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计算机系统应用:2018,27(8):226-231
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面向手机动画的基于注意力机制和卷积神经网络的短信情感分析
宗玉英, 段晓芳
(北京工业大学 信息学部, 北京 100124)
Sentiment Analysis of Short Messages Based on Attention Mechanism and Convolution Neural Network for Mobile Animation
ZONG Yu-Ying, DUAN Xiao-Fang
(Faculty of Information Science, Beijing University of Technology, Beijing 100124, China)
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投稿时间:2017-12-07    修订日期:2017-12-27
中文摘要: 2008年,中科院张松懋研究员提出了将3D动画自动生成技术应用在手机短信中.短信情感分析是手机3D动画自动生成系统的一个重要环节.目前系统中使用的方法是传统的机器学习方法,准确率较低,无法达到实用的目的.而近几年,深度学习在情感分析任务中取到了较好的效果,卷积神经网络可以自动提取短信中的语义情感特征,且注意力机制可以自动为词加权获取信息.为此,本文提出将深度学习中的注意力机制和卷积神经网络相结合应用于手机短信自动生成系统中的情感分类.实验表明,基于注意力机制的卷积神经网络比之前的方法准确率、召回率和F值都有明显的提高.
Abstract:In 2008, ZHANG Song-Mao, a researcher of Chinese Academy of Sciences, proposed the application of 3D animation automatic generation technology for mobile phone SMS. The sentiment analysis of SMS is an important part of the 3D animation automatic generation system. At present, the method used in the system is a traditional machine learning method, which has a low accuracy and cannot achieve a practical purpose. In recent years, deep learning has achieved good results in the task of sentiment analysis. Convolutional neural network can automatically extract the semantic and sentiment features of text messages, and attention mechanism can automatically obtain weighting information for words. Therefore, this study proposes to apply the attention mechanism and convolutional neural network in deep learning to the classification of sentiment analysis in the system of SMS automatic generation. Experiments show that the convolutional neural network based on attention mechanisms has significantly improved the accuracy, recall rate, and F-value than the previous methods.
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宗玉英,段晓芳.面向手机动画的基于注意力机制和卷积神经网络的短信情感分析.计算机系统应用,2018,27(8):226-231
ZONG Yu-Ying,DUAN Xiao-Fang.Sentiment Analysis of Short Messages Based on Attention Mechanism and Convolution Neural Network for Mobile Animation.COMPUTER SYSTEMS APPLICATIONS,2018,27(8):226-231

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