Multimodal sentiment analysis: integrating text, image, and audio
audio analysis; fusion methods; image analysis; Multimodal sentiment analysis; text analysis
Multimodal sentiment analysis aims to integrate text, images, and audio information to provide a more comprehensive understanding of human emotions and opinions. This chapter reviews key aspects of multimodal sentiment analysis, including feature extraction techniques, fusion methods, modeling approaches, and applications. For feature extraction the chapter discusses lexical, syntactic, and semantic features for text; visual attributes and facial expressions for images; and acoustic properties for audio. Three primary fusion techniques are examined: early fusion, which combines features before classification; late fusion, which integrates outputs from unimodal models; and model-based fusion, which learns joint representations across modalities. The chapter explores traditional machine learning and deep learning modeling approaches, highlighting the effectiveness of neural architectures like CNNs and RNNs. Key application areas discussed include social media analysis, emotion recognition, intelligent transportation, and education. The chapter also outlines future research directions, such as crossmodal learning, multimodal pretraining, and explainable AI. As multimodal data increases, sentiment analysis techniques that can effectively integrate information across modalities will become increasingly crucial for understanding human emotions and opinions in diverse contexts. This review provides a comprehensive overview of current approaches and emerging trends in this rapidly evolving field. 2026 Elsevier Inc. All rights reserved.
Multimodal Learning Using Heterogeneous Data;pp.99-115
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-27528-9.00017-6 https://www.scopus.com/pages/publications/105032930614?origin=resultslist
George J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India