An End-to-end Hierarchical Multi-task Learning Framework of Sentiment Analysis and Key Entity Identification for Online Financial Texts

Timely and accurate collection and analysis of a financial entity's online text not only helps investors a void risks and optimize investment plans, but also helps the institutions's own public opinion management. However, how to automatically identify the negative text from a large number of online financial text and identify the key entities mainly described by the text is a difficult problem. Most existing studies focuses only on the sentimental polarity of financial texts or only on all the entities that extract them. However, investors and institution may be more focused on key entities in financial texts, rather than just the sentiment of financial texts. Besides, the sentimental polarity of online news is often closely related to key entities which fits well with the idea of multi-task learning. In this paper, we propose a hierarchical multi-task learning framework(HMFF) to not only identify the sentimental polarity of online news but also enhance the feature learning of key entity identification. Experimental results show that our framework not only end-to-end identifies the sentimental polarity of financial text and key entities, but also improves the accuracy of the model.