A Comprehensive Survey on Multimodal Sentiment and Emotion Analysis in Digital Communication

Akshatha Rithesh, Hanumanthappa M

Abstract: With the significant increase in digital communication, the need for understanding human emotions in intelligent systems has gained utmost importance. It is difficult for the traditional sentiment analysis techniques to capture the complexity and subtlety of emotions expressed in online interactions. The goal of this survey paper is to understand and compare the recent advances in multimodal sentiment and emotion analysis, focusing on text, audio, visual, and other modalities to improve emotion detection. This survey encompasses recent papers and examines various methods, including deep learning, transformer-based models, and fusion strategies, as well as popular datasets such as CMU-MOSEI, IEMOCAP, and MELD. This paper discusses the strengths and weaknesses of existing approaches, identify gaps such as modality imbalance, interpretability, and cross-cultural generalization, and suggest future directions like lightweight architectures, explainable AI, and underutilized modalities like emojis and physiological signals. This paper will give a direction for researchers and practitioners to build more robust and context-aware emotion recognition systems.

Keywords: Multimodal Emotion Recognition, Sentiment Analysis, Affective Computing, Fusion Techniques, Transformer Models, Digital Communication

How to Cite?: Akshatha Rithesh, Hanumanthappa M, "A Comprehensive Survey on Multimodal Sentiment and Emotion Analysis in Digital Communication", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 1109-1114, https://www.ijsr.net/getabstract.php?paperid=SR25716112007, DOI: https://dx.doi.org/10.21275/SR25716112007