A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment information of the whole sentence through a CNN network at the sentence level. Experimental results on different data sets show that the CNN‐RLSTM model is better than the traditional model and the deep network model.

中文翻译：

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用于基于目标的情绪分析的分层神经模型

提出了卷积神经网络区域长短期记忆（CNN‐RLSTM），它是将CNN和区域LSTM相结合的卷积神经网络区域长短期记忆（CNN‐RLSTM）。该模型可以通过区域LSTM有效区分不同目标的情感极性，同时减少了模型的训练时间。另外，该模型可以通过CNN网络在句子级别保留整个句子的情感信息。在不同数据集上的实验结果表明，CNN-RLSTM模型优于传统模型和深度网络模型。