[1]王柳迪,马伟锋,孙晓勇,等.基于双输入和BiLSTM-MHSA的评论文本方面情感分类方法[J].浙江科技学院学报,2023,(05):412-420.[doi:10.3969/j.issn.1671-8798.2023.05.007]
 WANG Liudi,MA Weifeng,SUN Xiaoyong,et al.Aspect-level sentiment classification method for comment texts based on dual input and BiLSTM-MHSA[J].,2023,(05):412-420.[doi:10.3969/j.issn.1671-8798.2023.05.007]
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基于双输入和BiLSTM-MHSA的评论文本方面情感分类方法(/HTML)
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《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2023年05期
页码:
412-420
栏目:
出版日期:
2023-10-31

文章信息/Info

Title:
Aspect-level sentiment classification method for comment texts based on dual input and BiLSTM-MHSA
文章编号:
1671-8798(2023)05-0412-09
作者:
王柳迪马伟锋孙晓勇王雨晨毛思佳
(浙江科技学院 信息与电子工程学院,杭州 310023)
Author(s):
WANG Liudi MA Weifeng SUN Xiaoyong WANG Yuchen MAO Sijia
(School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China)
关键词:
方面词抽取方面情感分类多任务学习用户评论
分类号:
TP391.43
DOI:
10.3969/j.issn.1671-8798.2023.05.007
文献标志码:
A
摘要:
【目的】针对方面情感分类输入类别在不同领域之间差异较大,汽车用户评论文本语义信息不全,语义特征难以提取等问题,提出基于双通道输入的并行双向编码表征(bidirectional encoder representation from transformers,BERT)双向长短期记忆多头自注意力模型的方面情感分类方法。【方法】首先采用了方面情感和方面抽取的双重标签进行标注; 其次通过并行的方面抽取和方面情感分类任务通道,分别使用BERT、双向长短期记忆网络(bidirectional long and short-term memory networks,Bi-LSTM)及多头注意力机制(multihead self-attention,MHSA)提取更深层次的语义信息及近距离和远距离特征信息; 最后采用条件随机场(conditional random field,CRF)分类器和Softmax分类器进行分类。【结果】在相关的汽车用户评论文本数据集和多语言混合数据集上,本研究提出的模型相较于主流的方面情感分类方法,具有同步抽取方面词和判断情感极性的能力,且有效提高了方面词抽取和方面情感分类的准确率和F1值。【结论】本研究提出的模型更有利于汽车销售者分析用户评论,同时对识别用户评论文本的情感极性的研究也有一定的参考价值。

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备注/Memo

备注/Memo:
收稿日期:2022-06-21
基金项目:浙江科技学院企业委托项目(2020KJ272)
通信作者:马伟锋(1979— ),男,浙江省绍兴人,副教授,硕士,主要从事大数据与人工智能应用研究。E-mail:mawf@zust.edu.cn。
更新日期/Last Update: 2023-10-31