Information geometry enhanced fuzzy deep belief networks for sentiment classification

With the development of internet, more and more people share reviews. Efficient sentiment analysis over such reviews using deep learning techniques has become an emerging research topic, which has attracted more and more attention from the natural language processing community. However, improving performance of a deep neural network remains an open question. In this paper, we propose a sophisticated algorithm based on deep learning, fuzzy clustering and information geometry. In particular, the distribution of training samples is treated as prior knowledge and is encoded in fuzzy deep belief networks using an improved Fuzzy C-Means (FCM) clustering algorithm. We adopt information geometry to construct geodesic distance between the distributions over features for classification, improving the FCM. Based on the clustering results, we then embed the fuzzy rules learned by FCM into fuzzy deep belief networks in order to improve their performance. Finally, we evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that our algorithm brings out significant improvement over existing methods.