Multimodal Sentiment Analysis

Abstract-Multimodal sentiment analysis (MSA) integrates data from multiple sources, such as text, audio, and visual cues, to enhance the accuracy and interpretability of sentiment classification models. Traditional sentiment analysis predominantly relies on textual data, which can be limited in capturing non-verbal nuances like tone of voice or facial expressions. This paper explores the synergy between text, speech, and visual data in sentiment analysis tasks, addressing key challenges such as data alignment, feature extraction, and fusion techniques. We compare various fusion strategies, including early, late, and hybrid fusion, using state-of-the-art deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Experimental results demonstrate that multimodal approaches significantly outperform unimodal systems, providing higher accuracy and robustness in sentiment detection. We discuss the potential applications of multimodal sentiment analysis in fields such as social media monitoring, customer sentiment analysis, and healthcare. Finally, the paper outlines future research directions, emphasizing the need for more efficient fusion techniques and the incorporation of emerging models to advance multimodal sentiment analysis further.