Article
Version 1
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Advanced Cross-Modal Gating for Enhanced Multimodal Sentiment Analysis
Version 1
: Received: 3 August 2024 / Approved: 4 August 2024 / Online: 6 August 2024 (04:06:37 CEST)
How to cite: Johnson, E.; Patel, R.; Smith, M. Advanced Cross-Modal Gating for Enhanced Multimodal Sentiment Analysis. Preprints 2024, 2024080265. https://doi.org/10.20944/preprints202408.0265.v1 Johnson, E.; Patel, R.; Smith, M. Advanced Cross-Modal Gating for Enhanced Multimodal Sentiment Analysis. Preprints 2024, 2024080265. https://doi.org/10.20944/preprints202408.0265.v1
Abstract
The rapidly evolving domain of multimodal sentiment analysis is crucial for unraveling the intricate layers of emotional expression in social media content, customer service interactions, and personal vlogs. This research introduces a cutting-edge Advanced Cross-Modal Gating (ACMG) framework that significantly enhances the precision of sentiment analysis by refining the interplay among textual, auditory, and visual modalities. Our approach addresses three foundational aspects of sentiment analysis: (1) Advanced learning of cross-modal interactions, which focuses on extracting and synthesizing sentiment from varied modal inputs, thus providing a holistic view of expressed emotions; (2) Mastery over the temporal dynamics of multimodal data, enabling the model to maintain context and sentiment continuity over extended interactions; and (3) Deployment of a novel fusion strategy that not only integrates unimodal and cross-modal cues but also dynamically adjusts the influence of each modality based on its contextual relevance to the sentiment being expressed. The exploration of these dimensions reveals that the nuanced modeling of cross-modal interactions is crucial for enhancing model responsiveness and accuracy. By applying the ACMG model to two highly regarded datasets—CMU Multimodal Opinion Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI)—we achieve groundbreaking accuracies of 83.9\% and 81.1\%, respectively. These results represent significant improvements of 1.6\% and 1.34\% over the current state-of-the-art, showcasing the superior performance and potential of our approach in navigating the complexities of multimodal sentiment analysis.
Keywords
multimodal sentiment analysis; cross-modal gating; deep learning fusion
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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