Stock investment is an important mode of asset allocation and a crucial means of financial management. How to grasp the movement of stock price and predict its trend have been the focus of investors and investment companies. Since expert stock comments contain abundant essential information for investment decisions, how to identify the topic of expert stock comments with high precision and efficiency is an important research topic. However, the existing methods usually employ single feature selection strategies for topic identification of stock comments, which may lead to low accuracy. Thus, to deal with this limitation, we propose a multi-level information fusion method to construct a topic identification system of stock comments. Specifically, we firstly fuse various complementary feature selection methods via a multi-view learning framework which can comprehensively represent text-based topics. In addition, regarding the decision process, we propose a fusion strategy based on belief value which can further improve the classification performance. The experimental results indicate that the proposed multi-level information fusion method is not only superior to other methods in terms of classification, it is also able to accurately capture topics of expert stock comments.

中文翻译：

---

基于多层次信息融合的基于文本的专家股票评论主题识别

股票投资是一种重要的资产配置方式，是理财的重要手段。如何把握股价走势，预测其走势，一直是投资者和投资公司关注的焦点。由于专家股评包含丰富的投资决策必要信息，如何高精度、高效地识别专家股评主题是一个重要的研究课题。然而，现有方法对股票评论的主题识别通常采用单一的特征选择策略，可能导致准确率较低。因此，为了应对这一局限性，我们提出了一种多层次信息融合方法来构建股票评论主题识别系统。具体来说，我们首先通过可以全面表示基于文本的主题的多视图学习框架融合各种互补的特征选择方法。此外，关于决策过程，我们提出了一种基于信念值的融合策略，可以进一步提高分类性能。实验结果表明，所提出的多层次信息融合方法不仅在分类方面优于其他方法，而且能够准确捕捉专家股票评论的主题。