Topic identification of text‐based expert stock comments using multi‐level information fusion

Abstract 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.