基于胶囊网络的跨领域情感分类方法

Cross-domain sentiment classification by capsule network

情感分析主要基于文本数据研究人们对于商品、服务、事件等对象的情感、意见或者态度.标记数据稀缺是情感分析领域面临的巨大挑战.在有监督的情感分类任务中,标记数据稀少会导致分类器的效果下降.跨领域的方法能够在一定程度上帮助解决该问题,但领域间往往存在差异.因此在利用领域适应方法进行情感分类时,分类器对目标领域的效果会变差.本文提出利用少量的目标领域标记信息来提高目标领域适应效果的思想.特别地,本文提出了一个基于胶囊网络的跨领域情感分类模型,在此基础框架上,设计了额外的胶囊网络层辅助目标领域的适应.在真实数据集上的实验结果表明,本文提出的模型效果优于以往的研究方法.

Sentiment analysis aims to extract users' sentimentsand opinions about or their attitude towarda specific product,service,or event.The lack of labeled data is a significant challenge in sentiment analysisand will deteriorate the performance of the classifierin a supervised sentiment analysis task.The cross-domain approach has been shown to be effective in addressing this problem.However,the inherent difference between the source and target domains will make it difficult for the classifier to be adaptive to the target domain.In this paper,we propose a novel method to use the available labeled data,however few they may be,in the target domain to enhance the domain adaption.Specifically,we present a cross-domain sentiment classification model using the capsule network.Based on this architecture,we design extra capsule layers for domain adaption.Extensive experiments with real-world datasets prove that our proposed model outperforms baselines by a large margin.