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Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Hierarchical Text-Guided Refinement Network for Multimodal Sentiment Analysis.

Yue Su1, Xuying Zhao1

  • 1School of Mathematical Sciences, Capital Normal University, Beijing 100048, China.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Hierarchical Text-Guided Refinement Network (HTRN) for multimodal sentiment analysis (MSA). The HTRN effectively aligns non-text features and reduces redundancy, achieving state-of-the-art results on benchmark datasets.

Keywords:
multimodal fusionmultimodal sentiment analysissemantic alignment

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Natural Language Processing

Background:

  • Multimodal sentiment analysis (MSA) integrates text, audio, and video for enhanced understanding.
  • Existing methods struggle with aligning non-text features and mitigating information redundancy.

Purpose of the Study:

  • To propose a novel Hierarchical Text-Guided Refinement Network (HTRN) for improved MSA.
  • To enhance crossmodal interactions and suppress irrelevant signals in multimodal data.

Main Methods:

  • The HTRN framework refines and aligns non-text modalities using hierarchical textual representations.
  • Shuffle-Insert Fusion (SIF) disrupts local correlations for generalized representations.
  • Text-Guided Alignment Layer (TAL) uses textual semantics to guide audio-visual refinement via learnable gating factors.

Main Results:

  • HTRN achieved state-of-the-art accuracies: 86.3% (CMU-MOSI), 86.7% (CMU-MOSEI), and 80.3% (CH-SIMS).
  • Performance improvements ranged from 0.8-3.45% over existing methods.
  • Ablation studies confirmed SIF and TAL contributed 1.9-2.1% performance gains.

Conclusions:

  • The HTRN framework effectively addresses challenges in multimodal alignment and redundancy.
  • The proposed methods significantly advance the performance of multimodal sentiment analysis.
  • HTRN establishes a robust framework for multimodal representation learning.