Computer Science ›› 2017, Vol. 44 ›› Issue (12): 227-231.doi: 10.11896/j.issn.1002-137X.2017.12.041

Previous Articles     Next Articles

Sentimental Analysis Stacked Denoising Auto-encoder with Sparse Factor

JIANG Zong-li and WANG Yi-da   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Feature extraction based on deep learning is a hot research topic in data dimensionality reduction now.In deep learning,stacked auto-encoder is commonly used.The encoder just simply learns the features of sample and can’t get a good feature expression for the data which are mixed with noise and sparsity.Sparse factor is added in each hidden layer of the stacked denoising auto-encoder to solve the problem of feature extraction about data with noise and sparsity in this paper.The sentimental analysis experiments on COAE data set show that the precision and recall ratio are improved.

Key words: Deep learning,Stacked denoising auto-encoder,Sparse factor,Sentimental analysis

[1] BENGIO Y,DELALLEAU O.On the expressive power of deep architectures[C]∥International Conference on Discovery Science.Springer-Verlag,2011:1.
[2] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNetclassification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc,2012:1097-1105.
[3] DAHL G E,YU D,DENG L,et al.Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition[J].IEEE Transactions on Audio Speech & Language Processing,2012,0(1):30-42.
[4] KARPATHY A,TODERICI G,SHETTY S,et al.Large-Scale Video Classification with Convolutional Neural Networks[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2014:1725-1732.
[5] HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,8(7):1527-1554.
[6] VINCENT P,LAROCHELLE H,BENGIO Y,et al.Extracting and composing robust features with denoising autoencoders[C]∥International Conference on Machine Learning.DBLP,2008:1096-1103.
[7] CAO L L,HUANG W B,SUN F C.Building feature space of extreme learning machine with sparse denoising stacked-autoencoder[J].Neurocomputing,2016,4(PA):60-71.
[8] RETRIEVAL F T I I.Opinion Mining and Sentiment Analysis[J].Foundations & Trends in Information Retrieval,2008,2(1):459-526.
[9] PANG B,LEE L,VAITHYANATHAN S.Thumbs up?:sentiment classification using machine learning techniques[C]∥Conference on Empirical Methods in Natural Language Processing(Acl-02).Association for Computational Linguistics,2002:79-86.
[10] STOJANOVSKI D,STREZOSKI G,MADJAROV G,et al.Twitter Sentiment Analysis Using Deep Convolutional Neural Network[C]∥Hais.2015:726-737.
[11] LIU F L,YANG L,ZHANG S W,et al.Convolutional Neural Networks for Chinese Micro-blog Sentiment Analysis [J].Journal of Chinese Information Processing,2015,9(6):159-165.(in Chinese) 刘龙飞,杨亮,张绍武,等.基于卷积神经网络的微博情感倾向性分析[J].中文信息学报,2015,9(6):159-165.
[12] GLOROT X,BORDES A,BENGIO Y.Domain adaptation for large-scale sentiment classification:A deep learning approach[C]∥ International Conference on Machine Learning.Omnipress,2011:513-520.
[13] LIANG J,CHAI Y M,YUAN H B,et al.Deep Learning for Chinese Micro-blog Sentiment Analysis[J].Journal of Chinese Information Processing,2014,8(5):155-161.(in Chinese) 梁军,柴玉梅,原慧斌,等.基于深度学习的微博情感分析[J].中文信息学报,2014,8(5):155-161.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!