Assigning Polarity to Causal Information by Deep Learning and Extended Clue Expressions

Abstract

In this paper, we propose a method of assigning polarity to causal information extracted from summary of financial statements of companies. Our method assigns polarity (positive or negative) to causal information in accordance with business performance, e.g. "Orders of semiconductor manufacturing equipments were strong". First, we assigns polarity to extended clue expressions to be used to extract causal information. Using them, our method automatically generates training data and assigns polarity to causal information by deep learning. We evaluated our method and confirmed that it attained 86.7% precision and 95.4% recall of assigning polarity positive, and 90.0% precision and 73.9% recall of assigning polarity negative, respectively.