# Review Aggregator & Sentiment Dataset Builder (`express_kingfisher/review-sentiment-dataset-builder`) Actor Scrape product reviews from public pages and build AI-ready sentiment datasets. Extract themes, pros/cons, complaint categories, and ratings. - **URL**: https://apify.com/express\_kingfisher/review-sentiment-dataset-builder.md - **Developed by:** [Prince Raj](https://apify.com/express_kingfisher) (community) - **Categories:** Developer tools, AI, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Review Aggregator & Sentiment Dataset Builder Aggregate public reviews and build AI-ready sentiment datasets. Built for product teams, local businesses, and agencies who need to analyze customer feedback at scale. ### What It Extracts - **Review data**: Text, rating, date, author display name, review URL - **Sentiment classification**: Positive, negative, or neutral - **Keyword themes**: Top themes and topics from review content - **Platform detection**: Auto-detect review platform (Trustpilot, G2, Yelp, Google) - **Review statistics**: Average rating, sentiment distribution, review count ### Why It's Better Combines review extraction with sentiment analysis and theme extraction. Produces structured JSON output ready for AI training, dashboards, or research. Supports multiple review platforms. ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `reviewPageUrls` | string[] | required | URLs of review pages to scrape | | `maxReviews` | integer | 100 | Maximum reviews to extract (1-1000) | | `platform` | string | "auto" | Platform: auto, trustpilot, g2, yelp, google | | `includeKeywordThemes` | boolean | true | Extract keyword themes from reviews | ### Output Example ```json { "sourceUrl": "https://www.trustpilot.com/review/example.com", "platform": "trustpilot", "totalReviews": 245, "averageRating": 4.2, "reviews": [ { "text": "Great product, really helped our team...", "rating": 5, "date": "2025-01-10", "author": "John D.", "sentiment": "positive" } ], "sentimentDistribution": { "positive": 180, "neutral": 40, "negative": 25 }, "keywordThemes": [ { "theme": "customer support", "count": 45 }, { "theme": "ease of use", "count": 38 }, { "theme": "pricing", "count": 32 } ], "scrapedAt": "2025-01-15T10:30:00Z" } ```` ### Use Cases - **Product research**: Analyze customer sentiment across review platforms - **Competitive analysis**: Compare review sentiment with competitors - **Customer feedback analysis**: Identify common themes and pain points - **AI training data**: Build sentiment datasets for ML models - **Reputation monitoring**: Track review sentiment over time ### PPE Pricing | Event | Description | Suggested Price | |-------|-------------|-----------------| | `review-extracted` | One review extracted | $0.002 | | `review-analyzed` | Review with sentiment analysis | $0.003 | ### Limitations - Only extracts publicly visible reviews - Sentiment classification is keyword-based (not ML-powered) - Some platforms may block automated scraping - Author names may be anonymized on some platforms - Review dates may not always be available ### Legal/Ethical Use This actor only extracts publicly visible reviews. It does not access private data, bypass authentication, or store unnecessary personal information. Users are responsible for compliance with platform terms of service and applicable data protection laws. ### Local Run ```bash cd actors/review-sentiment-dataset-builder apify run --input-file .actor/sample_input.json ``` ### Deploy ```bash cd actors/review-sentiment-dataset-builder apify push ``` ### FAQ **Q: Which review platforms are supported?** A: Trustpilot, G2, Yelp, and Google Reviews. Auto-detection works for most standard review page formats. **Q: How accurate is sentiment analysis?** A: It uses keyword-based classification, which works well for clearly positive/negative reviews. Nuanced sentiment may need manual review. **Q: Can I extract reviews from multiple pages?** A: Yes, provide multiple URLs. Each URL is processed independently. ### Tags review scraper, sentiment analysis, customer feedback, review aggregator, Trustpilot scraper, G2 reviews, Yelp reviews, product reviews, AI training data # Actor input Schema ## `reviewPageUrls` (type: `array`): URLs of pages containing reviews (product pages, review sites, etc.) ## `maxReviews` (type: `integer`): Maximum number of reviews to extract per URL. ## `platform` (type: `string`): Review platform type for optimized extraction. ## `includeKeywordThemes` (type: `boolean`): Identify common themes and topics from review text. ## Actor input object example ```json { "reviewPageUrls": [ "https://www.trustpilot.com/review/apify.com" ], "maxReviews": 50, "platform": "auto", "includeKeywordThemes": true } ``` # 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 = { "reviewPageUrls": [ "https://www.trustpilot.com/review/apify.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("express_kingfisher/review-sentiment-dataset-builder").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 = { "reviewPageUrls": ["https://www.trustpilot.com/review/apify.com"] } # Run the Actor and wait for it to finish run = client.actor("express_kingfisher/review-sentiment-dataset-builder").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 '{ "reviewPageUrls": [ "https://www.trustpilot.com/review/apify.com" ] }' | apify call express_kingfisher/review-sentiment-dataset-builder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=express_kingfisher/review-sentiment-dataset-builder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Review Aggregator & Sentiment Dataset Builder", "description": "Scrape product reviews from public pages and build AI-ready sentiment datasets. Extract themes, pros/cons, complaint categories, and ratings.", "version": "1.0", "x-build-id": "xKQHgdladdj9o2TkX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/express_kingfisher~review-sentiment-dataset-builder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-express_kingfisher-review-sentiment-dataset-builder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/express_kingfisher~review-sentiment-dataset-builder/runs": { "post": { "operationId": "runs-sync-express_kingfisher-review-sentiment-dataset-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/express_kingfisher~review-sentiment-dataset-builder/run-sync": { "post": { "operationId": "run-sync-express_kingfisher-review-sentiment-dataset-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "reviewPageUrls" ], "properties": { "reviewPageUrls": { "title": "Review page URLs", "maxItems": 20, "type": "array", "description": "URLs of pages containing reviews (product pages, review sites, etc.)", "items": { "type": "string" } }, "maxReviews": { "title": "Max reviews per page", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of reviews to extract per URL.", "default": 50 }, "platform": { "title": "Platform type", "enum": [ "auto", "trustpilot", "g2", "capterra", "google", "yelp", "generic" ], "type": "string", "description": "Review platform type for optimized extraction.", "default": "auto" }, "includeKeywordThemes": { "title": "Extract keyword themes", "type": "boolean", "description": "Identify common themes and topics from review text.", "default": true } } }, "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 } } } } } } } } } } ```