文献类型：期刊文献

中文题名：基于注意力网络的属性级别情感分析

英文题名：Aspect-based sentiment analysis based on attention network

作者：沈斌[1,2];房一泉[1,2];蔡源[1];程华[2];钟烨[2]

机构：[1]华东理工大学信息化办公室,上海200237;[2]华东理工大学信息科学与工程学院,上海200237

年份：2022

卷号：39

期号：2

起止页码：411

中文期刊名：计算机应用研究

外文期刊名：Application Research of Computers

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：赛尔网络下一代互联网技术创新资助项目(NGII20170520)。

语种：中文

中文关键词：属性级别;情感分析;多头注意力;BERT

外文关键词：aspect-based;sentiment analysis;multi-head attention;BERT

摘要：传统的属性级别情感分析方法缺乏对属性实体与前后文之间交互关系的研究,导致情感分类结果的正确率不高。为了有效提取文本特征,提出了一种利用多头注意力机制学习属性实体与前后文之间关系的属性级别情感分析模型(intra&inter multi-head attention network,IIMAN),从而提高情感极性判断结果。该模型首先利用BERT预训练完成输入语句的词向量化;通过注意力网络中的内部多头注意力与联合多头注意力学习属性实体与前后文以及前后文内部间的关系;最后通过逐点卷积变换层、面向属性实体的注意力层和输出层完成情感极性分类。通过在三个公开的属性级别情感分析数据集Twitter、laptop、restaurant上的实验证明,IIMAN相较于其他基线模型,正确率和F_(1)值有了进一步的提升,能够有效提高情感极性分类结果。
Traditional aspect-based sentiment analysis methods lack the research on the interaction between aspect object and its context,which leads to the low accuracy of sentiment analysis.In order to effectively extract richer text features,this paper proposed an aspect-based sentiment analysis model(IIMAN)to improve the effect of sentiment polarity judgment,which used multi-head attention mechanism to learn the interaction between aspect object and its context.Firstly,the model used the BERT pre-training model to complete the word vectorization of the input sentences.Then,it used the intra multi-head attention network and the inter multi-head attention network in the attention network to learn the interaction between aspect object and its context,as well as the internal interactions within the context.Finally,it realized the sentiment polarity classification through the pointwise convolution transfer layer,the aspect object attention layer and the output softmax layer.Through the experiments on three public aspect-level sentiment analysis datasets:Twitter,laptop and restaurant,the results show that,compared with other baseline mo-dels,IIMAN model achieves better effect in accuracy and F_(1) value,improves the results of the sentiment polarity classification.