As a concise medium to describe events, short text plays an important role to convey the opinions of users. The classification of user emotions based on short text has been a significant topic in social network analysis. Neural Network can obtain good classification performance with high generalization ability. However, conventional neural networks only use a simple back-propagation algorithm to estimate the parameters, which may introduce large instabilities when training deep neural networks by random initializations. In this paper, we apply a pre-training method to deep neural networks based on restricted Boltzmann machines, which aims to gain competitive and stable classification performance of user emotions over short text. Experimental evaluations using real-world datasets validate the effectiveness of our model on the short-text sentiment classification task.
CITATION STYLE
Li, X., Pang, J., Mo, B., Rao, Y., & Wang, F. L. (2016). Deep neural network for short-text sentiment classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9645, pp. 168–175). Springer Verlag. https://doi.org/10.1007/978-3-319-32055-7_15
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