Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

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

Wenyan Xiao1, Lin Zhang1, Yangshuyi Xu1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai, China.

Plos One
|May 12, 2026
PubMed
Summary

This study introduces a novel Text-Guided Dual-Gated Network (TDGN) for multimodal sentiment analysis. TDGN enhances emotion recognition accuracy and robustness by effectively aligning and fusing text, audio, and visual data.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Granulocyte colony-stimulating factor-induced hypersensitivity reaction with leukocytosis in a pediatric germ cell tumor patient: a case report.

Frontiers in pediatrics·2026
Same author

Development and Internal Validation of a Nomogram Model to Predict Invasive Pulmonary Aspergillosis Occurrence Risk in ICU Patients with Sepsis.

Infection and drug resistance·2026
Same author

Association between pulse pressure levels and mortality in patients with septic shock: a retrospective cohort study.

Frontiers in medicine·2026
Same author

Role of non-coding RNAs in O<sup>6</sup>-methylguanine-DNA methyltransferase-positive glioblastoma (Review).

Oncology letters·2026
Same author

Multifaceted roles of miR‑124 in cancer: Molecular mechanisms and clinical prospects (Review).

International journal of oncology·2026
Same author

A novel serum phosphorus to chloride and bicarbonate ratio predicts severe acute kidney injury in critically ill patients: a multicenter cohort study.

Respiratory medicine·2026

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.

Related Experiment Videos

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.