TDGN: A text-guided dual-gated network for multimodal sentiment analysis

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Computer Vision
• Speech Processing

Background:

• Multimodal Sentiment Analysis (MSA) integrates diverse data types (text, audio, visual) for emotion interpretation.
• Challenges in MSA include heterogeneous data quality and weak inter-modal correlations, hindering stable alignment and effective fusion.
• Existing methods struggle with robustly combining nonverbal cues for accurate sentiment detection.

Purpose of the Study:

• To propose a novel Text-Guided Dual-Gated Network (TDGN) to overcome limitations in multimodal sentiment analysis.
• To enhance the alignment and fusion of heterogeneous modalities (text, audio, visual) for improved emotion recognition.
• To achieve state-of-the-art performance in sentiment analysis through a robust and accurate framework.

Main Methods:

• Developed a Text-Anchored Gated Attention (TGA) module for fine-grained alignment guided by textual semantics.
• Implemented a Dual-layer Gated Fusion (DGF) module for refining modality-specific features and balancing cross-modal contributions.
• Introduced Text-Anchored Contrastive Learning (TACL) to ensure fused features maintain modal consistency and diversity.

Main Results:

• The proposed TDGN achieved state-of-the-art performance on the CMU-MOSI and CMU-MOSEI benchmarks.
• TDGN demonstrated significantly higher accuracy and robustness in multimodal sentiment analysis tasks.
• Ablation studies and visualizations confirmed the effectiveness of individual components within the TDGN framework.

Conclusions:

• The Text-Guided Dual-Gated Network (TDGN) effectively addresses challenges in multimodal sentiment analysis.
• TDGN's hierarchical gating and text-anchored contrastive learning framework improve alignment and fusion.
• The proposed method offers a robust and accurate approach for interpreting emotions from multimodal data.