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A Text Content Classification Using LSTM For Objective Category Classification

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(5), pp.39-46
  • DOI : 10.9708/jksci.2021.26.05.039
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 18, 2021
  • Accepted : April 20, 2021
  • Published : May 31, 2021

Young-Dan Noh 1 Kyu-Cheol Cho 1

1인하공업전문대학

Accredited

ABSTRACT

AI is deeply applied to various algorithms that assists us, not only daily technologies like translator and Face ID, but also contributing to innumerable fields in industry, due to its dominance. In this research, we provide convenience through AI categorization, extracting the only data that users need, with objective classification, rather than verifying all data to find from the internet, where exists an immense number of contents. In this research, we propose a model using LSTM(Long-Short Term Memory Network), which stands out from text classification, and compare its performance with models of RNN(Recurrent Neural Network) and BiLSTM(Bidirectional LSTM), which is suitable structure for natural language processing. The performance of the three models is compared using measurements of accuracy, precision, and recall. As a result, the LSTM model appears to have the best performance. Therefore, in this research, text classification using LSTM is recommended.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.