摘要
情感分析随着人工智能的发展而逐渐受到重视,微博情感分析旨在研究用户对于社会热点事件的情感倾向,研究表明深度学习在情感分析上具有可行性.针对传统循环神经网络模型存在信息记忆丢失、忽略上下文非连续词之间的相关性和梯度弥散的问题,为此本文结合自注意机制和Tree-LSTM模型,并且在Tree-LSTM模型的输出端引入了Maxout神经元,基于以上两种改进基础上构建了SAtt-TLSTM-M模型.实验使用COAE2014评测数据集进行情感分析,实验结果表明:本文提出的模型相比于传统的SVM、MNB和LSTM模型准确率分别提高了16.18%、15.34和12.05%,其中引入了Maxout神经元的RMNN模型相对于LSTM模型准确率提高了4.10%,引入自注意力机制之后的Self-Attention+Tree-LSTM模型相比于Tree-LSTM模型准确率提高了1.85%,并在召回率和F值两项指标上均优于其他对比模型.由此证明,本文提出的SAtt-TLSTM-M模型可用于提高情感分析的准确率,具有一定的研究价值.
Sentiment analysis is valued with the development of artificial intelligence.Micro-blog sentiment analysis aims to study the emotional tendency of users to social hot events.Research shows that deep learning is feasible in sentiment analysis.In view of traditional cyclic neural network model,micro-blog sentiment analysis has information memory loss,ignores the correlation between context non-continuous words and gradient dispersion.Therefore,this paper combines the self-attention mechanism and the Tree-LSTM model,and introduces Maxout neuron at the output of the Tree-LSTM model.Based on the two improvemwnts,it constructed a Chinses microblog sentiment analysis model(SAtt-TLSTM-M).The experiment uses the COAE2014 evaluation data set for sentiment analysis.The experimental results show that the accuracy of the proposed model is improved by 16.18%,15.34%and 12.05%compared to the traditional SVM,MNB and LSTM models.The RMNN with Maxout neurons is improved by 4.10%compared with LSTM model.Self-Attention+Tree-LSTM model with the self-attention mechanism is improved by 1.85%compared with Tree-LSTM model.It is also superior to other contrast models in the recall rate and F value.Thus,It proves that the SAtt-TLSTM-M model in this paper can be used to improve the accuracy of sentiment analysis,which shows certain research value in research.
作者
石磊
张鑫倩
陶永才
卫琳
SHI Lei;ZHANG Xin-qian;TAO Yong-cai;WEI Lin(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;School of Software,Zhengzhou University,Zhengzhou 450002,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2019年第7期1486-1490,共5页
Journal of Chinese Computer Systems
基金
河南省高等学校重点科研项目(16A520027)资助
作者简介
石磊,男,1967年生,博士,教授,CCF会员,研究方向为高性能计算、Web数据挖掘;张鑫倩,女,1994年生,硕士研究生,研究方向为情绪分析、情感计算;陶永才,男,1975年生,博士,讲师,CCF会员,研究方向为网格计算、高性能计算,E-mail:ieyctao@zzu.edu.cn;卫琳,女,1968年生,硕士,副教授,CCF会员,研究方向为Web数据挖掘.
