Earticle

초록

영어

This study utilized integrated coherence to improve cryptocurrency market predictions. RoBERTa and DistilBERT models were employed for measuring integrated coherence and results were obtained by applying LSTM. The proposed DistilBERT and RoBERTa models were compared to normal coherence through the t-test. The results showed that both the proposed DistilBERT and RoBERTa models outperformed LSTM with normal coherence. The study's findings confirmed that user sentiment is a necessary factor for market predictions.

목차

Abstract
Introduction
Theoretical Background
Long Short Term Memory (LSTM)
RoBERTa 감성분석 모델
DistilBERT
Methods
Hypothesis Development
Data and Method Process
Result
Conclusion
References

저자

• 조남재 [ 한양대학교 경영대학 ]
• 변재현 [ 한양대학교 경영대학 ]
• 유기섭 [ 경북대학교 경상대학 ]

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