Sentiment Classification Based on Information Geometry and Deep Belief Networks

Abstract

Sentiment classification for reviews has attracted increasingly more attention from the natural language processing community. By embedding prior knowledge into learning structures, classifiers often achieve a better performance than original methods. In this paper, we propose a sophisticated algorithm based on deep learning and information geometry in which the distribution of all training samples in the space is treated as prior knowledge and is encoded by deep belief networks (DBNs). From the view of information geometry, we construct the geodesic distance between the distributions over the features for classification. The study of the distributions contributes to the training of the DBN, since the distance is correlated to the error rate in the classification. Finally, we evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that our algorithm results in a significant improvement over existing methods.