摘要
跨领域文本情感分析时,为了使抽取的共享情感特征能够捕获更多的句子语义信息特征,提出域对抗和BERT(bidirectional encoder representations from transformers)的深度网络模型。利用BERT结构抽取句子语义表示向量,通过卷积神经网络抽取句子的局部特征。通过使用域对抗神经网络使得不同领域抽取的特征表示尽量不可判别,即源领域和目标领域抽取的特征具有更多的相似性;通过在有情感标签的源领域数据集上训练情感分类器,期望该分类器在源领域和目标领域均能达到较好的情感分类效果。在亚马逊产品评论数据集上的试验结果表明,该方法具有良好的性能,能够更好地实现跨领域文本情感分类。
In order to capture more sentence semantic information from the extracted shared sentiment features for cross-domain sentiment analysis,a deep network model based on domain adversarial mechanism and BERT(bidirectional encoder representations from transformers)was proposed.The model firstly used BERT to obtain the semantic representation vectors of sentences,and then extracted the local features of sentences with a convolutional neural network.A domain adversarial neural network was designed to make the representations of features extracted from different domains to be as indistinguishable as possible,that was,the features extracted from source domain and target domain had much more similarities;and a sentiment classifier was trained on the source domain dataset with sentiment labels,and it was expected that the trained sentiment classifier would have good classification performance in the source domain,and in the target domain.The experimental results on Amazon product reviews dataset showed that the proposed method achieved the expectation and was competent for achieving cross-domain text sentiment classification.
作者
蔡国永
林强
任凯琪
CAI Guoyong;LIN Qiang;REN Kaiqi(School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin 541004.Guangxi,China)
出处
《山东大学学报（工学版）》
CAS
CSCD
北大核心
2020年第1期1-7,20,共8页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(61763007)
广西自然科学基金重点资助项目(2017JJD160017)。
关键词
跨领域
情感分析
卷积神经网络
域对抗网络
共享情感特征
cross-domain
sentiment analysis
convolution neural network
domain adversarial network
shared sentiment features
