Integrating Multimodal Data For Enhanced Analysis And Understanding: Techniques For Sentiment Analysis And Cross-Modal Retrieval

Abstract

In today's dynamic digital landscape, the prevalence of multimedia content across various platforms underscores the importance of advanced techniques for analyzing data across diverse modalities. This paper explores the integration of text data with other modalities such as images, videos, and audio to enable comprehensive analysis and understanding. Specifically, the focus is on investigating methods for sentiment analysis in multimedia content and facilitating cross-modal retrieval. The paper addresses the challenges and opportunities in multimodal analysis, reviews existing techniques, and proposes novel methods for enhancing sentiment analysis and cross-modal retrieval through multimodal fusion and deep learning architectures. The challenges inherent in multimodal analysis include data heterogeneity, semantic gap, modality imbalance, and scalability. These challenges necessitate the development of robust techniques for multimodal fusion, feature representation, and cross-modal mapping. Existing methods, including early fusion, late fusion, and hybrid fusion techniques, are reviewed, alongside recent advancements in deep learning-based multimodal fusion architectures. Proposed methodologies aim to augment sentiment analysis and cross-modal retrieval through innovative multimodal fusion techniques and deep learning architectures. Experimental evaluations validate the effectiveness of the proposed methods in improving sentiment analysis accuracy and cross-modal retrieval performance. This research contributes to advancing techniques for analyzing and understanding multimedia content in the increasingly complex digital landscape, facilitating enhanced data-driven insights and decision-making processes across various domains.