A Study of Multimodal Sentiment Analysis and Design of an Architecture

The field of multimodal sentiment analysis is experiencing significant growth as it extends to integrating natural language processing, computer vision, and emotion detection. This method aims to facilitate machines’ capacity to comprehend and analyze emotions conveyed through diverse modalities, encompassing written language, spoken communication, visual representations, and audiovisual content. Incorporating various modalities facilitates a more extensive comprehension of human emotions, enhancing interactions between humans and computers through empathy and context awareness. This paper explores current advancements in multimodal sentiment analysis, examining the difficulties encountered, the methodologies employed, and the various applications within this domain. This study explores cross-modal fusion techniques, contextual analysis, and strategies for addressing data sparsity and missing modalities. The advancement of multimodal sentiment analysis has resulted in its widespread influence across various domains. These domains include customer service, social media analysis, healthcare, and education. Ultimately, this progress contributes to the development of emotionally intelligent interactions between humans and machines in a world that is becoming increasingly interconnected. This paper explores some challenges and future research areas in multimodal sentiment analysis.