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.%基于深度学习的特征抽取是目前数据降维问题的研究热点,堆叠自编码器作为一种较为常用的模型,无法对混有噪声及较稀疏的数据进行良好的特征表达.面向微博情感分析,通过在堆叠降噪自编码器的各隐藏层中加入稀疏因子,来解决样本数据所含噪声和稀疏性对特征抽取的影响.使用COAE评测数据集进行的情感分析实验表明所提模型分类的准确率和召回率都有所提高.
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