Earticle

초록

영어

In this paper, we find a suitable methodology for Korean Sentiment Analysis through a comparative experiment in which methods of embedding and natural network models are learned at the highest accuracy and fastest speed. The embedding method compares word embeddeding and Word2Vec. The model compares and experiments representative neural network models CNN, RNN, LSTM, GRU, Bi-LSTM and Bi-GRU with IKEA review data. Experiments show that Word2Vec and BiGRU had the highest accuracy and second fastest speed with 94.23% accuracy and 42.30 seconds speed. Word2Vec and GRU were found to have the third highest accuracy and fastest speed with 92.53% accuracy and 26.75 seconds speed.

목차

Abstract
1. Introduction
2. Related research
2.1 CNN
2.2 RNN
2.3 LSTM
2.4 GRU
2.5 BiLSTM
2.6 BiGRU
3. Experiment
4. Experiment result
5. Conclusion
References

키워드

저자

• YuJeong Sim [ Graduate School of Smart Convergence Kwangwoon University, Korea ]
• Dai Yeol Yun [ Professor, Department of information and communication Engineering, Institute of Information Technology, Kwangwoon University, Seoul, 01897, Korea ]
• Chi-gon Hwang [ Visiting Professor, Department of Computer Engineering, Institute of Information Technology, Kwangwoon University, Seoul, 01897, Korea ]
• Seok-Jae Moon [ Professor, Department of Artificial Intelligence, Institute of Information Technology, KwangWoon University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380