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This content will become publicly available on April 11, 2026

Title: DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approaches can introduce redundancy and conflicts due to equal treatment of all modalities and the mutual transfer of information between modality pairs. To address these issues, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework, which incorporates a feature disentanglement module to separate modality-shared and modality-specific information. To further reduce redundancy and enhance language-targeted features, four geometric measures are introduced to refine the disentanglement process. A Language-Focused Attractor (LFA) is further developed to strengthen language representation by leveraging complementary modality-specific information through a language-guided cross-attention mechanism. The framework also employs hierarchical predictions to improve overall accuracy. Extensive experiments on two popular MSA datasets, CMU-MOSI and CMU-MOSEI, demonstrate the significant performance gains achieved by the proposed DLF framework. Comprehensive ablation studies further validate the effectiveness of the feature disentanglement module, language-focused attractor, and hierarchical predictions.  more » « less
Award ID(s):
2122320 2324937
PAR ID:
10654607
Author(s) / Creator(s):
; ; ; ;
Corporate Creator(s):
Editor(s):
NA
Publisher / Repository:
PKP Publishing Services Network AAAI Technical Track on Machine Learning VI
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
39
Issue:
20
ISSN:
2159-5399
Page Range / eLocation ID:
21180 to 21188
Subject(s) / Keyword(s):
NA
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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