Automatic Inference of Cabinet Approval Ratings by Information-Theoretic Competitive Learning
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.
KeywordsInput Pattern Modality Pattern Negative Word Generalization Error Input Unit
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