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 Nanjundan, Preethi; Thomas, Lijo; Indu, P.V.; Eslamian, Saeid Title A name given to the resource Multimodal data analytics for social media and user behavior 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.223-228 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00019-X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00019-X</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032968561?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032968561?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 Nanjundan P., Department of Data Science, CHRIST University, Lavasa Campus, Maharashtra, Pune, India; Thomas L., Department of Data Science, CHRIST University, Karnataka, Bengaluru, India; Indu P.V., Department of Data Science, CHRIST University, Karnataka, Bengaluru, India; Eslamian S., Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran Description An account of the resource The introduction of social media has prompted an explosion of diverse data types, such as textual content, pix, videos, and audio. Traditional unimodal analysis techniques do not effectively depict the difficult interactions between exceptional fact sorts and consumer sports. Multimodal data analytics addresses this difficulty through fusing one-of-a-kind modalities to unlock deeper insights, improving the accuracy and scope of social media analysis. This bankruptcy looks into the significance of multimodal facts in understanding consumer behavior, sentiment analysis, content material engagement assessment, and trend prediction. The bankruptcy starts off with the exploration of various sources of statistics in social media analytics, together with textual content posts, visual content, and consumer interactions. It then explores preprocessing and function extraction strategies utilized to prepare raw multimodal data for the usage of gadget gaining knowledge of. In-intensity methodologies, inclusive of natural language processing for text evaluation, computer vision for photo and video interpretation, and speech recognition for audio processing, are expounded in extraordinary detail. Integration of these modalities via fusion techniquesearly fusion, past due fusion, and hybrid modelsis also explored. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource computer vision (CV); feature fusion yechniques; machine learning in social media; Multimodal data analytics; natural language processing (NLP); sentiment analysis; social media behavior; user engagement patterns 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