Multimodal Sentiment Analysis: A Study on Emotion Understanding and Classification by Integrating Text and Images

Abstract

The advent of social media and the proliferation of multimodal content have led to the growing importance of understanding sentiment in both text and images. Traditional sentiment analysis relies heavily on textual data, but recent trends indicate that integrating visual information can significantly improve sentiment prediction accuracy. This paper explores multimodal sentiment analysis, specifically focusing on emotion understanding and classification by integrating textual and image-based features. We review existing approaches, develop a hybrid deep learning model utilizing attention mechanisms and transformer architectures for multimodal sentiment classification, and evaluate its performance on benchmark datasets, including Twitter and Instagram data. Our findings suggest that multimodal approaches outperform text-only models, especially in more nuanced sentiment cases such as sarcasm, irony, or mixed emotions. Moreover, we address key challenges like feature fusion, domain adaptation, and the contextual alignment of visual and textual information. The results provide insights into optimizing multimodal fusion techniques to enhance real-world application performance.