首页> 外文会议>Database systems for advanced applications >Deep Neural Network for Short-Text Sentiment Classification
【24h】

Deep Neural Network for Short-Text Sentiment Classification

机译:短文本情感分类的深度神经网络

获取原文
获取原文并翻译 | 示例

摘要

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 Boltz-mann 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.
机译:作为描述事件的简洁媒介,短文本在传达用户意见方面起着重要作用。基于短文本的用户情绪分类已经成为社交网络分析中的重要课题。神经网络具有良好的分类性能和较高的泛化能力。然而,常规的神经网络仅使用简单的反向传播算法来估计参数,当通过随机初始化训练深度神经网络时,可能会引入较大的不稳定性。在本文中,我们将一种预训练方法应用于基于受限Boltz-mann机器的深度神经网络,目的是在短文本上获得具有竞争力的稳定用户情绪分类性能。使用现实世界的数据集进行的实验评估验证了我们的模型在短文本情感分类任务上的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号