Deep Neural Network for Short Text Sentiment Classification
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.
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