计算机应用与软件2018，Vol.35Issue(1)：298-303,6.DOI:10.3969/j.issn.1000-386x.2018.01.052
L2-SVM下的短文本情感分类动态CNN模型
A SHORT TEXT SENTIMENT CLASSIFICATION MODEL BASED ON L2-SVM AND DYNAMIC CONVOLUTION NEURAL NETWORK
摘要
Abstract
An LDCNN model based on L2-SVM and dynamic convolution neural network is proposed to solve the problem of sparse emotion classification text,over-depending on emotional dictionary in traditional method and human setting feature engineering.The model adopted L2-SVM objective function rather than the classical CNN model to solve the gradient dispersion phenomenon in parameter optimization process.After comparing the real network review data set and the classical method quantitatively,there is a significant improvement in the accuracy of LDC model and the best model performance is obtained by adjusting the penalty coefficient.关键词
短文本/情感分类/文本稀疏/L2-SVM/动态卷积神经网络Key words
Short-text/Sentiment classification/Text sparse/Squared hinge loss SVM/Dynamic convolution neural network分类
信息技术与安全科学引用本文复制引用
鲁新新,柴岩..L2-SVM下的短文本情感分类动态CNN模型[J].计算机应用与软件,2018,35(1):298-303,6.
