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Related Experiment Video

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Enhanced graph attention network by integrating Long Short-Term Memory for artificial emotion representation in

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  • 1Department of Transportation, School of Transportation and Vehicle Engineering, ‌‌Wuxi University, Wuxi, China.

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Summary

This study introduces a hybrid AI model for emotion recognition, combining Enhanced Graph Attention Networks and Bidirectional Long Short-Term Memory. The model achieves state-of-the-art results on text-only and multi-modal datasets, improving human-computer interaction.

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Area of Science:

  • Artificial Intelligence
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Emotion recognition from multi-modal data is challenging due to complex feature relationships.
  • Existing methods struggle to integrate textual, audio, and visual emotional cues effectively.

Purpose of the Study:

  • To develop a hybrid AI model for robust emotion recognition across text-only and multi-modal data.
  • To improve the integration of semantic and temporal features for accurate emotion detection.

Main Methods:

  • Utilized Enhanced Graph Attention Networks (E-GAT) to capture structural dependencies from text embeddings.
  • Employed Bidirectional Long Short-Term Memory (Bi-LSTM) to model temporal dynamics in sequential data.
  • Integrated E-GAT and Bi-LSTM for a unified emotion recognition framework.

Main Results:

  • Achieved state-of-the-art performance on the SemEval-2018 (text-only) dataset with 58.5% accuracy and 68.7% F1-score.
  • Demonstrated high accuracy on multi-modal datasets: 78.9% on RAVDESS and 82.3% on CMU-MOSEI.
  • Showcased robust cross-modal generalization capabilities.

Conclusions:

  • The proposed hybrid E-GAT and Bi-LSTM model effectively addresses challenges in multi-modal emotion recognition.
  • This unified framework advances emotion recognition for applications in HCI and mental health monitoring.
  • The model offers significant improvements over baseline approaches for diverse emotional data types.