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amazon-review-analyzer

Analyze Amazon review CSVs into 22-dimension tags, distribution statistics, and insight reports.

22|2|Updated Jul 20, 2026
Search Tags:#e-commerce#csv#tagging#amazon#sentiment#review analysis#insight report
One-click install
npx skills add https://github.com/buluslan/n8n-to-skill --skill amazon-review-analyzer-buluslan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: amazon-review-analyzer
Source: https://github.com/buluslan/n8n-to-skill/tree/main/examples/example-output/amazon-review-analyzer
Command: npx skills add https://github.com/buluslan/n8n-to-skill --skill amazon-review-analyzer-buluslan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Cross-border e-commerce sellers need to turn raw Amazon product reviews into structured, decision-ready insights. Manually reading hundreds of reviews to extract customer profiles, pain points, and selling points is slow and inconsistent. ## Core Features & Use Cases - 22-Dimension Tagging: Labels every review across 22 dimensions (demographics, scenario, quality, service, experience, competitor, sentiment) with strict enumeration values for comparable statistics. - Distribution Statistics: Computes count and percentage distributions for all tag columns via a deterministic Python script. - 6-Chapter Insight Report: Generates a deep analysis report covering user personas, selling points, pain points, prioritized improvements, market opportunities, and typical user profiles. - Use Case: A seller preparing a product iteration exports reviews to CSV, runs the skill, and receives tagged.csv, stats.csv, and insight.md to guide redesign and negative-review response. ## Quick Start Analyze the reviews in my file reviews.csv and generate the 22-dimension tags, tag distribution statistics, and the 6-chapter insight report.

Frequently Asked Questions about amazon-review-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze Amazon product reviews with AI?▼

Provide a UTF-8 CSV of reviews containing a text column (内容, 内容(翻译), or text). The skill tags each review across 22 dimensions, computes tag distributions with a Python script, and generates a 6-chapter insight report covering personas, pain points, and improvements.

What output files does Amazon review analysis produce?▼

It writes three files to an output directory: tagged.csv with original columns plus 22 tag columns, stats.csv with count and percentage distributions per tag, and insight.md containing the 6-chapter deep analysis report.

What CSV format is required for review tagging?▼

The CSV must be UTF-8 encoded and include a review text column named 内容, 内容(翻译), or text. Optional columns like title, rating, and VP review status are also recognized in both Chinese and English naming.

What happens if review tagging fails for some rows?▼

Failed rows are retried three times, then filled with 未提及/不明 values and marked [TAG_FAILED] without blocking the rest. Reviews over 2000 characters are truncated, and fewer than 5 reviews skips the insight report.

Does this skill require API keys or external credentials?▼

No credentials are needed. The original workflow's Gemini API key and Google Sheets OAuth were removed during conversion; LLM tagging runs natively in the agent environment and storage uses local CSV files.

When should I not use this review analysis approach?▼

It does not scrape or collect reviews in real time, so you must supply an existing CSV. It also only handles Amazon-sourced reviews and does not perform product selection analysis outside the review dimension.

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