Multimodal Sentiment

Multimodal sentiment analysis aims to understand human emotions by integrating information from various sources like text, images, and audio, surpassing the limitations of text-only sentiment analysis. Current research focuses on improving the fusion of these modalities, exploring techniques like transformers, contrastive learning, and Bayesian methods to address challenges such as weak inter-modality correlations, missing data, and dataset biases. This field is significant for its potential to enhance applications ranging from social media monitoring and advertising effectiveness to mental health assessment and human-computer interaction, offering more nuanced and accurate emotion detection than unimodal approaches.