摘要
构建基于文本情感特征的心理评估模型.首先,根据词语的情感极性和词性设计词语特征,将文本中的每个词语映射成情感词向量,进而将其作为卷积神经网络的输入,并加入注意力机制对输出结果进行优化,得到包含情感特征的文本向量表示.其次,使用Bayes正则化算法优化权值,控制并平衡神经网络拟合程度,改进BP神经网络算法的网络泛化能力.最后,将文本向量作为Bayes正则化神经网络的输入,预测学生的心理状态,与心理评估结果的对比实验结果表明,模型效果较理想。
We constructed a psychological assessment model based o n text emotional characteristics. Firstly,acording to the emotional polarity an d part of speech of words,word features were designed,and each word in the tex t was mapped into an emotional word vector,which was then used as the input of convolutional neural network. Attention mechanism was added to optimize the outp ut results,the text vector representation containing emotional features was obt ained. Secondly,the Bayesian regularization algorithm was used to optimize the weights,control and balance the fitting degree of th e neural network,and improve the generalization ability of the BP neural networ k algorithm. Finally,the text vector was used as the input of Bayesian regulari zation neural network to predict the students’ psychological state. Compared wi th the results of psychological assessment,the experimental results show that t he effect of the model is ideal.
作者
杜天宝
于纯浩
温卓
孔馨
DU Tianbao;YU Chunhao;WEN Zhuo;KONG Xin(College of Software,Jilin University,Changchun 130012,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China;College of New Energy and Environment,Jilin University,Changchun 130012,China)
出处
《吉林大学学报(理学版)》
CAS
北大核心
2019年第4期927-932,共6页
Journal of Jilin University:Science Edition
基金
吉林省科技厅自然科学基金(批准号:20180101036JC)
关键词
情感特征
卷积神经网络
Bayes正则化
emotional characteristic
convolutional neural network
Bayesian regularization