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.

最近，数据挖掘和自然语言处理技术的发展使社交媒体与股票市场波动之间的关系探究成为可能。自然语言处理，深度学习和金融领域的整合是不可阻挡的。本文提出了一种基于推特嵌入和历史价格的股票市场预测的混合方法。与传统的文本嵌入方法不同，我们的方法考虑了Twitter数据的内部语义特征和外部结构特征，从而生成的推文向量可以包含更有效的信息。具体来说，我们开发了一种推文节点算法，用于通过构建推文节点网络来描述Twitter数据中的潜在连接。更多，我们的模型将情感属性补充到Twitter表示中，然后将这些属性输入到基于注意力机制和历史股价的深度学习模型中。此外，我们设计了一个可视的交互式库存预测工具来显示预测结果。