Abstract:

Most text sentiment analysis methods cannot effectively reflect the importance of different words in a text sequence and can not obtain enough text information. The text sentiment analysis model of the two-channel hybrid neural network is proposed. Firstly, the hybrid neural network layer adopts the attention mechanism respectively after the capsule network model and the Bidirectional Gated Recurrent Unit（BiGRU） model, so that it can perceive context information adaptively and extract the text features that affect text sentiment analysis. The model fuses the features extracted by the two models. Then, the results obtained by mixing the two different word vectors through the hybrid neural network layer are further fused and classified by using the Softmax classifier. The results on the three datasets demonstrate the validity of the model.

Key words: hybrid neural network, feature fusion, attention mechanism, Bidirectional Gated Recurrent Unit（BiGRU）, capsule network

摘要：

大多数文本情感分析方法不能有效地反映文本序列中不同单词的重要程度，并且不能获得足够的文本信息。提出了一种双通道混合神经网络的文本情感分析模型，混合神经网络层在胶囊网络（Capsule Network）模型和双向门限循环单元（BiGRU）模型之后分别引入注意力机制，使其自适应地感知上下文信息并提取影响文本情感分析的文本特征，将两种模型提取的特征进行融合。将两种不同的词向量经过混合神经网络层得到的结果进一步融合，由Softmax分类器进行分类。在三个标准数据集上的实验结果证明了该模型的有效性。

关键词: 混合神经网络, 特征融合, 注意力机制, 双向门循环单元, 胶囊网络