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A hybrid approach for stock trend prediction based on tweets embedding and historical prices

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Abstract

Recently, the development of data mining and natural language processing techniques enable the relationship probe between social media and stock market volatility. The integration of natural language processing, deep learning and the financial field is irresistible. This paper proposes a hybrid approach for stock market prediction based on tweets embedding and historical prices. Different from the traditional text embedding methods, our approach takes the internal semantic features and external structural characteristics of Twitter data into account, such that the generated tweet vectors can contain more effective information. Specifically, we develop a Tweet Node algorithm for describing potential connection in Twitter data through constructing the tweet node network. Further, our model supplements emotional attributes to the Twitter representations, which are input into a deep learning model based on attention mechanism together with historical stock price. In addition, we designed a visual interactive stock prediction tool to display the result of the prediction.

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Acknowledgements

This work is partially supported by GuangDong Basic and Applied Basic Research Foundation 2019B1515120048.

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    1. School of Software Engineering, East China Normal University, Shanghai, China

      Huihui Ni & Peng Cheng

    2. School of Computer and Information Engineering, Zhejiang Gongshang University, Zhejiang, China

      Shuting Wang

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    1. Huihui Ni
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    Correspondence to Peng Cheng.

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    Huihui Ni and Shuting Wang are joint first author and contribute equally to this work.

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    Ni, H., Wang, S. & Cheng, P. A hybrid approach for stock trend prediction based on tweets embedding and historical prices. World Wide Web 24, 849–868 (2021). https://doi.org/10.1007/s11280-021-00880-9

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