基于上下文保持能力的方面级情感分类模型  

Aspect-Level Sentiment Classification Model Based on Context-Preserving Capability

作　　者：何丽[1] 房婉琳 张红艳 HE Li;FANG Wanlin;ZHANG Hongyan(School of Science and Technology,Tianjin University of Finance and Economics,Tianjin 300222)

机构地区：[1]天津财经大学理工学院,天津300222

出　　处：《模式识别与人工智能》2021年第2期157-166,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.11701410);天津市自然科学基金项目(No.18JCYBJC85100)资助。

摘　　要：方面级情感分类可发现语句在不同方面隐藏的情感特征.文中基于特定方面的图卷积网络的框架,构建基于上下文保持能力的方面级情感分类模型.在图卷积层中引入上下文门控单元,整合前一层输出中的有用信息.在基于图卷积网络的模型中加入多粒度注意力计算模块,描述方面词与上下文在情感表达上的相互关系.在5个公开数据集上的实验表明,文中模型在分类准确率和F1宏平均指标上均表现较优.Hidden emotional characteristics of the statement in various aspects can be discovered by aspect-level sentiment classification.Based on the framework of aspect-specific graph convolutional network,an aspect-level sentiment classification model based on context-preserving capability is proposed.A context gating unit is introduced into the graph convolution layer to reintegrate the useful information in the output of the previous layer.A multi-grained attention computing module is added to the proposed model to describe the interrelation in emotional expression between aspect words and their context.Experimental results on five public datasets show the advantages of the proposed model in classification accuracy and macro-F1.

关 键 词：方面级情感分类 图卷积网络 多粒度注意力计算 上下文保持能力 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]