基于深度卷积神经网络模型的文本情感分类

doi:10.19678/j.issn.1000-3428.0050043

计算机工程 ›› 2019, Vol. 45 ›› Issue (3): 300-308. doi: 10.19678/j.issn.1000-3428.0050043

基于深度卷积神经网络模型的文本情感分类

周锦峰,叶施仁,王晖

常州大学信息科学与工程学院,江苏常州 213164

收稿日期:2018-01-10 出版日期:2019-03-15 发布日期:2019-03-15
作者简介:周锦峰(1978—),男,硕士,主研方向为机器学习、自然语言处理;叶施仁,副教授、博士;王晖(通信作者),讲师、博士。
基金资助:
国家自然科学基金(61272367);江苏省科技厅项目(BY2015027-12)。

Text Sentiment Classification Based on Deep Convolutional Neural Network Model

ZHOU Jinfeng,YE Shiren,WANG Hui

School of Information Science and Engineering,Changzhou University,Changzhou,Jiangsu 213164,China

Received:2018-01-10 Online:2019-03-15 Published:2019-03-15

摘要/Abstract

摘要：

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。

关键词: 情感分析, 情感分类标注, 深度学习, 卷积神经网络, 词向量

Abstract:

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.

Key words: sentiment analysis, sentiment classification annotation, deep learning, Convolutional Neural Network(CNN), word vector

中图分类号:

TP183

周锦峰,叶施仁,王晖. 基于深度卷积神经网络模型的文本情感分类[J]. 计算机工程, 2019, 45(3): 300-308.

ZHOU Jinfeng,YE Shiren,WANG Hui. Text Sentiment Classification Based on Deep Convolutional Neural Network Model[J]. Computer Engineering, 2019, 45(3): 300-308.

http://www.ecice06.com/CN/Y2019/V45/I3/300

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