论文标题
研究基于深度学习的文本情感挖掘与股票市场之间的相关性的研究
Research on the correlation between text emotion mining and stock market based on deep learning
论文作者
论文摘要
本文讨论了如何抓取金融论坛（例如库存栏）的数据，并进行情感分析与深入学习模型相结合。本文将使用BERT模型来培训财务语料库并预测深圳股票指数。通过对最大信息系数（MIC）的比较研究，发现通过将BERT模型应用于财务语料库获得的情感特征可以反映在股票市场的波动中，这有助于有效提高预测准确性。同时，本文将深入的学习与财务经文结合在一起，以进一步探索投资者情绪在股票市场的影响机制，这将有助于国家监管机构和政策部门制定更合理的政策和指南，以维持股票市场的稳定性。
This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the financial corpus and predict the Shenzhen stock index. Through the comparative study of the maximal information coefficient (MIC), it is found that the emotional characteristics obtained by applying the BERT model to the financial corpus can be reflected in the fluctuation of the stock market, which is conducive to effectively improve the prediction accuracy. At the same time, this paper combines in-depth learning with financial texts to further explore the impact mechanism of investor sentiment on the stock market through in-depth learning, which will help the national regulatory authorities and policy departments to formulate more reasonable policies and guidelines for maintaining the stability of the stock market.
