Wang, Philips Fu LeePhilips Fu LeeWangLi, X.Pang, J.Mo, B.Rao, Y.2021-04-082021-04-082016https://repository.sfu.edu.hk/handle/sfu/518As 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.enconference proceedings10.1007/978-3-319-32055-7_15