International Journal of Advanced Culture Technology

  • 제8권2호
  • /
  • Pages.231-238
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  • 2020
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  • 2288-7202(pISSN)
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  • 2288-7318(eISSN)
국제문화기술진흥원 (The International Promotion Agency of Culture Technology)
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Comparison of Neural Network Techniques for Text Data Analysis

  • Kim, Munhee (Department of Statistics, Hankuk University of Foreign Studies) ;
  • Kang, Kee-Hoon (Department of Statistics, Hankuk University of Foreign Studies)
  • 투고 : 2020.04.30
  • 심사 : 2020.05.30
  • 발행 : 2020.06.30
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초록

Generally, sequential data refers to data having continuity. Text data, which is a representative type of unstructured data, is also sequential data in that it is necessary to know the meaning of the preceding word in order to know the meaning of the following word or context. So far, many techniques for analyzing sequential data such as text data have been proposed. In this paper, four methods of 1d-CNN, LSTM, BiLSTM, and C-LSTM are introduced, focusing on neural network techniques. In addition, by using this, IMDb movie review data was classified into two classes to compare the performance of the techniques in terms of accuracy and analysis time.

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참고문헌

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