Abstract
Deep learning has been widely applied in natural language processing. The neuron model in a deep belief network is important for its performance, and so more attention should be paid to investigate how much influence the neuron will play on its results. In this paper we investigate the neuron’s effect for sentiment prediction, and then apply both total accuracy and F-measure to evaluate the performance. Finally, our experimental results show the idea of Gaussian neuron performs relatively better on the Stanford Twitter Sentiment corpus, which further proves the neuron model should be considered for a specific problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for boltzmann machines. Cogn. Sci. 9(1), 147–169 (1985)
Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)
Nair, V., Hinton, G.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010) (2010)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. JMLR W&CP Volume (2011)
Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2011)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2013)
Hartman, E.J., Keeler, J.D., Kowalski, J.M.: Layered neural networks with Gaussian hidden units as universal approximations. Neural Comput. 2(2), 210–215 (1990)
Rumelhart, D.E., Hinton, G., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5(3), 1 (1988)
Olson, D.L., Delen, D.: Advanced Data Mining Techniques. Springer Science & Business Media, Heidelberg (2008)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford University (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jin, Y., Du, D., Zhang, H. (2016). Gaussian Neuron in Deep Belief Network for Sentiment Prediction. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-34111-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-34110-1
Online ISBN: 978-3-319-34111-8
eBook Packages: Computer ScienceComputer Science (R0)