# Thefork Reviews Scraper (`stealth_mode/thefork-reviews-scraper`) Actor Scrape verified diner reviews from TheFork.com including ratings, meal dates, reviewer profiles, restaurant replies, and more. Perfect for reputation monitoring, sentiment analysis, and competitive research in the restaurant industry. - **URL**: https://apify.com/stealth\_mode/thefork-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 ## TheFork Restaurant Reviews Scraper: Extract Ratings & Feedback at Scale --- ### What Is TheFork? TheFork (formerly LaFourchette) is one of Europe's leading restaurant discovery and reservation platforms, operating across 20+ countries. Each restaurant profile hosts hundreds of verified diner reviews — a goldmine for restaurateurs, marketers, and food-tech developers. The **TheFork Reviews Scraper** automates extraction of this review data, turning pages of feedback into structured, analyzable datasets. --- ### Overview This scraper targets TheFork restaurant review pages, collecting verified guest feedback with full metadata. It is built for: - **Restaurant owners** monitoring reputation and tracking guest sentiment - **F&B consultants** benchmarking competitors across ratings and keywords - **Data analysts** building sentiment models on dining experiences - **Developers** powering review aggregators or hospitality dashboards Configurable sorting, pagination offset, and per-URL limits make it suitable for both quick snapshots and large-scale bulk collection. --- ### Input Format ```json { "ignore_url_failures": true, "max_items_per_url": 200, "offset": 0, "urls": [ "https://www.thefork.com/restaurant/bistrot-xvi-r840349/reviews" ], "sort_by": "RATING-DESC" } ```` | Field | Type | Description | |---|---|---| | `urls` | `array` | One or more TheFork restaurant **review page** URLs (must end in `/reviews`). Add individually or via bulk edit. | | `sort_by` | `string` | Sort order for reviews. Options: `MEAL_DATE-DESC` *(Newest)*, `RATING-DESC` *(Score high to low)*, `RATING-ASC` *(Score low to low)* | | `offset` | `integer` | Review index to start from. Use `0` for the beginning; increase to resume from a specific position (e.g., `100` to skip the first 100 reviews). | | `max_items_per_url` | `integer` | Maximum reviews to collect per URL. Default prefill: `20`. Set higher (e.g., `200`) for comprehensive collection. | | `ignore_url_failures` | `boolean` | If `true`, failed URLs are skipped rather than stopping the entire run — recommended for bulk jobs. | > **Tip:** To collect only negative reviews for analysis, use `sort_by: "RATING-ASC"`. To collect the most recent feedback, use `"MEAL_DATE-DESC"`. *** ### Output Format #### Sample Record (abbreviated) ```json { "id": "005991a0-fd58-43bf-ab23-a9cdbe297584", "uuid": "005991a0-fd58-43bf-ab23-a9cdbe297584", "rating_value": 10, "meal_date": "2025-12-06T12:45:00.000Z", "review": { "review_body": "très bien positionné. La serveuse a su gérer en étant toute seule. top !", "__typename": "RatingReview" }, "reviewer": { "id": "376e7cbf-e63c-4606-b6d2-29ad31b3f357", "avatar_url": "https://cdn.thefork.com/tf-lab/image/upload/v1779222864/avatar/376e7cbf-e63c-4606-b6d2-29ad31b3f357.png", "first_name": "vincent", "last_name": "d.", "review_count": 48, "__typename": "Reviewer" }, "restaurant_reply": null, "photos": [], "likes": 0, "keywords_position": null, "from_url": "https://www.thefork.com/restaurant/bistrot-xvi-r840349/reviews" } ``` Each record represents one verified diner review with the following fields: #### Core Review Data | Field | Meaning | |---|---| | `ID` | Internal numeric identifier for the review | | `UUID` | Universally unique identifier — useful as a stable key when deduplicating across runs | | `Rating Value` | Numerical score given by the diner (typically on TheFork's 10-point scale) | | `Meal Date` | The date the diner visited the restaurant (not the date the review was posted) | | `Review` | Full text of the diner's written feedback | #### Reviewer & Response | Field | Meaning | |---|---| | `Reviewer` | Profile metadata of the person who left the review (may include display name, reviewer tier) | | `Restaurant Reply` | The restaurant's official response to the review, if one exists — useful for tracking engagement quality | #### Enrichment Fields | Field | Meaning | |---|---| | `Photos` | Images attached to the review by the diner | | `Likes` | Number of "helpful" or like reactions the review has received from other users | | `Keywords Position` | Structured keyword or tag data associated with the review — may indicate dish mentions, service aspects, or TheFork-generated topic tags | *** ### How to Use 1. **Get the review URL** — Navigate to any restaurant on TheFork and go to the *Reviews* tab. Copy the URL (format: `.../restaurant/[name]-r[id]/reviews`). 2. **Configure input** — Paste the URL(s), set `sort_by` and `max_items_per_url` to match your needs. 3. **Set offset if resuming** — Use `offset` to paginate large datasets across multiple runs. 4. **Run and export** — Download results as JSON or CSV for use in Excel, Google Sheets, or your data pipeline. **Common issues:** - If no data is returned, confirm the URL ends in `/reviews` — product or menu pages will not work. - TheFork may vary page structure by country/locale; test with one URL before bulk runs. *** ### Use Cases & Business Value - **Reputation management:** Track rating trends over time and flag negative feedback early - **Competitor benchmarking:** Compare review volumes, average scores, and response rates across restaurants - **Sentiment analysis:** Feed review text into NLP pipelines to extract topics and sentiment at scale - **Menu intelligence:** Mine `Keywords Position` and review text for frequently mentioned dishes *** ### Conclusion The **TheFork Reviews Scraper** delivers structured, verified dining feedback at scale — eliminating hours of manual reading and copy-pasting. Whether you're managing a single venue or analyzing hundreds of competitors, it provides the raw material for data-driven hospitality decisions. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the Restaurant details urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. ## `sort_by` (type: `string`): Select your option to sort reviews ## `offset` (type: `integer`): Offset 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 { "urls": [ "https://www.thefork.com/restaurant/bistrot-xvi-r840349/reviews" ], "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 = { "urls": [ "https://www.thefork.com/restaurant/bistrot-xvi-r840349/reviews" ], "offset": 0, "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/thefork-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 = { "urls": ["https://www.thefork.com/restaurant/bistrot-xvi-r840349/reviews"], "offset": 0, "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("stealth_mode/thefork-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 '{ "urls": [ "https://www.thefork.com/restaurant/bistrot-xvi-r840349/reviews" ], "offset": 0, "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call stealth_mode/thefork-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stealth_mode/thefork-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Thefork Reviews Scraper", "description": "Scrape verified diner reviews from TheFork.com including ratings, meal dates, reviewer profiles, restaurant replies, and more. Perfect for reputation monitoring, sentiment analysis, and competitive research in the restaurant industry.", "version": "0.0", "x-build-id": "f8jnXZ767R1GapjZr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stealth_mode~thefork-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stealth_mode-thefork-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stealth_mode~thefork-reviews-scraper/runs": { "post": { "operationId": "runs-sync-stealth_mode-thefork-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stealth_mode~thefork-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-stealth_mode-thefork-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "urls": { "title": "URLs of the Restaurant details urls to scrape", "type": "array", "description": "Add the URLs of the Restaurant details urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.", "items": { "type": "string" } }, "sort_by": { "title": "Sort by", "enum": [ "MEAL_DATE-DESC", "RATING-DESC", "RATING-ASC" ], "type": "string", "description": "Select your option to sort reviews" }, "offset": { "title": "Offset", "type": "integer", "description": "Offset 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 } } } } } } } } } } ```