[1]周湘贞,李 帅,隋 栋.基于深度学习和注意力机制的微博情感分析[J].南京师大学报(自然科学版),2023,46(02):115-121.[doi:10.3969/j.issn.1001-4616.2023.02.015]
　Zhou Xiangzhen,Li Shuai,Sui Dong.Microblog Emotion Analysis Based on Deep Learning and Attention Mechanism[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(02):115-121.[doi:10.3969/j.issn.1001-4616.2023.02.015]
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基于深度学习和注意力机制的微博情感分析()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第46卷
期数:
2023年02期
页码:
115-121
栏目:
计算机科学与技术
出版日期:
2023-06-15

文章信息/Info

Title:
Microblog Emotion Analysis Based on Deep Learning and Attention Mechanism
文章编号:
1001-4616(2023)02-0115-07
作者:
周湘贞1; 2; 李 帅2; 隋 栋3
(1.郑州升达经贸管理学院信息工程学院,河南 郑州 451191)
(2.北京航空航天大学计算机学院,北京 100191)
(3.北京建筑大学电气与信息工程学院,北京 102406)
Author(s):
Zhou Xiangzhen1; 2; Li Shuai2; Sui Dong3
(1.School of Information Engineering,Zhengzhou Shengda College of Economics and Trade Management,Zhengzhou 451191,China)
(2.School of Computer Science,Beijing University of Aeronautics and Astronautics,Beijing 100191,China)
(3.School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 102406,China)
关键词:
微博情感; 深度学习; 循环神经网络; 注意力机制
Keywords:
microblog emotion;  deep learning;  recurrent neural network;  attention mechanism
分类号:
TP312; G254
DOI:
10.3969/j.issn.1001-4616.2023.02.015
文献标志码:
A
摘要:
为了提高微博情感分析的性能,采用深度学习算法中的循环神经网络用于情感分类,并采用注意力机制对词特征进行选择加权,以增强循环神经网络的分类的准确率. 首先,将微博语料进行去噪、分词、向量化等处理,形成微博初始样本. 然后,构建循环神经网络的微博分类模型,通过隐藏层节点循环,并结合历史时刻及当前时刻隐藏层输出获得词特征向量. 接着,注意力机制用于词特征相似计算及选择加权构建句子特征,并采用Softmax函数获得分类结果. 最后,通过微博情感分类仿真测试验证了所提方法的可靠性. 实验结果表明,相比常用微博情感分类算法,通过合理设置注意力机制窗口大小,所提方法在不同词向量规模样本下均表现出更高的分类性能.
Abstract:
In order to improve the performance of Weibo's sentiment analysis, the recurrent neural network in the deep learning algorithm is used for sentiment classification, and the attention mechanism is used to select and weight the word features, so as to enhance the classification accuracy of the recurrent neural network. First, the Weibo corpus is denoised, segmented and vectorized to form an initial sample of Weibo. Then, the Weibo classification model of recurrent neural network is constructed, and the word feature vector is obtained through the node circulation of hidden layer and the output of hidden layer at historical moment and current moment. Then, the attention mechanism is used to calculate the similarity of word features and select weights to construct sentence features, and the classification results are obtained by Softmax function. Finally, the reliability of the proposed method is verified by the Weibo emotion classification simulation test. Experimental results show that, compared with the commonly used Weibo emotion classification algorithm, the proposed method shows higher classification performance under different word vector size samples by setting the attention mechanism window size reasonably.

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备注/Memo

备注/Memo:
收稿日期:2023-01-03.
基金项目:国家自然科学青年基金项目(61702026)、河南省2022年度科技厅科技攻关项目(222102210290)、校级2021应用基础研究与应用专项项目(SD-ZDIAN2021-05).
通讯作者:隋栋,博士后,讲师,研究方向:人工智能与大数据. E-mail:619543699@qq.com
更新日期/Last Update: 2023-06-15