基于改进图卷积神经网络的评论有用性识别<sup>*</sup>

doi:10.11925/infotech.2096-3467.2022.0129

数据分析与知识发现

2022, Vol. 6

Issue (11): 38-51 https://doi.org/10.11925/infotech.2096-3467.2022.0129

研究论文

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基于改进图卷积神经网络的评论有用性识别^*

李雪梅,蒋建洪(

)

桂林电子科技大学商学院桂林 541004

Identifying Useful Reviews with Improved Graph Convolutional Neural Network

Li Xuemei,Jiang Jianhong(

)

Commercial College, Guilin University of Electronic Technology, Guilin 541004, China

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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。

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	李雪梅
	蒋建洪

关键词 ：评论有用性, 组块分析, 特征观点对, 图卷积网络

Abstract：

[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.

Key words： Reviews Helpfulness Chunk Analysis Feature Opinion Pair Graph Convolutional Network

收稿日期: 2022-02-18 出版日期: 2023-01-13

ZTFLH:

TP391

基金资助:* 国家自然科学基金项目(71940008);教育部人文社会科学研究基金项目(17YJCZH074);桂林电子科技大学研究生教育创新计划资助项目(C21YJM00WX06)

通讯作者: 蒋建洪 E-mail: jjhome@guet.edu.cn

引用本文:

李雪梅,蒋建洪. 基于改进图卷积神经网络的评论有用性识别^*[J]. 数据分析与知识发现, 2022, 6(11): 38-51.
Li Xuemei,Jiang Jianhong. Identifying Useful Reviews with Improved Graph Convolutional Neural Network. Data Analysis and Knowledge Discovery, 2022, 6(11): 38-51.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0129 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I11/38

Fig.1 模型结构

Table1 特征组块的抽取和组合规则

Table 2 观点组块的抽取和组合规则

Table 3 特征-观点对抽取规则

Fig.2 节点连边关系示意
（注：箭线：隶属关系；实线：依存关系；虚线：语义近似关系；红线：包含关系；D：文档节点；C_Ft：粗粒度特征词节点；F_Ft：细粒度特征词节点；B_Ft：组块特征词节点；Opp：观点词节点。）

Table 4 手机多粒度特征词

Table 5 实验环境

Table 6 数据集介绍

模型参数	值
Epoch（迭代轮数）	50
网络层数 $L$	2
Optimizer（优化器）	Adam
Learning_rate（初始学习率）	0.01
Dropout_rate（丢弃率）	0.5
Hidden_unit（隐藏层单元数）	200
语义相似度阈值 $λ$	0.75

Table 7 FFGCN参数设置

Table 8 人工标注示例

Table 9 组块提取规则精准率

Fig.3 训练结果

Table 10 实验结果对比

Table11 各模型实验结果对比

Table12 分模块实验效果对比

Fig.4 不同网络层数和相似度阈值对模型的影响

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