# Wego Reviews Scraper (`stealth_mode/wego-reviews-scraper`) Actor Scrape hotel reviews from Wego.com by hotel ID, with control over sorting, pagination, and volume. Outputs 13 structured fields including ratings, review content, author info, and provider metadata — perfect for hospitality analytics and reputation monitoring. - **URL**: https://apify.com/stealth\_mode/wego-reviews-scraper.md - **Developed by:** [Stealth mode](https://apify.com/stealth_mode) (community) - **Categories:** Automation, Developer tools, Travel - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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/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 ## Wego.com Hotel Reviews Scraper: Extract Guest Reviews at Scale --- ### What Is Wego.com? Wego.com is a travel metasearch platform popular across Asia-Pacific and the Middle East, aggregating hotel prices and guest reviews from multiple providers. Its review data — sourced from various booking platforms — offers valuable signals for reputation analysis, competitive benchmarking, and sentiment research. Manually collecting this data is impractical at scale; the **Wego.com Reviews Scraper** automates extraction with flexible filtering and pagination. --- ### Overview The **Wego Hotel Reviews Scraper** collects guest review records for any hotel listed on Wego.com, identified by its numeric Hotel ID. It supports: - **Reputation managers** monitoring guest sentiment across providers - **Data analysts** building review datasets for NLP or sentiment modeling - **Hospitality researchers** comparing ratings across properties or regions - **Developers** feeding review data into dashboards or aggregator platforms --- ### Input Format ```json { "hotel_id": "1028684", "sort_by": "posted_at,desc", "page": 2, "max_items_per_url": 200, "ignore_url_failures": true } ```` | Field | Type | Description | |---|---|---| | `hotel_id` | `string` | **Required.** The numeric Hotel ID from the Wego URL. Example: `.../1028684` → `"1028684"` | | `sort_by` | `string` | Sort order for reviews. Options: `"posted_at,desc"` (Latest), `"rating,desc"` (High rating first), `"rating,asc"` (Low rating first) | | `page` | `integer` | Page number to start scraping from. Default: `1`. Use `2`+ to skip earlier pages or resume a run | | `max_items_per_url` | `integer` | Maximum number of reviews to collect. Default: `20`. Set higher (e.g., `200`) for bulk extraction | | `ignore_url_failures` | `boolean` | If `true`, the scraper continues running even if a page fails. Recommended: `true` for large runs | **Finding your Hotel ID:**\ Open a hotel on Wego.com. The URL pattern is:\ `hotels/searches/lon/2026-05-26/2026-05-27/`**`1028684`**\ The trailing number is your `hotel_id`. *** ### Output Format **Sample record:** ```json { "id": "expedia.com_1651839863_1028684", "hotel_id": 1028684, "provider_code": "expedia.com", "provider_review_id": "1651839863", "provider_hotel_url": null, "wego_review_id": 2022, "locale": "en", "title": "Awesome Hotel", "rating": 10.0, "posted_at": "2022-05-06", "updated_at": "2026-05-26", "contents": [ { "type": "neutral", "comment": "They stay here was amazing! The staff was very friendly and always checking up on us. Their bar and restaurant was really good. We got a pizza and a chicken blue (not 100% sure on the name, but look at the pic). Their gym has an awesome view. \n\nThis was my first time in London and I had an awesome time! And this hotel was a big part. Good job team!\n\nFinally, this hotel is walking distance from Dadas Diner, which is a restaurant I really recommend. The walk there is nice!" } ], "author": { "type": "extended_group", "type_en": "extended_group", "name": "Martin", "travel_purpose": "leisure", "traveller_type": "Group", "traveller_type_en": "Group", "country": null, "is_anonymous": null }, "from_url": "https://srv.wego.com/hotel-management/api/provider-reviews/v2/search?app_type=WEB_APP&locale=en&per_page=100&page=2&sorts=score_desc&hotel_id=1028684" } ``` Each review returns 13 fields: #### Identifiers | Field | Meaning | |---|---| | `ID` | Internal scraper record ID | | `Hotel ID` | Wego hotel identifier matching your input | | `Wego Review ID` | Wego's own unique review identifier | | `Provider Review ID` | Original review ID from the source booking platform | | `Provider Code` | Code identifying the review source provider (e.g., Booking.com, Expedia) | | `Provider Hotel URL` | URL of the hotel listing on the originating provider's site | #### Review Content | Field | Meaning | |---|---| | `Title` | Headline of the review as written by the guest | | `Rating` | Numeric score given by the reviewer | | `Contents` | Full text body of the review | | `Locale` | Language/region of the reviewer (e.g., `en_US`, `ar_AE`) | #### Timestamps & Author | Field | Meaning | |---|---| | `Posted At` | Date the review was originally published | | `Updated At` | Date the review was last modified | | `Author` | Reviewer name or anonymized identifier | ----------------------------------------------------- ### How to Use 1. **Locate the Hotel ID** — Open the target hotel on Wego.com and extract the numeric ID from the URL. 2. **Set sort order** — Choose `posted_at,desc` for latest reviews, or `rating,asc` to surface the most critical feedback first. 3. **Configure pagination** — Set `page: 1` to start from the beginning, or a higher value to resume from a specific page. 4. **Set volume** — Use `max_items_per_url` to cap collection. Set `200`+ for comprehensive datasets. 5. **Run and export** — Download results as JSON or CSV for analysis. **Tips:** - To collect all reviews, run multiple times incrementing `page` and combining results. - Use `sort_by: "rating,asc"` to prioritize negative reviews for complaint analysis. - `ignore_url_failures: true` is recommended for multi-page runs to avoid interruptions on empty pages. *** ### Use Cases & Business Value - **Reputation monitoring:** Track how a property's perceived quality shifts over time - **Competitive analysis:** Compare sentiment and ratings across competing hotels - **NLP/sentiment modeling:** Build labeled datasets for machine learning pipelines - **Provider attribution:** Identify which booking platform drives the most reviews via `Provider Code` *** ### Conclusion The **Wego.com Hotel Reviews Scraper** delivers clean, structured review data with provider-level attribution and flexible sorting — without manual browsing. Whether you're monitoring a single property or building a multi-hotel dataset, it provides the depth needed for meaningful hospitality intelligence. # Actor input Schema ## `hotel_id` (type: `string`): Enter Hotel ID. For example, hotels/searches/lon/2026-05-26/2026-05-27/1028684 -> 1028684 ## `sort_by` (type: `string`): Select your option to sort reviews ## `page` (type: `integer`): Page to start from ## `ignore_url_failures` (type: `boolean`): If true, the scraper will continue running even if some URLs fail to be scraped. ## `max_items_per_url` (type: `integer`): The maximum number of items to scrape per URL. ## Actor input object example ```json { "hotel_id": "1028684", "page": 1, "ignore_url_failures": true, "max_items_per_url": 20 } ``` # 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 = { "hotel_id": "1028684", "page": 1, "ignore_url_failures": true, "max_items_per_url": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("stealth_mode/wego-reviews-scraper").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "hotel_id": "1028684", "page": 1, "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("stealth_mode/wego-reviews-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "hotel_id": "1028684", "page": 1, "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call stealth_mode/wego-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stealth_mode/wego-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wego Reviews Scraper", "description": "Scrape hotel reviews from Wego.com by hotel ID, with control over sorting, pagination, and volume. Outputs 13 structured fields including ratings, review content, author info, and provider metadata — perfect for hospitality analytics and reputation monitoring.", "version": "0.0", "x-build-id": "t5VgKQdx0eDb726a6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stealth_mode~wego-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stealth_mode-wego-reviews-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stealth_mode~wego-reviews-scraper/runs": { "post": { "operationId": "runs-sync-stealth_mode-wego-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stealth_mode~wego-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-stealth_mode-wego-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "hotel_id": { "title": "Hotel ID (Hotel ID on wego.com tour review page url, e.g. hotels/searches/lon/2026-05-26/2026-05-27/1028684 -> 1028684)", "type": "string", "description": "Enter Hotel ID. For example, hotels/searches/lon/2026-05-26/2026-05-27/1028684 -> 1028684" }, "sort_by": { "title": "Sort by", "enum": [ "posted_at,desc", "rating,desc", "rating,asc" ], "type": "string", "description": "Select your option to sort reviews" }, "page": { "title": "Page", "type": "integer", "description": "Page to start from" }, "ignore_url_failures": { "title": "Continue running even if some URLs fail to be scraped", "type": "boolean", "description": "If true, the scraper will continue running even if some URLs fail to be scraped." }, "max_items_per_url": { "title": "Max items per URL", "type": "integer", "description": "The maximum number of items to scrape per URL." } } }, "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 } } } } } } } } } } ```