Multi-domain sentiment classification is a challenging topic in natural language processing, where data from multiple domains are applied to improve the performance of classification. Recently, it has been demonstrated that attention neural networks exhibit powerful performance in this task. In the present study, we propose a collaborative attention neural network (CAN). A self-attention module and domain attention module work together in our approach, where the hidden states generated in the self-attention module are fed into both the domain sub-module and sentiment sub-module in the domain attention module. Compared with other attention neural networks, we use two types of attention modules to conduct the auxiliary and main sentiment classification tasks. The experimental results showed that CAN outperforms other state-of-the-art sentiment classification approaches in terms of the overall accuracy based on both English (Amazon) and Chinese (JD) multi-domain sentiment analysis data sets.

在自然语言处理中，多域情感分类是一个具有挑战性的主题，在该语言中，来自多个域的数据被应用于改善分类性能。最近，已经证明注意神经网络在该任务中表现出强大的性能。在本研究中，我们提出了一种协作注意神经网络（CAN）。自我关注模块和领域关注模块在我们的方法中一起工作，其中在自我关注模块中生成的隐藏状态被馈送到领域关注模块中的领域子模块和情感子模块中。与其他注意神经网络相比，我们使用两种类型的注意模块来执行辅助和主要情感分类任务。