基于图卷积网络的客服对话情感分析

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...

Sentiment Polarity Analysis of Customer Dialogues Based on Graph Convolutional Network

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.