Multimodal Sentiment Analysis Applications in Healthcare
N. Abinaya, V.S. Harikrishnan, S. Santhiya, A. Sesili, N. V. Nithya Shree · 2025
Multimodal sentiment analysis has emerged as a promising approach in healthcare, offering new avenues for understanding patient experiences, improving care delivery, and extracting valuable insights from diverse data sources. This provides an overview of the applications of multimodal sentiment analysis in healthcare. By integrating textual, visual, and auditory cues, healthcare providers can gain deeper insights into patient emotions, satisfaction levels, and overall well-being. We explore the various modalities used in healthcare contexts, including electronic health records (EHRs), patient feedback forms, social media interactions, and wearable sensor data. Moreover, it discusses how multimodal sentiment analysis can aid in patient-centered care by enabling healthcare providers to personalize treatment plans, detect early signs of patient distress, and tailor interventions based on individual emotional responses. Additionally, examine the role of sentiment analysis in improving healthcare communication, enhancing patient–provider interactions, and identifying areas for quality improvement within healthcare organizations. Furthermore, it highlights the challenges and opportunities associated with multimodal sentiment analysis in healthcare, such as data privacy concerns, integration with existing healthcare systems, and ensuring the accuracy and reliability of sentiment analysis models in clinical settings. Despite these challenges, multimodal sentiment analysis holds great potential for transforming healthcare delivery by providing valuable insights into patient emotions, preferences, and experiences, ultimately leading to improved patient outcomes and satisfaction.