Abstract: Sentiment analysis of financial news helps enterprises and investors to determine investment risks and improves economic benefits, resulting in high application value. Graph neural networks have excellent performance in text classification, and have been applied to the field of sentiment analysis. In this paper, we propose a sentiment analysis method that uses dependency syntax analysis in graph convolutional neural networks (Dependency Analysis-based Graph Convolutional Network, DA-GCN) for financial news. This method obtains the word order information of the sentence and the syntactic in the document by analyzing the dependency of words in the document. It then implements information propagation and weight updates in the graph with co-occurrence information in each document. Experiments on a financial news dataset show that our model achieves significant performance improvements over traditional deep learning methods.

Key words: graph neural networks, financial news, dependency analysis, sentiment analysis, deep learning