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[1]许 犇,徐国庆*,程志宇,等.基于MGCNN的商品评论情感分析[J].武汉工程大学学报,2020,42(05):585-590.[doi:10.19843/j.cnki.CN42-1779/TQ.202006010]
 XU Ben,XU Guoqing*,CHENG Zhiyu,et al.Sentiment Analysis of Product Reviews Based on Memory Graph Convolutional Neural Network[J].Journal of Wuhan Institute of Technology,2020,42(05):585-590.[doi:10.19843/j.cnki.CN42-1779/TQ.202006010]
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基于MGCNN的商品评论情感分析(/HTML)
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《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
42
期数:
2020年05期
页码:
585-590
栏目:
机电与信息工程
出版日期:
2021-01-29

文章信息/Info

Title:
Sentiment Analysis of Product Reviews Based on Memory Graph Convolutional Neural Network
文章编号:
1674 - 2869(2020)05 - 0585 - 06
作者:
许 犇徐国庆*程志宇罗 京
武汉工程大学计算机科学与工程学院,湖北 武汉 430205
Author(s):
XU Ben XU Guoqing* CHENG Zhiyu LUO Jing
School of Computer Science & Engineering,Wuhan Institute of Technology, Wuhan 430205, China
关键词:
图卷积网络长短期记忆网络注意力模型商品评论情感分析
Keywords:
graph convolutional networklong short-term memory networkattention modelproduct reviewssentiment analysis
分类号:
TP391
DOI:
10.19843/j.cnki.CN42-1779/TQ.202006010
文献标志码:
A
摘要:
为了解决传统的深度学习模型会忽略语料库中全局词共现信息所包含的非连续和长距离语义的问题。本文提出记忆图卷积神经网络(MGCNN)引入注意力机制的商品评论情感分析方法。首先提取词与词、词与文档之间的关系,以全部的词和文档作为节点,将整个数据集构造成一个异构文本图。再基于图卷积网络(GCN)来构建用于图结构数据的神经网络,利用长短期记忆网络(LSTM)提取上下文相关特征,并使用注意力层获取重要特征。多组对比实验结果表明,本方法的分类效果更好,且随着训练集数据所占比例的降低,其优势更加显著。
Abstract:
To solve the problem that the traditional deep learning models ignore the discontinuous and long-distance semantics existing in the global word co-occurrence information in the corpus. This paper proposes a sentiment analysis method for product reviews by introducing the attention model into the memory graph convolutional neural network. We constructed a heterogeneous text graph from a data set by taking words and documents as nodes and considering relationships among them. Then, graph convolutional network was used to extract features of graph structure data. Additionally, the long short-term memory network was employed to extract context-related features. After that, the attention layer was used to focus on important features. The results of multiple comparison experiments show that our method has a better classification effect, and as the proportion of data in the training set decreases, its advantages become more significant.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-06-16 作者简介:许 犇,硕士研究生。E-mail:lklk54@foxmail.com *通讯作者:徐国庆,博士,副教授。E-mail:124148659@qq.com 引文格式:许犇,徐国庆,程志宇,等. 基于MGCNN的商品评论情感分析[J]. 武汉工程大学学报,2020,42(5):585-590.
更新日期/Last Update: 2020-11-02