融合稀疏因子的情感分析堆叠降噪自编码器模型
发布时间：2018-08-02 21:28
【摘要】：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。
[Abstract]:Feature extraction based on depth learning is a hot topic in the field of data dimensionality reduction. As a common model, stacked self-encoder can not express the features of noisy and sparse data well. For Weibo emotional analysis, the influence of noise and sparseness of sample data on feature extraction is solved by adding sparse factor into each hidden layer of stack de-noising self-encoder. The experiment of emotion analysis using COAE data set shows that the accuracy and recall rate of the proposed model classification are improved.
【作者单位】： 北京工业大学信息学部;
【分类号】：TP391.1
，
本文编号：2160790
[Abstract]:Feature extraction based on depth learning is a hot topic in the field of data dimensionality reduction. As a common model, stacked self-encoder can not express the features of noisy and sparse data well. For Weibo emotional analysis, the influence of noise and sparseness of sample data on feature extraction is solved by adding sparse factor into each hidden layer of stack de-noising self-encoder. The experiment of emotion analysis using COAE data set shows that the accuracy and recall rate of the proposed model classification are improved.
【作者单位】： 北京工业大学信息学部;
【分类号】：TP391.1
，
本文编号：2160790
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