广西科技大学学报
廣西科技大學學報
엄서과기대학학보
Journal of Guangxi University of Science and Technology
2015年
3期
36-40
,共5页
情感分类%深度学习%稀疏自动编码器
情感分類%深度學習%稀疏自動編碼器
정감분류%심도학습%희소자동편마기
sentiment classification%deep learning%sparse autoencoder
微博情感倾向分类是分析微博语句带有正向、负向或者中性情感。已有的研究大多根据手工标注微博情感极性进行有监督或半监督分类。该文主要结合了稀疏自动编码器和支持向量机，自动提取情感特征，实现了无监督的微博情感分类。实验结果表明：稀疏自动编码器在微博情感倾向分类精度上基本和手工标注情感特征算法相近，但是微博文本形式多变，自动提取情感特征适应性更强。
微博情感傾嚮分類是分析微博語句帶有正嚮、負嚮或者中性情感。已有的研究大多根據手工標註微博情感極性進行有鑑督或半鑑督分類。該文主要結閤瞭稀疏自動編碼器和支持嚮量機，自動提取情感特徵，實現瞭無鑑督的微博情感分類。實驗結果錶明：稀疏自動編碼器在微博情感傾嚮分類精度上基本和手工標註情感特徵算法相近，但是微博文本形式多變，自動提取情感特徵適應性更彊。
미박정감경향분류시분석미박어구대유정향、부향혹자중성정감。이유적연구대다근거수공표주미박정감겁성진행유감독혹반감독분류。해문주요결합료희소자동편마기화지지향량궤，자동제취정감특정，실현료무감독적미박정감분류。실험결과표명：희소자동편마기재미박정감경향분류정도상기본화수공표주정감특정산법상근，단시미박문본형식다변，자동제취정감특정괄응성경강。
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.
