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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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Published on: December 15, 2023

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

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 fusion multimodal sentiment analysis semantic 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.