Due to the complex semantics of natural language, the multi-sentiment polarity of words, and the long-dependence of sentiments between words, the existing sentiment analysis methods (especially Chinese textual sentiment analysis) still face severe challenges. Aware of these issues, this paper proposes a scalable multi-channel dilated joint architecture of convolutional neural network and bidirectional long short-term memory (CNN–BiLSTM) model with an attention mechanism to analyze the sentiment tendency of Chinese texts. Through the multi-channel structure, this model can extract both the original context features and the multiscale high-level context features. Importantly, the number of the model channel can be optimally expanded according to the actual corpus. Furthermore, the attention mechanism including local attention and global attention is adopted to further distinguish the difference of features. The former is employed to weight the output features of each channel, and the latter is used to weight the fused features of all channels. Besides, an adaptive weighted loss function is designed to effectively avoid the imbalance of classes in training data. Finally, several experiments are performed to demonstrate the superior performance of the proposed model on two public datasets. Compared with word-level methods, the accuracy and Macro-$F1$ are respectively increased by over 1.19% and 0.9% on NLPCC2017-ECGC corpus, the accuracy and $F1$ are respectively increased by more than 1.7% and 1.214% on ChnSentiCorp-Htl-unba-10000 corpus. Compared with char-level pre-training methods, the accuracy and Macro-$F1$ also respectively achieve an improvement of over 3.416% and 4.324% on the NLPCC2017-ECGC corpus, the accuracy and $F1$ are respectively increased by more than 0.14% and 3% on the ChnSentiCorp-Htl-unba-10000 corpus.

由于自然语言的语义复杂，单词的多情感极性以及单词之间情感的长期依赖性，现有的情感分析方法（尤其是中文文本情感分析）仍然面临严峻挑战。意识到这些问题，本文提出了一种卷积神经网络和双向长短期记忆（CNN–BiLSTM）模型的可扩展多通道扩张联合架构，并带有一种关注机制来分析中文文本的情感倾向。通过多通道结构，此模型可以提取原始上下文特征和多尺度高级上下文特征。重要的是，可以根据实际语料库最佳地扩展模型通道的数量。此外，采用包括局部注意和全局注意的注意机制，以进一步区分特征。前者用于加权每个通道的输出特征，而后者用于加权所有通道的融合特征。此外，设计了自适应加权损失函数，可以有效避免训练数据中类的不平衡。最后，进行了一些实验，以证明该模型在两个公共数据集上的优越性能。与字级方法相比，准确性和宏 自适应加权损失函数旨在有效避免训练数据中类别的不平衡。最后，进行了一些实验，以证明该模型在两个公共数据集上的优越性能。与字级方法相比，准确性和宏 自适应加权损失函数旨在有效避免训练数据中类别的不平衡。最后，进行了一些实验，以证明该模型在两个公共数据集上的优越性能。与字级方法相比，准确性和宏$F\mathrm{1个}$ NLPCC2017-ECGC语料库分别增长了1.19％和0.9％以上，准确性和 $F\mathrm{1个}$ChnSentiCorp-Htl-unba-10000语料库分别增长了1.7％和1.214％以上。与char级的预训练方法相比，准确性和Macro-$F\mathrm{1个}$ 在NLPCC2017-ECGC语料库上也分别实现了3.416％和4.324％的改善，准确性和 $F\mathrm{1个}$ 在ChnSentiCorp-Htl-unba-10000语料库中，分别增加了0.14％和3％以上。