# Trustpilot Review Intelligence Monitor (`geneius/trustpilot-review-intelligence-monitor`) Actor Turn Trustpilot reviews into complaint themes, praise themes, urgency scores, and owner-response actions. - **URL**: https://apify.com/geneius/trustpilot-review-intelligence-monitor.md - **Developed by:** [Gene](https://apify.com/geneius) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $4.00 / 1,000 trustpilot review analyzeds 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 ### Trustpilot Review Intelligence Monitor Turn raw Trustpilot company reviews into **complaint themes, evidence quotes, urgency scores, public-response drafts, and client-report bullets**. This Actor analyzes Trustpilot review records collected by another Apify Actor or supplied inline; it does not scrape Trustpilot directly. Instead of returning another spreadsheet of review text, it gives brand, ecommerce, and agency teams a clean action dataset: what customers complain about, what they praise, which reviews need attention, and what to do next. ### Workflow Hub See the public [review intelligence workflow](https://geneiusk.github.io/apify-actor-powerhouse-hub/review-intelligence.html) for the scraper dataset -> analyzer path and links across the review-intelligence Actors. For the first run, use the [Trustpilot review tutorial](https://geneiusk.github.io/apify-actor-powerhouse-hub/trustpilot-review-tutorial.html) or the [Trustpilot Review Intelligence alternatives page](https://geneiusk.github.io/apify-actor-powerhouse-hub/trustpilot-review-intelligence-monitor-alternatives.html). The [proof GIF](https://geneiusk.github.io/apify-actor-powerhouse-hub/assets/trustpilot-review-proof.gif) shows reviews becoming a reputation queue. ### What You Learn - Which reviews are positive, negative, or mixed - Which themes keep appearing: support, refunds, delivery, pricing, product quality, trust, and experience - Which complaints deserve urgent follow-up - Which positive reviews can become Trustpilot widget copy, landing-page proof, or ad proof - Which exact quote explains the theme and priority score - Which reply draft or workflow bucket to use - What action to take for each review ### Use Cases - Weekly Trustpilot reputation monitoring for brands and agencies - Refund, delivery, and support complaint triage - Public-response prioritization for reputation teams - Brand-proof mining from positive Trustpilot reviews - Review exports to Google Sheets, Slack, dashboards, or reporting decks ### Input You can provide reviews inline or pass an Apify `datasetId` from another review scraper. ```json { "businessName": "Northstar Software", "sourceName": "Trustpilot", "reviews": [ { "rating": 1, "text": "Support ignored my refund request after a delayed delivery.", "authorName": "Example Customer", "date": "2026-05-01" } ], "maxReviews": 100, "includeRawReview": true } ```` ### Output Each dataset item is one analyzed review: ```json { "status": "succeeded", "recordIndex": 1, "billingEventName": "trustpilot-review-analyzed", "businessName": "Northstar Software", "sourceName": "Trustpilot", "rating": 1, "sentimentLabel": "negative", "sentimentScore": -100, "detectedThemes": ["support", "refunds", "delivery"], "complaintThemes": ["support", "refunds", "delivery"], "urgencyScore": 95, "priorityReason": "Money-related complaint is likely to escalate without follow-up.", "workflowCategory": "urgent_public_response", "proofQuote": "Support ignored my refund request after a delayed delivery.", "replyNeeded": true, "ownerResponseDraft": "Thank you for raising this. We are sorry the refunds experience with Northstar Software fell short...", "recommendedAction": "Escalate refund and cancellation complaints for Northstar Software; these reviews can become visible trust blockers.", "analyzedAt": "2026-05-12T12:00:00+00:00" } ``` The run also writes a `SUMMARY` key-value-store record with analyzed counts, sentiment counts, top complaint themes, top urgent reviews, praise quotes, client-report bullets, and the charge event name. ### FAQ #### Does this scrape Trustpilot? No. It analyzes Trustpilot review records you provide inline or through an Apify dataset from another Actor. It is an intelligence layer, not a Trustpilot scraper. #### What input do I need for the first run? Use the Store example with one or more review records. At minimum, each record should include review text, and ratings improve sentiment and urgency scoring. #### What do I get back? One dataset item per analyzed review, including sentiment, complaint themes, urgency, proof quote, public-response draft, and recommended next action. The run also writes a `SUMMARY` record. #### Can I use data from another review scraper? Yes. Pass a `datasetId` from another Actor or paste inline records. The analyzer recognizes common review fields such as `text`, `reviewText`, `rating`, `stars`, `authorName`, `date`, `reviewUrl`, `companyName`, and `companyDomain`. #### How much does it cost? The configured paid event is `trustpilot-review-analyzed` at `$0.004` per successfully analyzed review. ### Pricing Default monetization model: pay per event. Recommended chargeable event: - Event name: `trustpilot-review-analyzed` - Event meaning: one successfully analyzed review - Store price: `$0.004` per analyzed review - Pricing status: active from `2026-05-13T19:04:19Z`; verified by private and public paid smokes Successful rows are pushed only after the charge path allows the event. If `Actor.charge` fails, the Actor fails closed before returning paid output. ### Limitations - This MVP analyzes review records; it does not scrape Trustpilot directly. - Review source schemas vary. The Actor recognizes common fields such as `text`, `reviewText`, `rating`, `stars`, `authorName`, `date`, `reviewUrl`, `companyName`, and `companyDomain`. - Theme detection is deterministic and intentionally explainable. It is built for reliable monitoring, not black-box sentiment theater. ### Automation And Agent Use - Run a review scraper first, then pass its dataset ID to this Actor. - Schedule weekly analysis for each brand, product line, or Trustpilot domain. - Send negative high-urgency reviews to Slack or a ticketing queue. - Append `sentimentLabel`, `detectedThemes`, and `recommendedAction` to Google Sheets for client reporting. ### Local Development ```bash python3 -m pip install -r requirements.txt ACTOR_TEST_PAY_PER_EVENT=true apify run --purge --input-file examples/smoke-input.json ``` # Actor input Schema ## `businessName` (type: `string`): Company, brand, domain, or client name used in recommendations. ## `sourceName` (type: `string`): Source label for the input reviews, such as Trustpilot. ## `reviews` (type: `array`): Optional review records. Each record should include review text. Use this or datasetId; if both are supplied, inline records are analyzed first and dataset records are appended. ## `datasetId` (type: `string`): Optional Apify dataset ID containing Trustpilot review records from another Actor. Provide this or inline reviews. ## `maxReviews` (type: `integer`): Safety cap for analyzed reviews in one run. ## `datasetItemLimit` (type: `integer`): Maximum number of records to load from the source dataset. ## `includeRawReview` (type: `boolean`): When enabled, dataset items include the original review text. ## `failOnInvalidRecord` (type: `boolean`): When enabled, the Actor fails on the first invalid review record instead of returning partial error records. ## Actor input object example ```json { "businessName": "Northstar Software", "sourceName": "Trustpilot", "reviews": [ { "rating": 1, "text": "Support ignored my refund request after a delayed delivery.", "authorName": "Example Customer", "date": "2026-05-01" } ], "maxReviews": 100, "datasetItemLimit": 100, "includeRawReview": true, "failOnInvalidRecord": false } ``` # Actor output Schema ## `results` (type: `string`): Dataset items with one row per analyzed review, including sentiment, urgency, themes, and recommended action. ## `summary` (type: `string`): Run-level summary with analyzed review count, failures, charge event name, sentiment/theme counts, top urgent reviews, praise quotes, and client-report bullets. # 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 = { "businessName": "Northstar Software", "sourceName": "Trustpilot", "reviews": [ { "rating": 1, "text": "Support ignored my refund request after a delayed delivery.", "authorName": "Example Customer", "date": "2026-05-01" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("geneius/trustpilot-review-intelligence-monitor").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 = { "businessName": "Northstar Software", "sourceName": "Trustpilot", "reviews": [{ "rating": 1, "text": "Support ignored my refund request after a delayed delivery.", "authorName": "Example Customer", "date": "2026-05-01", }], } # Run the Actor and wait for it to finish run = client.actor("geneius/trustpilot-review-intelligence-monitor").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 '{ "businessName": "Northstar Software", "sourceName": "Trustpilot", "reviews": [ { "rating": 1, "text": "Support ignored my refund request after a delayed delivery.", "authorName": "Example Customer", "date": "2026-05-01" } ] }' | apify call geneius/trustpilot-review-intelligence-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=geneius/trustpilot-review-intelligence-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Trustpilot Review Intelligence Monitor", "description": "Turn Trustpilot reviews into complaint themes, praise themes, urgency scores, and owner-response actions.", "version": "0.1", "x-build-id": "3t6AkWhCFQeWBC6fm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/geneius~trustpilot-review-intelligence-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-geneius-trustpilot-review-intelligence-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/geneius~trustpilot-review-intelligence-monitor/runs": { "post": { "operationId": "runs-sync-geneius-trustpilot-review-intelligence-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/geneius~trustpilot-review-intelligence-monitor/run-sync": { "post": { "operationId": "run-sync-geneius-trustpilot-review-intelligence-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "businessName": { "title": "Company or brand name", "type": "string", "description": "Company, brand, domain, or client name used in recommendations." }, "sourceName": { "title": "Review source", "type": "string", "description": "Source label for the input reviews, such as Trustpilot.", "default": "Trustpilot" }, "reviews": { "title": "Inline review records", "minItems": 1, "type": "array", "description": "Optional review records. Each record should include review text. Use this or datasetId; if both are supplied, inline records are analyzed first and dataset records are appended." }, "datasetId": { "title": "Source dataset ID", "minLength": 1, "type": "string", "description": "Optional Apify dataset ID containing Trustpilot review records from another Actor. Provide this or inline reviews." }, "maxReviews": { "title": "Maximum reviews", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Safety cap for analyzed reviews in one run.", "default": 100 }, "datasetItemLimit": { "title": "Dataset item limit", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of records to load from the source dataset.", "default": 100 }, "includeRawReview": { "title": "Include raw review text", "type": "boolean", "description": "When enabled, dataset items include the original review text.", "default": true }, "failOnInvalidRecord": { "title": "Fail on invalid record", "type": "boolean", "description": "When enabled, the Actor fails on the first invalid review record instead of returning partial error records.", "default": false } } }, "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 } } } } } } } } } } ```