Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Text Classification on Social Network Platforms Based on Deep Learning Models

  • YA, Chen (Department of Information Engineering, GuangXi Transport Vocational and Technical College) ;
  • Tan, Juan (Weifang University of Science and Technology) ;
  • Hoekyung, Jung (Department of Computer Engineering, Paichai University)
  • Received : 2021.11.06
  • Accepted : 2022.04.19
  • Published : 2023.03.31
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Abstract

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

Keywords

Acknowledgement

This study was supported by "the Yound and Middle-aged Promotion Project of GuangXi Education Department, China (Grant: 2019KY348)" and by "fine-grained Image Classification in vehicle application technology research (Grant: 2020KY24019)." This study was also supported by the China Scholarship Council (No. 202008450033).

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