論 文Papers

CONFERENCE (INTERNATIONAL)

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

Tomoki Ito(UTokyo), Hiroki Sakaji(UTokyo), Kota Tsubouchi, Kiyoshi Izum(UTokyo), Tatsuo Yamashita

The 22nd Pacific-Asia Conference on Knowledge Discovery and Data (PAKDD 2018), 2018/6

Category:

機械学習 (Machine Learning) データサイエンス (Data Science) その他の取り組み (Misc.)

Abstract:
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.
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Text-visualizing Neural Network Model: Understanding Online Financial Textual Data(外部サイト/External Site Link)