为高效提取不同卷积层窗口的文本局部语义特征, 提出一种深度卷积神经网络 (CNN) 模型.通过堆叠多个卷积层, 提取不同窗口的局部语义特征.基于全局最大池化层构建分类模块, 对每个窗口的局部语义特征计算情感类别得分, 综合类别得分完成情感分类标注.实验结果表明, 与现有CNN模型相比, 该模型具有较快的文本情感分类速度.%This paper proposes a deep Convolutional Neural Network (CNN) model to efficiently extract the local semantic features of different convolutional layer windows for text.The model avoids manually specifying multiple window sizes and retains local semantic features of different windows by stacking a number of convolutional layers.Classification modules are built based on the Global Max Pooling (GMP) layer to calculate the category score for the local semantic features of each window.The model synthesizes these category scores to complete the sentiment classification annotation.Experimental results show that the model has faster text sentiment classification speed than that of other CNN models.
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