CLOUD-BASED AI AND MULTIVARIATE OPTIMIZATION METHODS FOR REAL-TIME SENTIMENT ANALYSIS ON SOCIAL MEDIA

Social media has emerged as a widely used platform for individuals and businesses to share updates, opinions, and emotions. Real-time sentiment analysis of social media data provides valuable insights, enabling organizations to make informed, data-driven decisions. However, analyzing vast amounts of social media data in real-time presents significant challenges, requiring high computational power and advanced analytical capabilities. This is where cloud-based AI and multivariate optimization techniques become essential. Cloud-based AI leverages the scalability and speed of cloud computing to process large volumes of data efficiently in real-time. The multivariate optimization model enhances the analysis by handling complex, diverse datasets and evaluating multiple variables simultaneously. This research focuses on delivering a unified framework that performs real-time sentiment analysis, and the system integrates cloud-based AI with multivariate optimization strategies to automatically collect, process, and analyze social media data in real-time, delivering actionable insights with improved accuracy and efficiency.