# Yelp Review Intelligence (`enezli/yelp-review-intelligence`) Actor Yelp review analysis for local business owners. Turns reviews into an owner-ready action plan: sentiment, recurring themes (service, staff, food/product, atmosphere, cleanliness, value) and prioritized fixes that lift your rating. - **URL**: https://apify.com/enezli/yelp-review-intelligence.md - **Developed by:** [Turgay NANTA](https://apify.com/enezli) (community) - **Categories:** AI, Marketing - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $400.00 / 1,000 per 100 reviews 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Yelp Review Analysis for Local Business Owners: From Reviews to an Action Plan Turns hundreds of **Yelp reviews** into an **owner-ready action plan** in seconds. It does not just tell you what customers think; it tells you what to do about it: prioritized fixes, quick wins, and the single critical move to lift your rating this week. ### What it does Connect the output of a review-scraper (Google Maps, Trustpilot, Amazon, App Store) or your own review list, and the actor produces: - **Overall sentiment** + star-rating distribution - **Recurring themes** (frequency + positive/negative) - **Top complaints and praises** - **🎯 Action plan** — prioritized, concrete steps (each with benefit/cost + expected gain) - **⚡ Quick wins** — low-effort, high-impact moves - **The single critical step for this week** - **Competitor opportunities** + a short executive summary ### Why it's different Most review tools just hand you a raw sentiment score. This actor **behaves like a consultant who makes decisions**: it links every finding to an actionable step. Managers don't read the report and ask "so what do I do?" — the plan is already in their hands. ### Input | Field | Description | |---|---| | `reviews` | The review list: plain strings OR `{text, rating}` objects. A review-scraper dataset can be connected directly. | | `businessName` | Business name (used as report context) | | `language` | Output language of the analysis content: English / Türkçe / Deutsch / Español / Français | | `model` | (Advanced) LLM model — the default is fast and economical | ### Output Delivered in two layers: - **Dataset (table):** Prioritized **action plan** rows — columns: `Priority` · `Issue / Area` · `Risk of Inaction` · `Risk Level` · `Benefit` · `Cost` · `Benefit/Cost` · `Score` · `Recommended Action`. Scan, sort, and export directly. - **Report (key-value store → `MANAGER_REPORT`):** Full narrative + machine-readable data with English keys: `overall_sentiment` · `themes` · `top_complaints` · `top_praises` · `competitor_opportunities` · `critical_step` · `quick_wins` · `executive_summary` · `rating_distribution` · `review_count` · `all_actions` (including more than what is shown in the main table). ### How to use 1. Collect your reviews with a review-scraper (or paste in your own list). 2. Connect the output to this actor as `reviews`. 3. Set the business name and language → **Start**. 4. Grab your action plan from the Dataset tab. ### Pricing (Pay-Per-Event) You only pay for what you use: per run + per 100 reviews processed + per generated report. No monthly subscription. # Actor input Schema ## `reviews` (type: `array`): The Yelp business reviews to analyze. Provide an array of plain strings OR {text, rating} objects. You can connect a Yelp review-scraper's dataset output directly (map the review text field to 'text'). ## `businessName` (type: `string`): The business name. Used in the report title and as context for the analysis. ## `domainContext` (type: `string`): Local-business context that guides theme extraction. Leave as-is for restaurants/local venues; edit only for a different business type. ## `language` (type: `string`): Language of the analysis content (sentiment and action text). ## `model` (type: `string`): LLM model. The default is cheap and fast; pick a stronger model for deeper analysis. ## `maxAksiyon` (type: `integer`): How many prioritized actions to show in the main table (for focus). All of them are always kept in the report (MANAGER_REPORT). Range 3-10. ## Actor input object example ```json { "reviews": [ { "text": "Food was delicious and the patio is lovely, but we waited 25 minutes just to be seated with a half-empty dining room.", "rating": 3 }, { "text": "The staff were so warm and welcoming, remembered our names from last time. This is why we keep coming back.", "rating": 5 }, { "text": "Prices have crept up a lot and portions got smaller. Not the value it used to be.", "rating": 2 }, { "text": "Tables were sticky and the restroom clearly had not been cleaned. Great food ruined by poor hygiene.", "rating": 2 }, { "text": "Fast, friendly service and the best coffee in the neighborhood. Cozy atmosphere, perfect for working.", "rating": 5 } ], "businessName": "This business", "domainContext": "This is a local business (restaurant / cafe / salon / shop) reviewed on Yelp. Pay special attention to local-business dimensions: service quality & speed, staff friendliness, food / product quality, atmosphere & ambiance, cleanliness & hygiene, pricing & value, wait times, and reservation / booking experience.", "language": "English", "model": "claude-haiku-4-5-20251001", "maxAksiyon": 5 } ```` # 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 = { "reviews": [ { "text": "Food was delicious and the patio is lovely, but we waited 25 minutes just to be seated with a half-empty dining room.", "rating": 3 }, { "text": "The staff were so warm and welcoming, remembered our names from last time. This is why we keep coming back.", "rating": 5 }, { "text": "Prices have crept up a lot and portions got smaller. Not the value it used to be.", "rating": 2 }, { "text": "Tables were sticky and the restroom clearly had not been cleaned. Great food ruined by poor hygiene.", "rating": 2 }, { "text": "Fast, friendly service and the best coffee in the neighborhood. Cozy atmosphere, perfect for working.", "rating": 5 } ] }; // Run the Actor and wait for it to finish const run = await client.actor("enezli/yelp-review-intelligence").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 = { "reviews": [ { "text": "Food was delicious and the patio is lovely, but we waited 25 minutes just to be seated with a half-empty dining room.", "rating": 3, }, { "text": "The staff were so warm and welcoming, remembered our names from last time. This is why we keep coming back.", "rating": 5, }, { "text": "Prices have crept up a lot and portions got smaller. Not the value it used to be.", "rating": 2, }, { "text": "Tables were sticky and the restroom clearly had not been cleaned. Great food ruined by poor hygiene.", "rating": 2, }, { "text": "Fast, friendly service and the best coffee in the neighborhood. Cozy atmosphere, perfect for working.", "rating": 5, }, ] } # Run the Actor and wait for it to finish run = client.actor("enezli/yelp-review-intelligence").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 '{ "reviews": [ { "text": "Food was delicious and the patio is lovely, but we waited 25 minutes just to be seated with a half-empty dining room.", "rating": 3 }, { "text": "The staff were so warm and welcoming, remembered our names from last time. This is why we keep coming back.", "rating": 5 }, { "text": "Prices have crept up a lot and portions got smaller. Not the value it used to be.", "rating": 2 }, { "text": "Tables were sticky and the restroom clearly had not been cleaned. Great food ruined by poor hygiene.", "rating": 2 }, { "text": "Fast, friendly service and the best coffee in the neighborhood. Cozy atmosphere, perfect for working.", "rating": 5 } ] }' | apify call enezli/yelp-review-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=enezli/yelp-review-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Yelp Review Intelligence", "description": "Yelp review analysis for local business owners. Turns reviews into an owner-ready action plan: sentiment, recurring themes (service, staff, food/product, atmosphere, cleanliness, value) and prioritized fixes that lift your rating.", "version": "0.1", "x-build-id": "PL5g00bRzCgywYfd2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/enezli~yelp-review-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-enezli-yelp-review-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/enezli~yelp-review-intelligence/runs": { "post": { "operationId": "runs-sync-enezli-yelp-review-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/enezli~yelp-review-intelligence/run-sync": { "post": { "operationId": "run-sync-enezli-yelp-review-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "reviews" ], "properties": { "reviews": { "title": "Yelp reviews", "type": "array", "description": "The Yelp business reviews to analyze. Provide an array of plain strings OR {text, rating} objects. You can connect a Yelp review-scraper's dataset output directly (map the review text field to 'text')." }, "businessName": { "title": "Business name", "type": "string", "description": "The business name. Used in the report title and as context for the analysis.", "default": "This business" }, "domainContext": { "title": "Analysis focus (advanced)", "type": "string", "description": "Local-business context that guides theme extraction. Leave as-is for restaurants/local venues; edit only for a different business type.", "default": "This is a local business (restaurant / cafe / salon / shop) reviewed on Yelp. Pay special attention to local-business dimensions: service quality & speed, staff friendliness, food / product quality, atmosphere & ambiance, cleanliness & hygiene, pricing & value, wait times, and reservation / booking experience." }, "language": { "title": "Output language", "enum": [ "English", "Türkçe", "Deutsch", "Español", "Français" ], "type": "string", "description": "Language of the analysis content (sentiment and action text).", "default": "English" }, "model": { "title": "Model (advanced)", "enum": [ "claude-haiku-4-5-20251001", "claude-sonnet-4-6" ], "type": "string", "description": "LLM model. The default is cheap and fast; pick a stronger model for deeper analysis.", "default": "claude-haiku-4-5-20251001" }, "maxAksiyon": { "title": "Actions shown in the table", "minimum": 3, "maximum": 10, "type": "integer", "description": "How many prioritized actions to show in the main table (for focus). All of them are always kept in the report (MANAGER_REPORT). Range 3-10.", "default": 5 } } }, "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 } } } } } } } } } } ```