Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource George, Jossy Title A name given to the resource Multimodal sentiment analysis: integrating text, image, and audio Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.99-115 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00017-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00017-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032930614?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032930614?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource 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. Subject The topic of the resource audio analysis; fusion methods; image analysis; Multimodal sentiment analysis; text analysis Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online