# Shopee Reviews & Buyer Feedback Analyzer (`quanmatrix/shopee-reviews-buyer-feedback-scraper`) Actor Analyze Shopee review datasets to detect buyer pain points, product-quality defects, delivery issues, sentiment and high-priority feedback signals for product teams, monitoring and AI agents. - **URL**: https://apify.com/quanmatrix/shopee-reviews-buyer-feedback-scraper.md - **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community) - **Categories:** E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.03 / 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/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. 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. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## 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. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## Shopee Reviews and Buyer Feedback Analyzer Analyze Shopee review datasets to detect buyer pain points, product-quality defects, delivery issues, sentiment and high-priority feedback signals for product teams, monitoring and AI agents. ### Why use this Actor Analyze Shopee review datasets to detect buyer pain points, quality defects, delivery issues, sentiment and high-priority product signals for AI agents and product teams. ### Key features - Buyer-native inputs aligned to **Shopee review analyzer**. - Predictable structured output in the default Apify Dataset. - Pay Per Event pricing tied to measurable result rows or reports. - Optional MCP connector delivery for downstream automation and AI agents. ### Quick start Start with this working example and replace the target values with your own: ```json { "currentItems": [ { "reviewId": "r1", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Battery stopped charging after two days. Very disappointed.", "rating": 1, "likeCount": 38, "variation": "Black" }, { "reviewId": "r2", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Great sound and fast delivery, highly recommend.", "rating": 5, "likeCount": 14, "sellerReply": "Thank you!" }, { "reviewId": "r3", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Delivery was late and the box was damaged.", "rating": 2, "likeCount": 22 } ], "maxItems": 20000 } ``` The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows. ### Input - `currentItems` — Current source records: Inline review/comment rows to analyze. Use this or currentDatasetId. - `currentDatasetId` — Current Apify Dataset ID: Dataset from an upstream extraction workflow. - `maxItems` — Maximum records to analyze: Maximum number of supplied Dataset records analyzed and written to the output Dataset. - `payload` — Apify integration payload: Actor-to-Actor integration payload. payload.resource.defaultDatasetId is accepted. - `previousDatasetId` — Previous Apify Dataset ID: Optional previous Dataset for period-over-period baseline comparison. All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline. ### Output The Dataset exposes predictable machine-readable output. Representative fields include `results`. ### Pricing This Actor uses Pay Per Event. The current factory base price is **$0.002900 per result event**. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer. ### Use cases - Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first. - Use the Actor from API or schedules for recurring collection, comparison or monitoring. - Feed the structured Dataset output into spreadsheets, databases, automations or AI agents. - Compare repeat runs when the product supports snapshots or previous-run inputs. ### Automation and AI Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste. ### Limitations - Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice. - Fields that are not publicly available are returned as unavailable or omitted rather than fabricated. - Analytical outputs depend on the quality and coverage of the supplied or collected source data. - Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence. ### Detailed documentation ### Detailed documentation ### Detailed documentation ### Detailed documentation ### Detailed documentation ### Detailed documentation ### Detailed documentation ### Detailed documentation ### Detailed documentation **GEN6 Premium** dataset-first intelligence for reliable automation. This Actor intentionally analyzes supplied or upstream-collected data instead of attempting to bypass source-platform access controls. ### Quick start Paste source rows into `currentItems`, or select an upstream Apify Dataset in `currentDatasetId`, then run. ### Input - `currentItems`: inline source rows. - `currentDatasetId`: upstream Apify Dataset. - `maxItems`: analysis cap. - Optional MCP connector fields for safe downstream delivery. ### Output Every Dataset row preserves the source record and adds deterministic intelligence including signal score, classification and `agentAction`. An aggregate `INTELLIGENCE_REPORT` is also written to the key-value store. ### Pricing Pay Per Event. The primary event is one normalized, scored and decision-ready output row. ### Automation & AI Designed for scheduled pipelines, Actor-to-Actor Dataset workflows, MCP connectors and AI agents. The explicit action fields avoid forcing an agent to reinterpret raw feedback before deciding what deserves attention. ### Reliability Source completeness depends on the supplied Dataset. This design removes brittle anonymous source access from the execution contract and makes runs deterministic, testable and repeatable. # Changelog This Actor's version history is a separate document: https://apify.com/quanmatrix/shopee-reviews-buyer-feedback-scraper/changelog.md # Actor input Schema ## `currentItems` (type: `array`): Inline review/comment rows to analyze. Use this or currentDatasetId. ## `currentDatasetId` (type: `string`): Dataset from an upstream extraction workflow. ## `maxItems` (type: `integer`): Maximum number of supplied Dataset records analyzed and written to the output Dataset. ## `payload` (type: `object`): Actor-to-Actor integration payload. payload.resource.defaultDatasetId is accepted. ## `mcpConnectors` (type: `array`): Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service. ## `mcpActionPreset` (type: `string`): Choose a safe action pattern. AUTO_SAFE_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery. ## `mcpToolName` (type: `string`): Exact MCP tool to call after the Actor produces its result, for example send_message, create_issue, or create_page. Leave blank when no external action should run. ## `mcpToolArguments` (type: `object`): JSON object passed to the selected MCP tool. String values may use {{actor_title}}, {{result_summary}}, or {{result_json}} placeholders. ## `mcpFailOnError` (type: `boolean`): When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable. ## `previousItems` (type: `array`): Optional previous review/comment rows for baseline comparison and change intelligence. ## `previousDatasetId` (type: `string`): Optional previous Dataset for period-over-period baseline comparison. ## Actor input object example ```json { "currentItems": [ { "reviewId": "r1", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Battery stopped charging after two days. Very disappointed.", "rating": 1, "likeCount": 38, "variation": "Black" }, { "reviewId": "r2", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Great sound and fast delivery, highly recommend.", "rating": 5, "likeCount": 14, "sellerReply": "Thank you!" }, { "reviewId": "r3", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Delivery was late and the box was damaged.", "rating": 2, "likeCount": 22 } ], "maxItems": 20000, "mcpActionPreset": "AUTO_SAFE_WRITE", "mcpToolName": "", "mcpToolArguments": {}, "mcpFailOnError": false } ``` # Actor output Schema ## `results` (type: `string`): Normalized source rows from this Actor. # 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 = { "currentItems": [ { "reviewId": "r1", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Battery stopped charging after two days. Very disappointed.", "rating": 1, "likeCount": 38, "variation": "Black" }, { "reviewId": "r2", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Great sound and fast delivery, highly recommend.", "rating": 5, "likeCount": 14, "sellerReply": "Thank you!" }, { "reviewId": "r3", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Delivery was late and the box was damaged.", "rating": 2, "likeCount": 22 } ] }; // Run the Actor and wait for it to finish const run = await client.actor("quanmatrix/shopee-reviews-buyer-feedback-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 = { "currentItems": [ { "reviewId": "r1", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Battery stopped charging after two days. Very disappointed.", "rating": 1, "likeCount": 38, "variation": "Black", }, { "reviewId": "r2", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Great sound and fast delivery, highly recommend.", "rating": 5, "likeCount": 14, "sellerReply": "Thank you!", }, { "reviewId": "r3", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Delivery was late and the box was damaged.", "rating": 2, "likeCount": 22, }, ] } # Run the Actor and wait for it to finish run = client.actor("quanmatrix/shopee-reviews-buyer-feedback-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "currentItems": [ { "reviewId": "r1", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Battery stopped charging after two days. Very disappointed.", "rating": 1, "likeCount": 38, "variation": "Black" }, { "reviewId": "r2", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Great sound and fast delivery, highly recommend.", "rating": 5, "likeCount": 14, "sellerReply": "Thank you!" }, { "reviewId": "r3", "productUrl": "https://shopee.sg/product-i.1.101", "content": "Delivery was late and the box was damaged.", "rating": 2, "likeCount": 22 } ] }' | apify call quanmatrix/shopee-reviews-buyer-feedback-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/shopee-reviews-buyer-feedback-scraper" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/ruZ2Awpio5N9h0fQz/builds/ODfOHBGk7xWMUvoh4/openapi.json