Advancements in Web Accessibility and Sentiment Analysis: A Comprehensive Review

This paper examines recent developments in web accessibility and sentiment analysis, drawing from studies on machine learning, web technologies, database systems, and optimization techniques. Web accessibility ensures that digital platforms are usable by people with disabilities, following standards like WCAG 2.1, which promotes equal access to online resources. Sentiment analysis uses machine learning to understand public opinions from sources such as social media, online reviews, and forums, providing valuable insights for businesses, governments, and communities. By reviewing key research, we identify major challenges, effective methods, and new trends in these fields, focusing on their applications in healthcare, education, and crisis management. We also explore how technologies like NoSQL databases, genetic algorithms, blockchain, and Web 3.0 improve data processing, security, and user experience. This study highlights the connection between accessibility and sentiment analysis, showing how they can work together to create fair and smart digital systems. Our goal is to provide a clear guide for students, researchers, and professionals, offering ideas for future work to build inclusive and data-driven online environments that benefit everyone