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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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A Hybrid Deep Learning Emotion Classification System Using Multimodal Data.

Dong-Hwi Kim¹, Woo-Hyeok Son¹, Sung-Shin Kwak¹

¹Department of Computer Science, Dankook University, 152 Jukjeon-ro Campus, Suji-gu, Yongin-si 16890, Republic of Korea.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This summary is machine-generated.

This study introduces a hybrid deep learning emotion classification system (HDECS) for Korean, outperforming existing models. The HDECS effectively handles multimodal data for improved emotion recognition in diverse applications.

Keywords:

BERT deep learning emotion classification multimodal

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Area of Science:

Artificial Intelligence
Natural Language Processing
Speech Emotion Recognition

Background:

Emotion classification is crucial for AI and personalized services.
Existing single-modality approaches face challenges with intonation, text structure, and physiological signal variations.
Korean language presents unique NLP challenges like subject omission and spacing.

Purpose of the Study:

To propose a hybrid multimodal deep learning system (HDECS) for enhanced emotion classification in Korean.
To address limitations of single-modality sentiment analysis in spoken contexts.
To improve emotion recognition accuracy using multimodal data.

Main Methods:

Retraining LSTM and CNN models for multimodal Korean data until prediction agreement exceeds 0.75.
Generating emotional sentences from predictions to augment script data.
Utilizing BERT for final emotion prediction on the enhanced dataset.

Main Results:

The HDECS case model demonstrated superior performance compared to the KLUE/roBERTa model.
Achieved improvements of 0.5 in Categorical Cross-Entropy (CCE), 0.09 in accuracy, and 0.11 in F1 score.
The model effectively integrates multimodal information for robust emotion classification.

Conclusions:

The proposed HDECS offers a significant advancement in Korean emotion classification.
The hybrid approach is adaptable for sentiment classification across various languages and regional speech characteristics.
HDECS shows promise for real-world applications requiring nuanced emotion understanding.