Research On Cross-Domain Sentiment Analysis Based On Transfer Learning

With the popularization and development of the Internet,a large amount of comment data and information has been generated through communication and interaction among online users.This data contains users’ opinions and views on products,services,and social events,and reflects their personal emotional tendencies.Effectively processing and analyzing these text data using sentiment analysis techniques has great practical value and significance,as it can help users make decisions,improve product quality for businesses,and assist governments in guiding public opinion,etc.Currently,many existing sentiment analysis works still rely on a large amount of labeled data for training.However,the explosive growth of online comment data has resulted in an increasing number of text domains,and labeling data for each domain is not only time-consuming and labor-intensive but also may be inadequate for some domains.In addition,the background information and data distribution between different domains often differ,meaning that models trained in a specific domain may not necessarily be effective when applied directly to other domains.Therefore,using labeled data-rich domain(source domain)to assist labeled data-poor domain(target domain)in cross-domain sentiment analysis has become an important research topic with both theoretical and practical value.Depending to the number of source domains,cross-domain sentiment analysis research can be divided into two different dimensions: single-source domain and multi-source domain,this paper carries out relevant research work based on the ideas and methods of transfer learning,which are summarized as follows:(1)In the single-source domain dimension,many studies attempt to use adversarial learning to obtain domain-invariant knowledge to reduce inter-domain distribution discrepancy,but this may result in the distortion of original feature representations that contain discriminative knowledge,leading to inconsistent conditional distributions across domains.In response,this paper proposes a Moka-ADA(Adversarial Domain Adaptation with Model-Oriented Knowledge Adaptation)model based on domain adversarial adaptation and dual-level knowledge adaptation.The model adopts Adversarial Discriminative Domain Adaptation(ADDA)architecture as the basis for adversarial training framework to learn domain-invariant knowledge for marginal distribution alignment.Meanwhile,the model designs a model-oriented knowledge adaptation module for conditional distribution alignment,which is a dual-level knowledge adaptation structure that transfers knowledge at both the intermediate feature representation and final classification probability stages.This allows the target model in training to learn discriminative sentiment knowledge from the well-trained source model.Finally,experiments on 12 single-source domain transfer tasks of the Amazon review benchmark dataset show that the proposed model outperforms the comparison models in terms of accuracy performance.Moreover,ablation experiments and visualized feature representations verify the role of dual-level knowledge adaptation.(2)In the multi-source domain dimension,the direct application of single-source domain methods cannot guarantee its effectiveness,and further consideration is needed for the relationship between each source domain and the target domain,as multi-source domain may also have distribution discrepancy with each other.In response,this paper proposes a DG-MDA(Multi-Domain Adversarial with Domain Genes)model based on multidomain adversarial and domain genes.Firstly,the model designs a knowledge selection and fusion method based on domain genes to initialize the target encoder,enabling it to have stable and good rapid learning ability.Secondly,each source domain is paired with the target domain to form a separate domain pair,and each domain pair is mapped to different feature spaces for domain adversarial training,learning domain-invariant knowledge between each domain pair to promote domain distribution alignment in their respective spaces.Then,fuse different source domain classification decision boundaries based on domain genes to comprehensively judge the sentiment polarity of the target domain data.Finally,experiments on 4 multi-source domain transfer tasks of the Amazon review benchmark dataset and FDU-MTL dataset respectively show that the proposed model achieves more competitive accuracy performance.Moreover,ablation experiments and threshold parameter analysis show the impact of domain genes.