Microservices-Based System for Automated Data Collection, Sentiment Analysis, and Visualization in Video Game Reviews Across Multiple Platforms

With the video gaming industry projected to reach US$363.20bn by 2027, efficient methods to analyze user reviews across platforms become critical. This paper presents an automated multi-platform system for collecting and analyzing video game review sentiment using microservices architecture with React/Next.js frontend, FastAPI backend, and MongoDB database, integrated with Google Gemini API for natural language processing. Performance testing resulted in strong web page quality scores (100/100 Lighthouse performance) and acceptable throughput (6.98 requests/second). However, stress testing exposed bottlenecks in Microsoft Store scraping with 41% failure rates versus 0.49% for review collection. User Acceptance Testing demonstrated 94.81% success rate with 4.28/5 usefulness scores, validating system effectiveness in reducing manual review analysis effort.