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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 871412, 11 pages
http://dx.doi.org/10.1155/2014/871412
Research Article

An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery

IT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, Japan

Received 23 August 2013; Revised 28 December 2013; Accepted 13 January 2014; Published 26 February 2014

Academic Editor: Ying-Tung Hsiao

Copyright © 2014 Shigeaki Sakurai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments.