论文标题
基于深度学习的中文文字挖掘和股票市场相关研究
Deep learning based Chinese text sentiment mining and stock market correlation research
论文作者
论文摘要
我们探索如何抓取诸如库存栏之类的财务论坛数据，并将它们与深度学习模型相结合以进行情感分析。在本文中，我们将使用BERT模型对财务语料库进行训练并预测SZSE组件指数，并通过最大信息系数比较研究发现将BERT模型应用于财务语料库。获得的情感功能将能够反映股票市场上的波动，并有效提高预测准确性。同时，本文将深度学习与财务文本结合在一起，进一步通过深度学习方法进一步探讨了股票市场上投资者情绪的机制，这对国家监管机构和政策部门将有益于制定更合理的政策指南，以维持股票市场的稳定性。
We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.
