基于改进图卷积神经网络的评论有用性识别  

Identifying Useful Reviews with Improved Graph Convolutional Neural Network

作　　者：李雪梅 蒋建洪[1] Li Xuemei;Jiang Jianhong(Commercial College,Guilin University of Electronic Technology,Guilin 541000,China)

机构地区：[1]桂林电子科技大学商学院,桂林541000

出　　处：《数据分析与知识发现》2022年第11期38-51,共14页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71940008);教育部人文社会科学研究基金项目(项目编号:17YJCZH074);桂林电子科技大学研究生教育创新计划资助项目(项目编号:C21YJM00WX06)的研究成果之一。

摘　　要：【目的】充分建模评论中的特征观点语义偏差,提升评论有用性识别的性能。【方法】构建一种融合组块分析和特征隶属关系的FFGCN模型进行评论有用性识别。通过组块分析获得特征和观点词块作为图上节点,同时借助多粒度特征词库融入特征词间隶属关系构图,经过图上卷积进行评论二分类。【结果】FFGCN模型在两个数据集上的识别准确率分别为93.4%和93.9%,比基线模型最优结果分别提升0.9和1.0个百分点。【局限】选取手机评论数据进行实验,未将模型拓展到其他产品类型验证其识别性能。【结论】所提模型能够有效对评论文本进行建模,大大提高评论有用性识别的性能。[Objective]This paper tries to utilize the semantic deviation of comments,aiming to identify the useful online reviews.[Methods]We constructed an FFGCN model integrating chunk analysis and feature membership to evaluate the comments’usefulness.Then,we utilized chunk analysis to obtain the feature and opinion chunks as nodes on the graph.Third,with the help of multi-granularity feature thesaurus,we integrated the membership relationship between feature words into the graph.Finally,we classified the comments through convolution on the graph.[Results]The recognition accuracy of the FFGCN model on the two datasets were 93.4%and 93.9%,which were 0.9 and 1.0 percentadge point higher than the optimal results of the baseline model.[Limitations]We only examined the new model with mobile phone review data.More research is needed to evaluate the model with data sets from other fields.[Conclusions]The proposed model can effectively identify the helpful products reviews online.

关 键 词：评论有用性 组块分析 特征观点对 图卷积网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]