Hierarchical Label Embedding Networks for Financial Document Sentiment Analysis

With the rapid development of the Internet, document data have become an important source of information in the financial field. The application of documents sentiment analysis in the financial field has attracted increasing attention. It is obviously impractical to extract sentiments manually from a large amount of financial document, but natural language processing (NLP) technology can solve this problem. The research object of this paper focuses on the research reports of listed companies, which is a kind of long financial document published by experts in the field. In this paper, we propose a hierarchical label embedding neural network model for sentiment analysis of financial documents. This model adopts hierarchical network structure to capture the structural information of financial documents. Moreover, the model also includes an expression embedding mechanism for focusing on important content. We believe that most of the words and sentences in a document are consistent with the sentiments of the labels marked by the author. The label embedding mechanism can pay more attention to the content that is consistent with the sentiments of the labels during the document's hierarchical representation. Experiments showed that our method is more effective than other advanced methods on the established dataset.