Proceedings of the 6th EAI International Conference on IoT in Urban Space, Urb-IoT 2021, 20-21 December 2021, Shenzhen, People’s Republic of China

Research Article

Text classification method based on LSTM

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  • @INPROCEEDINGS{10.4108/eai.20-12-2021.2315016,
        author={Jiang  Qinghua},
        title={Text classification method based on LSTM},
        proceedings={Proceedings of the 6th EAI International Conference on IoT in Urban Space, Urb-IoT 2021, 20-21 December 2021, Shenzhen, People’s Republic of China},
        publisher={EAI},
        proceedings_a={EAI URB-IOT},
        year={2022},
        month={5},
        keywords={text classification deep learning},
        doi={10.4108/eai.20-12-2021.2315016}
    }
    
  • Jiang Qinghua
    Year: 2022
    Text classification method based on LSTM
    EAI URB-IOT
    EAI
    DOI: 10.4108/eai.20-12-2021.2315016
Jiang Qinghua1,*
  • 1: Youlian shipyard (Shekou) Co., Ltd
*Contact email: zty224@126.com

Abstract

With the rapid development of Internet technology and the explosive growth of social media, a large amount of information continues to be generated, of which the amount of text information is the largest. The main feature of various Chinese short text messages such as news headlines and instant messages is sparsity, which is only composed of a few to dozens of words, and the content of effective information packets is very small. As a result, the samples with sparse features and high feature set dimensions are difficult to provide key and accurate features for text classification learning. This paper mainly studies the application of deep learning in the field of Chinese text classification, and proposes a text classification model based on word level and character level mixed features.