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, 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.

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.

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).

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.

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.

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.

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.

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.