Optimizing Emotion Recognition in Korean Counseling Texts Using Parameter Efficient Fine Tuning

Q: What does this research mean for the field?

Applying Parameter-Efficient Fine-Tuning (LoRA) to KcBERT achieves high accuracy (0.824) and F1-score (0.812) in emotion recognition for Korean counseling texts while updating only 0.3% of parameters, demonstrating its viability for resource-constrained mental health support systems. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Myung-Suk Lee

doi:10.9708/jksci.2026.31.05.075

May 31, 2026Journal of the Korea Society of Computer and Information

Optimizing Emotion Recognition in Korean Counseling Texts Using Parameter Efficient Fine Tuning

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Key Points

This study aims to propose a parameter-efficient emotion recognition model optimized for Korean counseling dialogues.
Applied LoRA and QLoRA techniques on KcBERT
Utilized AI Hub corpus for fine-tuning
Compared performance with KoGPT2 using accuracy and F1-score metrics.
Achieved accuracy of 0.824 and F1-score of 0.812 with only 0.3% of parameters updated
Confirmed stable emotion expression learning through t-SNE analysis
Demonstrated practical deployment potential of high-performance models in resource-limited environments.

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Implication

Demonstrates improved emotion recognition in Korean counseling texts, suggesting better mental health support efficiency.

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Myung-Suk Lee (2026) studied this question.

synapsesocial.com/papers/6a23b9ca71a5da9775e7594f https://doi.org/10.9708/jksci.2026.31.05.075

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Optimizing Emotion Recognition in Korean Counseling Texts Using Parameter Efficient Fine Tuning

Key Points

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Cite This Study

Optimizing Emotion Recognition in Korean Counseling Texts Using Parameter Efficient Fine Tuning

Key Points

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