广西科技大学学报Issue(3)：36-40,5.DOI:10.16375/j.cnki.cn45-1395/t.2015.03.007
基于稀疏自动编码器的微博情感分类应用研究
Research of micro-blog sentiment classification based on sparse autoencoder
摘要
Abstract
Micro-blog sentiment classification analysis is to analyze the emotions that macro-blog statements contain, such as positive, negative or neutral emotions. Most of the existing research is based on manual annotation of micro -blog emotion to conduct supervised or semi -supervised classification. This paper automatically extracts emotional characteristics and achieves unsupervised micro-blog sentiment classification by integrating the sparse autoencoders with support vector machines. Experimental results show that sparse autoencoder is applied to micro-blog emotion tendency classification, although the accuracies are close to manual annotation emotional characteristics algorithm, since micro-blog text is changeable, the model with automatically extracting emotional characteristics is adaptable.关键词
情感分类/深度学习/稀疏自动编码器Key words
sentiment classification/deep learning/sparse autoencoder分类
信息技术与安全科学引用本文复制引用
秦胜君..基于稀疏自动编码器的微博情感分类应用研究[J].广西科技大学学报,2015,(3):36-40,5.基金项目
广西教育厅人文社科研究项目（SK13YB069）资助 （）
