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:

{
  "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.