Security Aspects of Blockchain Technology

Abstract

Recently, there has been a notable increase in the advancement of multimodal emotion analysis systems. These systems try to get a comprehensive knowledge of human emotions by combining data from several sources, including text, voice, video, and images. This complete strategy tackles the constraints of text-only sentiment analysis, which could disregard subtle emotional expressions. This chapter examines the difficulties and approaches related to analyzing emotions utilizing many modes of data, specifically emphasizing combining data, extracting features, and ensuring scalability. This underscores the significance of creating strong fusion techniques and network architectures to integrate various data modalities efficiently. The research also explores the utilization of these systems in domains such as social media sentiment analysis and clinical evaluations, showcasing their capacity to improve decision-making and user experiences.