摘要
针对目标人物的情绪变化,本文提出了一种情绪预测方式对情绪进行识别、预测和分析.在情绪预测前,通过一种情绪定量算法对情绪数据集数据进行归一化处理,以得到每种情绪对应的程度系数,为下一步的情绪预测奠定基础.然后汇总目标人物一天的情绪变化得到一种主要情绪,再通过情绪预测算法得到最终的预测结果.本文应用BERT(Bidirectional Encoder Representations from Transformers)神经网络对短对话进行情绪建模,以做到对目标人物的实时情绪预测.结果表明应用本文的训练模型,可以有效判断目标人物的未来情绪波动状况.
Aiming at the target person’s emotional changes,this study proposes a method of emotion prediction to identify,predict,and analyze emotions.Before sentiment prediction,a sentiment quantitative algorithm is used to normalize the sentiment data set to obtain the degree coefficient corresponding to each sentiment,which lays the foundation for the next sentiment prediction.Then,we summarize the mood changes of the target person for one day to get a main mood,and then use the mood prediction algorithm to get the final prediction result.In this study,Bidirectional Encoder Representations from Transformers(BERT)neural network is used to model the emotion of short dialogues in order to achieve real-time emotion prediction of target person.The results show that the application of the training model in this study can effectively determine the future mood fluctuations of the target person.
作者
刘勇
王振
LIU Yong;WANG Zhen(Information Science and Technology Academy,Qingdao University of Science and Technology,Qingdao 266100,China)
出处
《计算机系统应用》
2020年第6期211-217,共7页
Computer Systems & Applications
基金
国家自然科学基金(91546203)。
关键词
情绪预测
情绪定量分析
情绪程度系数
BERT神经网络
emotional prediction
emotional quantitative analysis
emotional degree coefficient
BERT neural network
