...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Text Emotion Analysis Method Using the Dual-Channel Convolution Neural Network in Social Networks
【24h】

A Text Emotion Analysis Method Using the Dual-Channel Convolution Neural Network in Social Networks

机译：一种在社交网络中双通道卷积神经网络的文本情感分析方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In order to solve the problem that the existing deep learning method has insufficient ability in feature extraction in the text emotion classification task, this paper proposes a text emotion analysis using the dual-channel convolution neural network in the social network. First, a double-channel convolutional neural network is constructed. Combined with emotion words, parts of speech, degree adverbs, negative words, punctuation, and other word features that affect the text’s emotional tendency, an extended text feature is formed. Then, using the CNN’s multichannel mechanism, the extended text features based on the word vector features and the semantic features based on the word vectors are, respectively, input into the CNN model. After each convolution operation of the convolution channel, the BN technology is used to normalize the internal data of the network and the padding technology is used to improve the ability of the model to extract edge features of the data and the speed of the model. Finally, a dynamic k-max continuous pooling strategy is adopted to realize the dimensionality reduction of features and enhance the model’s ability to extract features. The experimental results show that the accuracy and F1 values obtained by the proposed method can be as high as 94.16% and 92.61%, respectively, which are better than several comparison algorithms.
机译：为了解决现有的深度学习方法在文本情感分类任务中具有特征提取能力不足的问题，本文提出了一种在社交网络中使用双通道卷积神经网络的文本情感分析。首先，构建双通道卷积神经网络。结合情感词语，言语，学位副词，否定词，标点符号和其他影响文本情绪倾向的其他字特征，形成了扩展文本特征。然后，使用CNN的多通道机制，基于Word Vector特征和基于字向量的语义特征的扩展文本特征分别输入CNN模型。在卷积通道的每个卷积操作之后，BN技术用于标准化网络的内部数据，填充技术用于提高模型提取数据的边缘特征的能力和模型的速度。最后，采用动态K-MAX连续汇集策略来实现特征的维度降低，提高模型提取特征的能力。实验结果表明，通过该方法获得的精度和F1值分别高达94.16％和92.61％，优于几种比较算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号