Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | PAKDD | Market sentiment,Text mining,Architecture,Computer science,Support system,Visualization,Technical documentation,Artificial neural network,Finance |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomoki Ito | 1 | 1 | 1.72 |
| Hiroki Sakaji | 2 | 30 | 17.97 |
| Kota Tsubouchi | 3 | 81 | 26.58 |
| Kiyoshi Izumi | 4 | 127 | 37.12 |
| Tatsuo Yamashita | 5 | 4 | 2.51 |