GINN: gradient interpretable neural networks for visualizing financial texts

• DOI: 10.1007/s41060-018-0160-8
• Publication Date: 2018-12-04T03:53:07Z
• Authors: Tomoki Ito; Hiroki Sakaji; Kiyoshi Izumi; Kota Tsubouchi; Tatsuo Yamashita
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

This study aims to visualize financial documents in such a way that even nonexperts can understand the sentiments contained therein. To achieve this, we propose a novel text visualization method using an interpretable neural network (NN) architecture, called a gradient interpretable NN (GINN). A GINN can visualize a market sentiment score from an entire financial document and the sentiment gradient scores in both word and concept units. Moreover, the GINN can visualize important concepts given in various sentence contexts. Such visualization helps nonexperts easily understand financial documents. We theoretically analyze the validity of the GINN and experimentally demonstrate the validity of text visualization produced by the GINN using real financial texts.