Deep neural network for short-text sentiment classification
Author(s)
Wang, Philips Fu Lee
Author(s)
Li, X.
Pang, J.
Mo, B.
Rao, Y.
Date Issued
2016
Publisher
Springer
Related Publication(s)
Database Systems for Advanced Applications (DASFAA 2016 International Workshops) Proceedings
Start page
168
End page
175
Abstract
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.