[1]吴琼,陈锻生.多尺度卷积循环神经网络的情感分类技术[J].华侨大学学报(自然科学版),2017,38(6):875-879.[doi:10.11830/ISSN.1000-5013.201606077]
　WU Qiong,CHEN Duansheng.Sentiment Classification With Multiscale Convolutional Recurrent Neural Network[J].Journal of Huaqiao University(Natural Science),2017,38(6):875-879.[doi:10.11830/ISSN.1000-5013.201606077]
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多尺度卷积循环神经网络的情感分类技术()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第6期
页码:
875-879
栏目:
出版日期:
2017-11-20

文章信息/Info

Title:
Sentiment Classification With Multiscale Convolutional Recurrent Neural Network
文章编号:
1000-5013(2017)06-0875-05
作者:
吴琼;  陈锻生
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
WU Qiong;  CHEN Duansheng
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
文本情感分类;  卷积神经网络;  循环神经网络;  长短时记忆;  多尺度
Keywords:
text sentiment classification;  convolutional neural network;  recurrent neural network;  long short-term memory;  multiscale
分类号:
TP391.4
DOI:
10.11830/ISSN.1000-5013.201606077
文献标志码:
A
摘要:
结合卷积神经网络对于特征提取的优势和循环神经网络的长短时记忆算法的优势,提出一种新的基于多尺度的卷积循环神经网络模型,利用卷积神经网络中的多尺寸滤波器提取出具有丰富上下文关系的词特征,循环神经网络中的长短时记忆算法将提取到的词特征与句子的结构联系起来,从而完成文本情感分类任务.实验结果表明:与多种文本情感分类方法相比,文中算法具有较高的精度.
Abstract:
Combining the advantages of convolution neural network(CNN)for feature extraction and recurrent neural network(RNN)for long shot-term memory, a new model based on multiscale convolutional recurrent neural network is proposed. This model utilize multi-size filter of CNN to extract word feature which contain a rich context information and use the long short-term memory algorithm of RNN to reflect the grammatical relations about the word and the sentence, and then completing the sentiment classification task. The experimental results show that: through comparing with many other sentiment classification, this new model has a high accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-06-28
通信作者: 陈锻生(1959-),男,教授,博士,主要从事计算机视觉与多媒体技术的研究.E-mail:dschen@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61370006); 福建省科技计划(工业引导性)重点项目(2015H0025)
更新日期/Last Update: 2017-11-20