A comprehensive survey on multimodal sentiment analysis: Techniques, models, and applications

Multimodal sentiment analysis (MSA) is an evolving field that integrates information from multiple modalities such as text, audio, and visual data to analyze and interpret human emotions and sentiments. This review provides an extensive survey of the current state of multimodal sentiment analysis, highlighting fundamental concepts, popular datasets, techniques, models, challenges, applications, and future trends. By examining existing research and methodologies, this paper aims to present a cohesive understanding of MSA, Multimodal sentiment analysis (MSA) integrates data from text, audio, and visual sources, each contributing unique insights that enhance the overall understanding of sentiment. Textual data provides explicit content and context, audio data captures the emotional tone through speech characteristics, and visual data offers cues from facial expressions and body language. Despite these strengths, MSA faces limitations such as data integration challenges, computational complexity, and the scarcity of annotated multimodal datasets. Future directions include the development of advanced fusion techniques, real-time processing capabilities, and explainable AI models. These advancements will enable more accurate and robust sentiment analysis, improve user experiences, and enhance applications in human-computer interaction, healthcare, and social media analysis. By addressing these challenges and leveraging diverse data sources, MSA has the potential to revolutionize sentiment analysis and drive positive outcomes across various domains.