融合卷积神经网络与双向GRU的文本情感分析胶囊模型

文本情感分析是自然语言处理领域一个重要的分支。现有深度学习方法不能更为全面地提取文本情感特征,且严重依赖于大量的语言知识和情感资源,需要将这些特有的情感信息充分利用使模型达到最佳性能。该文提出了一种融合卷积神经网络与双向GRU网络的文本情感分析胶囊模型。该模型首先使用多头注意力学习单词间的依赖关系、捕获文本中情感词,利用卷积神经网络和双向GRU提取文本不同粒度的情感特征,特征融合后输入全局平均池化层,在得到文本的实例特征表示的同时,针对每个情感类别结合注意力机制生成特征向量构建情感胶囊,最后根据胶囊属性判断文本情感类别。模型在MR、IMDB、SST-5及谭松波酒店评论数据集上进行实验,相比于其他基线模型具有更好的分类效果。

Text Sentiment Analysis Capsule Model Combining Convolutional Neural Network and Bidirectional GRU

Text sentiment analysis is an important branch in the field of natural language processing. This paper proposes a text sentiment analysis capsule model that combines convolutional neural networks and bidirectional GRU networks. Firstly, the multi-head attention is used to learn the dependency between words and capture the emotional words in the text. Then, the convolutional neural network and bidirectional GRU network are used to extract emotional features of different granularities in the text. After the feature fusion, the global average pooling is used to get the instance feature representation of the text, and the attention mechanism is combined to generate feature vectors for each emotion category to construct an emotion capsule. Finally, the emotion category of the text is judged by the capsule attributes. Tested on the MR, IMDB, SST-5 and Tan Songbo hotel review datasets, the proposed model achieves better classification effect than other baseline models.