## 5 Conclusions  
To further deal with the conflicting sentiment information contained in the input text, we proposed an attention neural network with a novel attention-pooling mechanism, AP-MDSC, to model the relative importance of domain-aware sentiment features. We demonstrated the advantages of AP-MDSC on a public multi-domain sentiment classification dataset and validated various settings of the model through a series of comparative experiments. Meanwhile, we observed some limitations of AP-MDSC, including its sensitivity to multi-task conflicts. In future work, we plan to delve into the balance algorithms between multi-tasks in our proposed attention neural network to improve model performance. Additionally, we will conduct more research on sentiment classification tasks in real sentiment classification scenarios based on large-size datasets to extend the boundaries of our work. Conceptualization, C.Y. and A.L. ; data curation, S.G.; formal analysis, C.Y. and A.L. ; funding acquisition, C.Y. and Z.C. ; investigation, Z.C. and G.L. ; methodology, C.Y. and A.L. ; project administration, Z.C. ; resources, A.L. ; software, C.Y. ; supervision, G.L. ; validation, A.L. ; visualization, A.L. ; writing--original draft, C.Y. ; writing--review and editing, Z  
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