Cross-domain sentiment classification based on syntactic structure transfer and domain fusion

[Objective] Deep learning models for text sentiment analysis, such as recurrent neural networks, often require many parameters and a large amount of high-quality labeled training data to effectively train and optimize recurrent neural networks. However, obtaining domain-specific high-quality sentiment-labeled data is a challenging task in practical applications. This study proposes a cross-domain text sentiment classification method based on syntactic structure transfer and domain fusion (SSTDF) to address the domain-invariant learning and distribution distance difference metric problems. This method can effectively alleviate the dependence on domain-specific annotated data due to the difference in the data distribution among different domains.[Methods] A method combining SSTDF was proposed in this study to solve the problem of cross-domain sentiment classification. Dependent syntactic features are introduced into the recurrent neural network for syntactic structure transfer for designing a migratable dependent syntactic recurrent neural network model. Furthermore, a parameter transfer strategy is employed to transfer syntactic structure information across domains efficiently for supporting sentiment transfer. The conditional maximum mean discrepancy distance metric is used in domain fusion to quantify the distribution differences between the source and target domains and further refine the cross-domain same-category distance metric information. By constraining the distributions of source and target domains, domain variable features are effectively extracted to maximize the sharing of sentiment information between source and target domains. In this paper, we used a joint optimization and training approach to address cross-domain sentiment classification. Specifically, the sentiment classification loss of source and target domains is minimized, and their fusion losses are fully considered in the joint optimization process. Hence, the generalization performance of the model and classification accuracy of the cross-domain sentiment classification task are considerably improved.[Results] The dataset used in this study is the sentiment classification dataset of Amazon English online reviews, which has been widely used in cross-domain sentiment classification studies; furthermore, it contains four domains—B (Books), D (DVD), E (Electronic), and K (Kitchen)—each with 1 000 positive and negative reviews. The experimental results show that the accuracy of the SSTDF method is higher than the baseline method, achieving 0. 844, 0. 830, and 0. 837 for average accuracy, recall, and Fl values, respectively. Fine-tuning allows the fast convergence of the network, thereby improving its transfer efficiency.[Conclusions] Finally, we used deep transfer learning methods to solve the task of cross-domain text sentiment classification from the perspective of cross-domain syntactic structure consistency learning. A recurrent neural network model that integrates syntactic structure information is used; additionally, a domain minimum distance constraint is added to the syntactic structure transfer process to ensure that the distance between the source and target domains is as similar as possible during the learning process. The effectiveness of the proposed method is finally verified using experimental results. The next step is to increase the number of experimental and neutral samples to validate the proposed method on a larger dataset. Furthermore, a more fine-grained aspect-level cross-domain sentiment analysis will be attempted in the future. © 2023 Press of Tsinghua University. All rights reserved.