ReviewSense is an advanced AI-powered customer review intelligence system developed during a hackathon at Alva’s Institute. The project transforms unstructured customer reviews into meaningful business insights using a scalable microservices architecture. It consists of four core components: a React-based frontend dashboard for analysts and administrators, a Node.js and Express backend acting as an API gateway and real-time engine, a Python FastAPI-based AI service for natural language processing, and a Chrome Extension that extracts reviews from platforms like Google Maps. :contentReference[oaicite:0]{index=0}
The frontend is built using React 18, Vite, and TailwindCSS to provide a fast and responsive user experience. It integrates tools like Recharts for data visualization and Socket.io-client for real-time updates. The backend leverages Express.js with MongoDB for data storage and uses WebSockets for live communication. Authentication is handled using JWT, while additional tools such as Multer, Cloudinary, and Nodemailer enable file uploads, storage, and automated alerting. :contentReference[oaicite:1]{index=1}
The AI/ML service is the core of the system, built using FastAPI, PyTorch, and HuggingFace Transformers. It performs sentiment analysis, aspect-based sentiment analysis (ABSA), sarcasm detection, and multilingual translation. Advanced preprocessing techniques such as emoji expansion, slang normalization, language detection, and translation ensure accurate understanding of real-world noisy text. The system also includes a bot and spam detection engine that identifies fake or duplicate reviews using heuristic and similarity-based methods. :contentReference[oaicite:2]{index=2}
Beyond basic sentiment analysis, ReviewSense provides deeper insights through trend detection, anomaly analysis, and geo-location intelligence. It identifies emerging issues using rolling windows and statistical methods, classifies problems as systemic or isolated, and computes an overall business health score. It also performs regional analysis by mapping reviews to cities and states, enabling localized insights and alerts for better decision-making. :contentReference[oaicite:3]{index=3}
A real-time processing engine continuously monitors incoming reviews, clusters issues, and automatically escalates critical problems while notifying stakeholders through alerts. The Chrome Extension, built using Manifest V3, scrapes review data and securely sends it to the backend. Additionally, the system supports report generation in PDF and CSV formats, making it suitable for business analytics and reporting. :contentReference[oaicite:4]{index=4}
Overall, ReviewSense demonstrates how AI, real-time systems, and modern web technologies can be combined to convert raw customer feedback into actionable insights, helping organizations improve their services and customer experience. :contentReference[oaicite:5]{index=5}