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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 11/2019

09.01.2019 | Original Article

Information geometry enhanced fuzzy deep belief networks for sentiment classification

verfasst von: Meng Wang, Zhen-Hu Ning, Tong Li, Chuang-Bai Xiao

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 11/2019

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Abstract

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.
Vorheriger Artikel A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy
Nächster Artikel A memetic approach for training set selection in imbalanced data sets

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Metadaten
Titel
Information geometry enhanced fuzzy deep belief networks for sentiment classification
verfasst von
Meng Wang
Zhen-Hu Ning
Tong Li
Chuang-Bai Xiao
Publikationsdatum
09.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 11/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-00920-3

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