Automatic Inference of Cabinet Approval Ratings by Information-Theoretic Competitive Learning

Abstract

In this paper, we demonstrate that cabinet approval ratings can automatically be inferred with good performance by a neural network technique, that is, information-theoretic competitive learning. Because cabinet approval rating estimation is an extremely complex process with much non-linearity, neural networks may give much better performance than conventional statistical methods. Though an attempt to infer public opinions seem to be a challenging topic for machine learning, little attempts have been made to infer approval ratings to our best knowledge. In this context, we try to apply information-theoretic competitive learning to the problem of cabinet approval ratings. Information-theoretic competitive learning has been developed so as to simulate competitive processes of neurons. One of the main characteristics of the method is that it is a very soft-type of competitive learning in which conventional competitive learning is only a special case. Though the method seems to be promising due to its general property, we have had a few experimental results to show better performance. Experimental results show that without any teacher information neural networks can appropriately infer the rise and fall of approval ratings through a process of information maximization. This experiment result surely opens up new perspectives for neural networks as well as mass communication studies.