Multimodal sentiment analysis, which integrates textual, auditory, and visual data, has emerged as a transformative approach to understanding human emotions and enhancing mental health care. This study proposes an innovative framework to address limitations in conventional sentiment analysis by leveraging advanced algorithms in natural language processing, computer vision, and machine learning. Utilizing point-light displays (PLDs), the research investigates deficits in body language perception, uncovering significant challenges in interpreting emotional cues that reflect broader social cognitive impairments. Task analysis plays a pivotal role in identifying and addressing these deficits, facilitating a more profound comprehension of emotional and social dynamics. Dimensionality reduction techniques are employed to handle complex multimodal datasets efficiently, ensuring accurate and actionable insights. By integrating data from diverse sources, this framework aspires to refine diagnostic methodologies, enable personalized interventions, and advance theoretical and practical knowledge in mental health care. The proposed approach bridges critical gaps in sentiment analysis, offering promising pathways for improving emotional and social outcomes for individuals facing mental health challenges.