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논문검색

Korean Sentiment Analysis Using Natural Network : Based on IKEA Review Data

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

저자정보

  • 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

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