## 1 Introduction  
Sentiment classification is a classic research topic in natural language processing (NLP), with promising application and research prospects. For instance, sentiment polarity analysis of product and service reviews can help businesses identify and resolve problems, enhancing user experience. It can also serve as a tool for public sentiment monitoring, analyzing people's comments on specific events to understand their emotional reactions and respond accordingly. Additionally, sentiment analysis can be used for mental health monitoring, helping individuals track their emotional state and detect mental disorders. In the field of chatbots, this technology can assist robots to promptly capture the emotions of interlocutors, facilitating deeper human-machine conversations. Furthermore, sentiment analysis can be applied in cross-cultural communication, reducing misunderstandings caused by cultural differences and enhancing communication and comprehension among people in a multilingual environment. An effective sentiment classification system must be able to predict the sentiment polarity of a text quickly and accurately, such as negative, neutral, or positive. Some previous research has treated sentiment classification as a general text classification task and utilized classic classification methods. Many methods based on statistics [1; 2], dictionary and rule-based [3; 4], as well as shallow machine learning models [5; 6] provide solutions for this task. In recent years, deep learning has attracted widespread attention, and three typical frameworks, namely convolutional neural networks (CNNs) [7], recurrent neural networks (RNNs) [8], and transformers [9], have also shown their abilities in this task. However, sentiment classification is different from other text classification tasks. It is a domain-specific issue. This implies that the same expression can convey different sentiment polarities or effects across domains. For instance, the word \"simple\" may serve as a synonym for \"not rich\" and \"handy\" when describing breakfasts in a hotel and the use of an electrical appliance, respectively. Even the same meaning can have varying sentiments across different domains. For the sentence \"This skirt is very beautiful, but the camera on my phone is subpar\", it's positive in the clothing domain, but negative in the phone domain. One simple method to resolve this issue is to train domain-specific classifiers for different domains. Nonetheless, they frequently require a large number of labeled samples to ensure satisfactory performance. In addition, it is impractical to create a sufficient labeled sample set for each domain. Thus, some studies have started to explore how to leverage data from multiple domains to improve the classification accuracies of domain-specific sentiment classifiers. Multi-task learning, which can achieve mutual benefits from different tasks by learning them together, offers fresh prospects to this matter. By simultaneously training multiple domain-specific classifiers, a number of models extract both shared and domain-specific features for sentiment classification. These methods can be roughly divided into two categories. One category uses independent components to learn shared and domain-specific features [10], like a neural network containing a shared layer and other task-specific layers. The other category uses a general model structure [11], such as a neural network used to generate a feature pool and then attention or other techniques applied to extract the features required for domain-specific sentiment classification from the pool. Although the aforementioned methods are efficient, we observe that they only concentrate on domain-related features and cannot directly model the relative importance of the features. In fact, this is essential for sentiment classification, particularly when a sentence contains descriptions from multiple aspects and the corresponding polarities are inconsistent.