Stock text topic recognition based on Stu-BERT

Xinning Li; Yating Gao; Shiyu Ge; Lina Wang; Yingjie Song; Feng Zhao

doi:10.1117/12.2637485

15 June 2022 Stock text topic recognition based on Stu-BERT

Xinning Li, Yating Gao, Shiyu Ge, Lina Wang, Yingjie Song, Feng Zhao

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122851G (2022) https://doi.org/10.1117/12.2637485
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

Expert stock comments are important basis for accurately predicting stock trends. How to capture the theme of expert stock reviews effectively is an important issue in text classification. The Bidirectional Encoder Representations from Transformers (BERT) model has been widely used for text classification. However, BERT has some limitations. (1) The information extraction of stock comments beyond the limit of the fixed length in model is incomplete. (2) The features extracted from the model are not comprehensive enough. (3) The application efficiency of BERT model is low due to the large number of parameters. To tackle above issues, we propose a Student Bidirectional Encoder Representations from Transformers (Stu-BERT) model for accurately identifying of stock comments. Specifically, we firstly intercept at beginning and end of stock comments beyond the fixed length to improve access to information. Secondly, we fuse all the features of the last layer of the hidden layer in model to improve the topic recognition accuracy. In addition, we distill the BERT model to get Stu-BERT model, which enhance the practicability of applying it to the topic identification. Experimental results on real data demonstrate the effectiveness of the proposed method.

Citation Download Citation

Xinning Li, Yating Gao, Shiyu Ge, Lina Wang, Yingjie Song, and Feng Zhao "Stock text topic recognition based on Stu-BERT", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122851G (15 June 2022); https://doi.org/10.1117/12.2637485

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