# E-commerce Review Sentiment Analysis and Intelligent Recommendation System: Full-Stack Practice from Data to Decision-Making

> This article introduces an e-commerce review analysis system based on FastAPI and React, demonstrating how to implement user review sentiment analysis, intelligent product recommendations, and complete user permission management functions.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-27T16:44:55.000Z
- 最近活动: 2026-05-27T16:49:08.423Z
- 热度: 163.9
- 关键词: FastAPI, React, 情感分析, 推荐系统, 电商, 自然语言处理, 全栈开发, Python, 机器学习, 用户管理
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-tuanngo-02-analysis-system-of-tiki
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-tuanngo-02-analysis-system-of-tiki
- Markdown 来源: floors_fallback

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## [Introduction] Full-Stack Practice of E-commerce Review Sentiment Analysis and Intelligent Recommendation System

This article introduces an e-commerce review analysis system based on FastAPI and React, which implements user review sentiment analysis, intelligent product recommendations, and complete user permission management functions. The project uses a modern tech stack to solve the pain point of manual analysis of massive e-commerce reviews, and can be directly applied to real e-commerce scenarios to provide data-driven decision support for merchants, platforms, and consumers.

## Project Background and Business Value

In the e-commerce field, user reviews are valuable assets, but manual analysis of massive reviews is time-consuming and prone to missing key information. Designed to address this need, this project is a fully functional full-stack web application that can automatically analyze the sentiment tendency of reviews (positive/negative/neutral), recommend similar products based on the results, and includes user authentication, permission management, and data statistics functions, which can be directly implemented in real scenarios.

## System Architecture and Technology Selection

**Architecture**: Separation of front-end and back-end design, decoupling the interface from business logic to improve development efficiency and maintainability.

**Backend**: FastAPI framework (asynchronous support, automatic documentation, type safety), SQLAlchemy ORM (database operations), SQLite (lightweight database), JWT+Passlib+Bcrypt (security authentication).

**Frontend**: React 19 (componentization, virtual DOM, rich ecosystem), React Router (routing navigation).

## Detailed Explanation of Core Functions

### Sentiment Analysis Engine
Accepts e-commerce product links (supports Tiki, Shopee, etc.), automatically crawls reviews, analyzes sentiment tendencies (including category, confidence level, statistical indicators), helps merchants understand user satisfaction and identify product pain points.

### Intelligent Recommendation
Based on sentiment analysis results, recommends similar products through logic such as category matching, price range, user preferences, and sentiment weighting, improving user experience and cross-selling opportunities for the platform.

### User Management
Implements an RBAC system: Regular users can use sentiment analysis, recommendations, and view personal statistics; administrators can view the overview dashboard, manage users, view global data, and monitor system status.

## Development Practice and Engineering Standards

**Project Structure**: Clear layering—backend includes core (configuration), model (data model), routers (API routes), etc.; frontend includes components (React components), services (API calls), etc.

**API Design**: RESTful endpoints such as registration/login, sentiment analysis, recommendations, etc., following REST principles.

**Deployment Steps**: Install Python 3.8+ and Node.js 14+, start front-end and back-end services after installing dependencies, and the SQLite database and default administrator account will be automatically created on first run.

## Application Scenarios and Business Value

### Merchant Operations
Monitor product feedback, identify improvement points, and compare sentiment distribution of competitor reviews.

### Platform Operations
Provide data services to merchants, identify popular trends, and optimize search rankings.

### Consumer Decision-Making
Quickly find alternatives, assist in purchase decisions, and avoid pitfalls.

## Technical Highlights and Areas for Improvement

**Highlights**: Full-stack coverage (from database to front-end), modern tech stack (FastAPI/React19), complete functions (algorithm + user management), user-friendly documentation (detailed installation steps).

**Areas for Improvement**: Increase model transparency (e.g., integrate BERT), migrate database to PostgreSQL, introduce Redis caching, and handle comment crawling and analysis asynchronously.

## Conclusion

This system demonstrates the full process of implementing machine learning technology into product functions, covering modules required for production-level AI applications such as sentiment analysis, intelligent recommendation, and user management. It is an excellent reference case for learning full-stack development or the e-commerce AI field. Mastering the development skills of such systems will enhance developers' competitiveness in the data-driven e-commerce industry.