `100` | Max start URLs (hotels) processed concurrently. | | `minConcurrency` | integer | `1` | Crawler floor. | | `maxRequestRetries` | integer | `15` (range `0`ā€“`50`) | Retries per request before giving up. Lower = surface failures fast, higher = absorb transient antiā€‘bot blocks. | | `proxy` | object | `{ useApifyProxy: true, apifyProxyGroups: ["RESIDENTIAL"] }` | Standard Apify proxy block. Apify Residential is strongly recommended for TripAdvisor. | #### šŸ“Š Output overview Each dataset row is **one review** enriched with the host place's `placeInfo`. The full review schema is emitted in **both** `scrapeReviewerInfo` modes ā€” the only difference is whether the `user` object is populated (`true`) or set to `null` (`false`). ##### Review schema (both modes) ```json { "id": "1003456789", "url": "https://www.tripadvisor.com/ShowUserReviews-g60763-d208453-r1003456789-Hilton_New_York_Times_Square-New_York_City_New_York.html", "title": "Great location, friendly staff", "lang": "en", "language": "en", "originalLanguage": "en", "locationId": "208453", "publishedDate": "2026-03-14", "publishedPlatform": "OTHER", "rating": 5, "helpfulVotes": 2, "text": "We stayed three nights and...", "travelDate": "2026-03", "stayDate": "2026-03-14", "tripType": "COUPLES", "user": null, "ownerResponse": { "id": "987654", "text": "Thank you for staying with us...", "lang": "en", "publishedDate": "2026-03-16", "responder": "Hilton Times Square", "connectionToSubject": "Manager" }, "subratings": [ { "name": "Service", "value": 5 }, { "name": "Cleanliness", "value": 5 }, { "name": "Value", "value": 4 }, { "name": "Location", "value": 5 }, { "name": "Rooms", "value": 4 }, { "name": "Sleep Quality", "value": 5 } ], "photos": [ { "id": "812340000", "width": 4032, "height": 3024, "image": "https://media-cdn.tripadvisor.com/media/photo-o/30/68/0c/00/lobby.jpg" } ], "placeInfo": { "id": "208453", "name": "Hilton New York Times Square", "rating": 4.0, "numberOfReviews": 8944, "locationString": "New York City, New York", "latitude": 40.756, "longitude": -73.989, "webUrl": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html", "website": "https://www.hilton.com/...", "address": "234 W 42nd St, New York City, NY 10036", "addressObj": { "street1": "234 W 42nd St", "city": "New York City", "state": "NY", "postalcode": "10036", "country": "United States" }, "ratingHistogram": { "count1": 412, "count2": 480, "count3": 1100, "count4": 2680, "count5": 4272 } } } ```` With `scrapeReviewerInfo: true` the only field that changes is `user` ā€” it is replaced with the reviewer's profile object: ```json { "user": { "userId": "ABCDEF12345", "name": "Traveler 123", "contributions": { "totalContributions": 42 }, "username": "traveler123", "userLocation": { "shortName": "Berlin", "name": "Berlin, Germany", "id": "187323" }, "avatar": { "id": "5567890", "width": 200, "height": 200, "image": "https://media-cdn.tripadvisor.com/media/photo-l/...jpg" }, "link": "www.tripadvisor.com/Profile/traveler123" } } ``` #### šŸ—‚ļø Output fields ##### Review core All fields below are emitted in **both** `scrapeReviewerInfo` modes. | Field | Type | Description | |-------|------|-------------| | `id` | string | TripAdvisor review ID | | `url` | string | Permalink to the review on TripAdvisor | | `title` | string | Review headline | | `text` | string | Review body | | `rating` | integer (1ā€“5) | Stars left by the reviewer | | `lang` | string (ISO 639ā€‘1) | Source language (preserves `originalLanguage` for machineā€‘translated reviews) | | `language` | string (ISO 639ā€‘1) | Current API language (may be the translated language) | | `originalLanguage` | string (ISO 639ā€‘1) | Source language before machine translation | | `publishedDate` | string (ISO date) | When the review was published | | `publishedPlatform` | string | Source platform (e.g. `OTHER`, `MOBILE`) | | `helpfulVotes` | integer | Helpfulā€‘vote count | | `travelDate` | string (`YYYY-MM`) | Month/year of the stay | | `stayDate` | string (`YYYY-MM-DD`) | Full stay date | | `tripType` | string | null | `COUPLES`, `FAMILY`, `BUSINESS`, `SOLO`, `FRIENDS`, ā€¦ | | `locationId` | string | TripAdvisor location ID for the place | ##### Owner response | Field | Type | Description | |-------|------|-------------| | `ownerResponse.id` | string | Response ID | | `ownerResponse.text` | string | Response body | | `ownerResponse.lang` | string | Response language | | `ownerResponse.publishedDate` | string | Response date | | `ownerResponse.responder` | string | Display name (e.g. property name or manager) | | `ownerResponse.connectionToSubject` | string | `Manager`, `Owner`, etc. | ##### Subratings `subratings` is an **array** of `{ name, value }`: | Field | Type | Description | |-------|------|-------------| | `subratings[].name` | string | `Service`, `Cleanliness`, `Value`, `Location`, `Rooms`, `Sleep Quality`, ā€¦ | | `subratings[].value` | integer (1ā€“5) | Perā€‘aspect rating | ##### Photos `photos` is an **array** of `{ id, width, height, image }`: | Field | Type | Description | |-------|------|-------------| | `photos[].id` | string | TripAdvisor photo ID | | `photos[].width` / `height` | integer | Native pixel dimensions | | `photos[].image` | string | Bare CDN URL (no `?w=...` query) | ##### Reviewer (only when `scrapeReviewerInfo: true`) | Field | Type | Description | |-------|------|-------------| | `user.userId` | string | null | Internal TripAdvisor user ID | | `user.name` | string | null | Display name | | `user.username` | string | null | URL handle | | `user.contributions.totalContributions` | integer | Lifetime contribution count | | `user.userLocation` | object | null | `{ shortName, name, id }` resolved from hometown | | `user.avatar` | object | null | `{ id, width, height, image }` | | `user.link` | string | null | Profile link without scheme (`www.tripadvisor.com/Profile/...`) | ##### Place metadata (`placeInfo`) | Field | Type | Description | |-------|------|-------------| | `placeInfo.id` | string | Numeric location ID | | `placeInfo.name` | string | Hotel name | | `placeInfo.rating` | number | Aggregate 1ā€“5 rating | | `placeInfo.numberOfReviews` | integer | Total review count on TripAdvisor | | `placeInfo.locationString` | string | Humanā€‘readable city/state/country | | `placeInfo.latitude` / `longitude` | number | Geo coordinates | | `placeInfo.address` | string | Singleā€‘line address | | `placeInfo.addressObj` | object | `{ street1, street2, city, state, postalcode, country }` | | `placeInfo.website` | string | Hotel's official website | | `placeInfo.webUrl` | string | TripAdvisor URL | | `placeInfo.ratingHistogram` | object | `{ count1, count2, count3, count4, count5 }` | #### šŸš€ Examples ##### Single hotel, defaults ```json { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" } ], "maxItemsPerQuery": 50 } ``` ##### Multiple hotels, Englishā€‘only, last 30 days ```json { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" }, { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d93589-Reviews-The_Manhattan_at_Times_Square_Hotel-New_York_City_New_York.html" } ], "maxItemsPerQuery": 100, "reviewsLanguages": ["en"], "lastReviewDate": "30 days" } ``` ##### Recent 3ā€‘star reviews with full reviewer profiles ```json { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" } ], "maxItemsPerQuery": 50, "reviewRatings": ["3"], "reviewsLanguages": ["en"], "lastReviewDate": "2026-01-01", "scrapeReviewerInfo": true } ``` ##### Mix hotels and restaurants in one run ```json { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" }, { "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html" } ], "maxItemsPerQuery": 50, "reviewsLanguages": ["en"] } ``` #### šŸ’» Integrations ##### Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("muhamed-didovic/apify-tripadvisor").call(run_input={ "startUrls": [ {"url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html"}, ], "maxItemsPerQuery": 50, "reviewsLanguages": ["en"], "lastReviewDate": "30 days", }) for review in client.dataset(run["defaultDatasetId"]).iterate_items(): print(f"{review['rating']}ā˜… {review['title']} ({review['lang']})") print(f" place: {review['placeInfo']['name']} rating: {review['placeInfo']['rating']}") ``` ##### JavaScript / Node ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('muhamed-didovic/apify-tripadvisor').call({ startUrls: [ { url: 'https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html' }, ], maxItemsPerQuery: 50, scrapeReviewerInfo: true, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((r) => { console.log(`${r.rating}ā˜… ${r.title} by ${r.user?.username ?? 'anon'}`); }); ``` #### šŸ“ˆ Performance | Metric | Value | |--------|-------| | Reviews per hotel page (GraphQL) | 10 | | Concurrent review pages per hotel | up to 3 (autoā€‘capped to 1 when many hotels run in parallel) | | Antiā€‘bot path | Mobileā€‘Safari UA + `ImpitHttpClient` GraphQL fingerprint, singleā€‘shot DataDome detection | | Failure visibility | Perā€‘location rows in `tripadvisor-failures` named dataset | | Output formats | JSON, CSV, Excel, RSS, XML, HTML (via Apify dataset views) | #### šŸ’” Tips for best results 1. **Start with one place and `maxItemsPerQuery: 10` to validate the schema.** Works the same for hotels, restaurants and attractions ā€” confirm the fields you care about land in the dataset before launching big runs. 2. **Use serverā€‘side filters.** `reviewRatings` and `reviewsLanguages` are pushed into the GraphQL payload ā€” much cheaper than fetching everything and filtering downstream. 3. **Prefer relative dates over absolute when running on a schedule.** `"30 days"` always means "last 30 days from now"; an absolute `2026-01-01` will silently widen as time passes. 4. **Lower `maxRequestRetries` for fast feedback.** The default `15` absorbs antiā€‘bot blocks but can hide a misconfigured proxy. Try `3` while iterating, raise it for production. 5. **Toggle `scrapeReviewerInfo` only when you need profiles.** All review fields are kept in both modes; the only thing that changes is whether `user` is populated. Disabling it makes the dataset smaller and the run faster. 6. **Check the `tripadvisor-failures` dataset after each run.** Empty = perfect run. Rows = locationā€‘level issues you can replay. #### ā“ FAQ **Which TripAdvisor URLs are supported?** `Hotel_Review-ā€¦`, `Restaurant_Review-ā€¦`, and `Attraction_Review-ā€¦` URLs on `www.tripadvisor.com` (anything that follows the `ā€¦-g{geoId}-d{locationId}-Reviews-ā€¦` slug pattern). They share the same GraphQL endpoint and review schema ā€” only `placeInfo.type` differs (`HOTEL` / `EATERY` / `ATTRACTION`). Search/list pages and `tripadvisor.co.*` country domains are **not** supported ā€” use `.com`. **How many reviews can I scrape per run?** As many as your Apify plan and `maxItems` / `maxItemsPerQuery` allow. The actor paginates TripAdvisor's GraphQL API in batches of 10 per request, in parallel where possible. **What does `scrapeReviewerInfo` actually change?** The only field that changes is `user`. With `true` (default) the `user` object is populated with the reviewer's profile (username, display name, avatar, hometown, contributions, profile link). With `false` it's `null`. All other review fields (`id`, `lang`, `subratings`, `photos`, `helpfulVotes`, `tripType`, ā€¦) are emitted in both modes. **What date formats does `lastReviewDate` accept?** Both absolute (`2026-01-01`, ISO `YYYY-MM-DD`) and relative durations: `"22 days"`, `"3 weeks"`, `"6 months"`, `"1 year"` (singular and plural both work). Reviews older than the resolved date are dropped clientā€‘side, with an earlyā€‘break optimisation that stops paginating once an entire page is older than the cutoff. **Why is `lang` sometimes different from the onā€‘site language toggle?** TripAdvisor machineā€‘translates reviews. We always emit the **source** language (`originalLanguage`), not the displayed translation, so analytics groupings stay accurate. **Can I scrape multiple places in one run?** Yes ā€” pass any number of `startUrls`. Hotels, restaurants and attractions can be mixed in the same run; they're processed concurrently up to `maxConcurrency`. Intraā€‘place pagination is autoā€‘capped to 1 in that case so the GraphQL endpoint isn't hammered with `concurrency Ɨ pages` requests. **Where do failed locations end up?** In a named dataset called `tripadvisor-failures` with rows like `{ url, locationId, locationType, reason, message, timestamp }`. The reason field is one of `invalid-location-id`, `html-blocked`, `datadome-block`, `reviews-fetch-failed`, `no-reviews-saved`. The Apify run page surfaces a direct link. **Is private content accessible?** No. The actor only sees public TripAdvisor pages and the public review GraphQL endpoint. #### šŸ“¬ Support - Bug reports / feature requests: use the [Issues](https://console.apify.com/actors/ftbw0YFo17iTQ2qjv/issues) tab on the actor page. - Direct contact: - Author's website: [muhamed-didovic.github.io](https://muhamed-didovic.github.io/) #### šŸ› ļø Additional services - Custom output schemas, oneā€‘off datasets, scheduled exports. - Other platforms (Booking, Yelp, Google Maps, etc.) on request. - API integrations and automation pipelines. Email: . #### šŸ”­ Explore more scrapers If this actor was useful, browse other scrapers from [memo23 on Apify](https://apify.com/memo23). #### āš–ļø Legal & compliance This scraper targets publicly accessible TripAdvisor content for legitimate research, monitoring, and analytics. You are responsible for: - Complying with TripAdvisor's terms of service. - Respecting `robots.txt` and reasonable rate limits. - Using scraped data lawfully (privacy, copyright, GDPR/CCPA where applicable). - Obtaining any necessary permissions for commercial reuse of the data. # Actor input Schema ## `startUrls` (type: `array`): Add one or more URLs of places on TripAdvisor and get one or all (or anything in between) reviews. Both **Hotel\_Review** and **Restaurant\_Review** URLs are supported (Attraction\_Review URLs work too ā€” anything that follows the `/_Review-g{geoId}-d{locationId}-Reviews-ā€¦` pattern). Mix and match in a single run. Examples: - Hotel: `https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html` - Restaurant: `https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html` ## `maxItemsPerQuery` (type: `integer`): Maximum number of reviews to scrape per place. Use 0 for unlimited (will fetch every review the API exposes). ## `scrapeReviewerInfo` (type: `boolean`): When enabled, populate the `user` object on each review with the reviewer's profile (username, display name, avatar, hometown, profile link, contributions). When disabled, `user` is set to null. ## `lastReviewDate` (type: `string`): Skip any review published before this date. Pick an absolute date (YYYY-MM-DD) or a relative duration like `22 days`, `2 weeks`, `3 months`, `1 year`. ## `reviewRatings` (type: `array`): Filter reviews by overall rating. Leave empty to include every rating. ## `reviewsLanguages` (type: `array`): Filter reviews by language (ISO 639-1 codes). Leave empty to include every language. ## `maxItems` (type: `integer`): Hard ceiling on the total number of items written to the dataset across all places. Use 0 for no global cap (still limited per-place by `maxItemsPerQuery`). ## `maxConcurrency` (type: `integer`): Maximum number of pages that can be processed at the same time. ## `minConcurrency` (type: `integer`): Minimum number of pages that will be processed at the same time. ## `maxRequestRetries` (type: `integer`): Number of times each failed request is retried before giving up. Lower values surface persistent failures quickly; higher values absorb transient anti-bot blocks at the cost of long-tail latency. ## `proxy` (type: `object`): Specifies proxy servers that will be used by the scraper in order to hide its origin.

For details, see Proxy configuration in README. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" }, { "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html" } ], "maxItemsPerQuery": 50, "scrapeReviewerInfo": true, "reviewRatings": [ "ALL_REVIEW_RATINGS" ], "reviewsLanguages": [ "ALL_REVIEW_LANGUAGES" ], "maxItems": 10000, "maxConcurrency": 100, "minConcurrency": 1, "maxRequestRetries": 15, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): Direct link to the run's default dataset items. ## `failures` (type: `string`): Locations that produced 0 reviews or failed mid-pagination. Empty if the run was perfect. # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" }, { "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html" } ], "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("memo23/tripadvisor-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`šŸ’¾ 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); }); // šŸ“š Want to learn more šŸ“–? Go to ā†’ 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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" }, { "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html" }, ], "proxy": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("memo23/tripadvisor-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("šŸ’¾ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # šŸ“š Want to learn more šŸ“–? Go to ā†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html" }, { "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html" } ], "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call memo23/tripadvisor-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=memo23/tripadvisor-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TripAdvisor Reviews [Only $0.45šŸ’°] Scraper", "description": "šŸ’°$0.45 per 1,000 Scrape TripAdvisor hotel reviews: title, rating, language, text, dates, owner response, photos, sub-ratings, and optional reviewer profiles. Each review is enriched with place metadata (rating, address, geo, website, histogram). Filter by rating, language, date and per-place limit", "version": "0.0", "x-build-id": "MnuDU9VIQJWmU2ayd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/memo23~tripadvisor-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-memo23-tripadvisor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/memo23~tripadvisor-scraper/runs": { "post": { "operationId": "runs-sync-memo23-tripadvisor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/memo23~tripadvisor-scraper/run-sync": { "post": { "operationId": "run-sync-memo23-tripadvisor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "Add one or more URLs of places on TripAdvisor and get one or all (or anything in between) reviews. Both **Hotel_Review** and **Restaurant_Review** URLs are supported (Attraction_Review URLs work too ā€” anything that follows the `/_Review-g{geoId}-d{locationId}-Reviews-ā€¦` pattern). Mix and match in a single run.\n\nExamples:\n- Hotel: `https://www.tripadvisor.com/Hotel_Review-g60763-d208453-Reviews-Hilton_New_York_Times_Square-New_York_City_New_York.html`\n- Restaurant: `https://www.tripadvisor.com/Restaurant_Review-g60763-d25324283-Reviews-Allora_Fifth_Ave-New_York_City_New_York.html`", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItemsPerQuery": { "title": "Limit reviews for each place", "minimum": 0, "type": "integer", "description": "Maximum number of reviews to scrape per place. Use 0 for unlimited (will fetch every review the API exposes).", "default": 50 }, "scrapeReviewerInfo": { "title": "Scrape reviewer info", "type": "boolean", "description": "When enabled, populate the `user` object on each review with the reviewer's profile (username, display name, avatar, hometown, profile link, contributions). When disabled, `user` is set to null.", "default": true }, "lastReviewDate": { "title": "Only reviews since date", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$|^(\\d+)\\s*(day|week|month|year)s?$", "type": "string", "description": "Skip any review published before this date. Pick an absolute date (YYYY-MM-DD) or a relative duration like `22 days`, `2 weeks`, `3 months`, `1 year`." }, "reviewRatings": { "title": "Review ratings", "uniqueItems": true, "type": "array", "description": "Filter reviews by overall rating. Leave empty to include every rating.", "items": { "type": "string", "enum": [ "1", "2", "3", "4", "5", "ALL_REVIEW_RATINGS" ], "enumTitles": [ "Terrible", "Poor", "Average", "Very Good", "Excellent", "All ratings" ] }, "default": [ "ALL_REVIEW_RATINGS" ] }, "reviewsLanguages": { "title": "Reviews languages", "uniqueItems": true, "type": "array", "description": "Filter reviews by language (ISO 639-1 codes). Leave empty to include every language.", "items": { "type": "string", "enum": [ "ALL_REVIEW_LANGUAGES", "ab", "aa", "af", "ak", "sq", "am", "ar", "an", "hy", "as", "av", "ae", "ay", "az", "bm", "ba", "eu", "be", "bn", "bh", "bi", "bs", "br", "bg", "my", "ca", "ch", "ce", "ny", "zh", "cv", "kw", "co", "cr", "hr", "cs", "da", "dv", "nl", "dz", "en", "eo", "et", "ee", "fo", "fj", "fi", "fr", "ff", "gl", "ka", "de", "el", "gn", "gu", "ht", "ha", "he", "hz", "hi", "ho", "hu", "ia", "id", "ie", "ga", "ig", "ik", "io", "is", "it", "iu", "ja", "jv", "kl", "kn", "kr", "ks", "kk", "km", "ki", "rw", "ky", "kv", "kg", "ko", "ku", "kj", "la", "lb", "lg", "li", "ln", "lo", "lt", "lu", "lv", "gv", "mk", "mg", "ms", "ml", "mt", "mi", "mr", "mh", "mn", "na", "nv", "nd", "ne", "ng", "nb", "nn", "no", "ii", "nr", "oc", "oj", "cu", "om", "or", "os", "pa", "pi", "fa", "pl", "ps", "pt", "qu", "rm", "rn", "ro", "ru", "sa", "sc", "sd", "se", "sm", "sg", "sr", "gd", "sn", "si", "sk", "sl", "so", "st", "es", "su", "sw", "ss", "sv", "ta", "te", "tg", "th", "ti", "bo", "tk", "tl", "tn", "to", "tr", "ts", "tt", "tw", "ty", "ug", "uk", "ur", "uz", "ve", "vi", "vo", "wa", "cy", "wo", "fy", "xh", "yi", "yo", "za", "zu" ], "enumTitles": [ "All review languages", "Abkhaz", "Afar", "Afrikaans", "Akan", "Albanian", "Amharic", "Arabic", "Aragonese", "Armenian", "Assamese", "Avaric", "Avestan", "Aymara", "Azerbaijani", "Bambara", "Bashkir", "Basque", "Belarusian", "Bengali", "Bihari", "Bislama", "Bosnian", "Breton", "Bulgarian", "Burmese", "Catalan", "Chamorro", "Chechen", "Chichewa", "Chinese", "Chuvash", "Cornish", "Corsican", "Cree", "Croatian", "Czech", "Danish", "Divehi", "Dutch", "Dzongkha", "English", "Esperanto", "Estonian", "Ewe", "Faroese", "Fijian", "Finnish", "French", "Fula", "Galician", "Georgian", "German", "Greek", "GuaranĆ­", "Gujarati", "Haitian Creole", "Hausa", "Hebrew", "Herero", "Hindi", "Hiri Motu", "Hungarian", "Interlingua", "Indonesian", "Interlingue", "Irish", "Igbo", "Inupiaq", "Ido", "Icelandic", "Italian", "Inuktitut", "Japanese", "Javanese", "Kalaallisut", "Kannada", "Kanuri", "Kashmiri", "Kazakh", "Khmer", "Kikuyu", "Kinyarwanda", "Kyrgyz", "Komi", "Kongo", "Korean", "Kurdish", "Kwanyama", "Latin", "Luxembourgish", "Ganda", "Limburgish", "Lingala", "Lao", "Lithuanian", "Luba-Katanga", "Latvian", "Manx", "Macedonian", "Malagasy", "Malay", "Malayalam", "Maltese", "Māori", "Marathi", "Marshallese", "Mongolian", "Nauru", "Navajo", "Northern Ndebele", "Nepali", "Ndonga", "Norwegian BokmĆ„l", "Norwegian Nynorsk", "Norwegian", "Nuosu", "Southern Ndebele", "Occitan", "Ojibwe", "Old Church Slavonic", "Oromo", "Oriya", "Ossetian", "Panjabi", "Pāli", "Persian", "Polish", "Pashto", "Portuguese", "Quechua", "Romansh", "Kirundi", "Romanian", "Russian", "Sanskrit", "Sardinian", "Sindhi", "Northern Sami", "Samoan", "Sango", "Serbian", "Scottish Gaelic", "Shona", "Sinhala", "Slovak", "Slovenian", "Somali", "Southern Sotho", "Spanish", "Sundanese", "Swahili", "Swati", "Swedish", "Tamil", "Telugu", "Tajik", "Thai", "Tigrinya", "Tibetan Standard", "Turkmen", "Tagalog", "Tswana", "Tonga", "Turkish", "Tsonga", "Tatar", "Twi", "Tahitian", "Uyghur", "Ukrainian", "Urdu", "Uzbek", "Venda", "Vietnamese", "VolapĆ¼k", "Walloon", "Welsh", "Wolof", "Western Frisian", "Xhosa", "Yiddish", "Yoruba", "Zhuang", "Zulu" ] }, "default": [ "ALL_REVIEW_LANGUAGES" ] }, "maxItems": { "title": "Max Items (global)", "type": "integer", "description": "Hard ceiling on the total number of items written to the dataset across all places. Use 0 for no global cap (still limited per-place by `maxItemsPerQuery`).", "default": 10000 }, "maxConcurrency": { "title": "Max Concurrency", "type": "integer", "description": "Maximum number of pages that can be processed at the same time.", "default": 100 }, "minConcurrency": { "title": "Min Concurrency", "type": "integer", "description": "Minimum number of pages that will be processed at the same time.", "default": 1 }, "maxRequestRetries": { "title": "Max Request Retries", "minimum": 0, "maximum": 50, "type": "integer", "description": "Number of times each failed request is retried before giving up. Lower values surface persistent failures quickly; higher values absorb transient anti-bot blocks at the cost of long-tail latency.", "default": 15 }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Specifies proxy servers that will be used by the scraper in order to hide its origin.

For details, see Proxy configuration in README.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```