Text-Visualizing Neural Network Model: Understanding Online Financial Textual Data

  • Tomoki ItoEmail author
  • Hiroki Sakaji
  • Kota Tsubouchi
  • Kiyoshi Izumi
  • Tatsuo Yamashita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)


This study aims to visualize financial documents to swiftly obtain market sentiment information from these documents and determine the reason for which sentiment decisions are made. This type of visualization is considered helpful for nonexperts to easily understand technical documents such as financial reports. To achieve this, we propose a novel interpretable neural network (NN) architecture called gradient interpretable NN (GINN). GINN can visualize both the market sentiment score from a whole financial document and the sentiment gradient scores in concept units. We experimentally demonstrate the validity of text visualization produced by GINN using a real textual dataset.


Interpretable neural network Text mining Support system 



This work was supported in part by JSPS Fellows Grant Number 17J04768.

Supplementary material

469284_1_En_20_MOESM1_ESM.pdf (133 kb)
Supplementary material 1 (pdf 133 KB)


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Tomoki Ito
    • 1
    Email author
  • Hiroki Sakaji
    • 1
  • Kota Tsubouchi
    • 2
  • Kiyoshi Izumi
    • 1
  • Tatsuo Yamashita
    • 2
  1. 1.The University of TokyoTokyoJapan
  2. 2.Yahoo Japan CorporationTokyoJapan

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