[1D3-GS-13] AI application: Text mining and natural language

[1D3-GS-13-03] Extraction of causal and complementary information for generating market analysis comments by automatic generation of training data

〇Hiroyuki Sakai¹, Hiroki Sakaji², Kiyoshi Izumi², Tohgoroh Matsui³, Keitaro Irie⁴ (1. Seikei University, 2. The University of Tokyo, 3. Chubu University, 4. Mitsubishi UFJ Kokusai Asset Management Co., Ltd.)

Keywords:market analysis, causal extraction, operational efficiency improvement, text mining

In this research, we propose a method for extracting sentences containing causal information from articles describing the market conditions of the Nikkei Stock Average.
The sentences containing causal information are needed to generate market analysis comments. Our method extracts articles describing the market conditions of the Nikkei Stock Average from economic newspaper articles and extracting sentences containing causal information from the extracted articles by deep learning. Here, our method automatically generates the training data necessary to extract the articles describing the market conditions and sentences containing causal information by deep learning and achieved high accuracy. Moreover, our method extracts complementary information of the content described in the causal sentences by using economic causal-chain search.