摘要
随着电力业务的发展,客服环节时刻产生着大量的数据,然而传统对话数据情感检测方法对于客服质量检测的手段存在着诸多的问题和挑战.本文根据词语出现的排列和定位构建字图,对整个语句进行非连续长距离的语义建模;并针对文档不同组成部分之间的关系,对语句上下文之间的交互依赖或自我依赖关系进行建模;最后通过卷积神经网络对所构建的图进行特征提取和邻域节点的特征聚合以得到文本的最终特征表示,进而实现客服通话过程中的情绪状态检测.通过实验证明本文提出的模型情感分类性能指标始终高于基线模型,这表明融合词共现关系、顺序语句上下文编码和交互语句上下文编码结构可以有效提高情感类别检测精度.该方法为智能化、自动化地检测客服通话过程中的情绪状态提供了更细粒度的分析,为有效地提高客服服务质量具有重要意义.
With the development of power business,a large amount of data is produced in the link of customer service.However,traditional sentiment analysis methods for dialogues face many problems and challenges in customer service quality detection.In this study,the word graph is constructed according to the arrangement and location of words,and then the discontinuous long-distance semantic modeling of the whole sentence is carried out.Next,according to the relationship among different parts of the document,the self and interaction dependency relationships between sentence contexts are modeled,respectively.Finally,the convolutional neural network(CNN)is applied to the constructed graph for feature extraction and feature aggregation of the neighbor nodes to obtain the final feature representation of the text.In this way,the detection of emotional states is realized in customer dialogues.Experimental results show that the performance of the proposed model is always higher than that of the baseline model,which demonstrates that the fusion of word co-occurrence relationships,as well as sequential context coding and interactive context coding structures,can effectively improve the accuracy of sentiment category detection.This method provides a fine-grained analysis for intelligently and automatically detecting the emotional states in customer dialogues,which is of great significance to effectively improve the quality of customer service.
作者
孟洁
李妍
赵迪
张倩宜
刘赫
MENG Jie;LI Yan;ZHAO Di;ZHANG Qian-Yi;LIU He(State Grid Tianjin Information&Telecommunication Company,Tianjin 300010,China;Key Laboratory of Energy Big Data Simulation of Tianjin Enterprise,Tianjin 300010,China)
出处
《计算机系统应用》
2022年第5期147-156,共10页
Computer Systems & Applications
基金
国网科技项目(KJ20-1-15)。
关键词
对话情感分析
异质网络
图卷积网络
注意力机制
双向门控循环单元
dialogue sentiment analysis
heterogeneous network
graph convolutional network(GCN)
attention mechanism
bi-directional gated recurrent unit(Bi-GRU)
