Inference of Cabinet approval ratings by neural networks

Abstract

In this paper, we try to estimate Japan's cabinet approval ratings by using neural networks. Though there are a number of studies on approval rating estimation, little attempts have been made to apply neural networks to estimate approval ratings. Especially, in Japan, no attempts have been made to infer approval ratings by neural networks. Thus, this is the first attempt to use neural networks for approval estimation in Japan. Neural networks may show better performance for this complex problem, because approval ratings are affected by many complex factors that cannot be dealt with by conventional statistical methods. Experimental results show that neural networks have much better performance than that obtained by the standard regression analysis in terms of training and testing errors.