Text Sentiment Orientation Analysis of Multi-Channels CNN and BiGRU Based on Attention Mechanism
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摘要: 近年来，卷积神经网络(convolutional neural network, CNN)和循环神经网络(recurrent neural network, RNN)已在文本情感分析领域得到广泛应用，并取得了不错的效果.然而，文本之间存在上下文依赖问题，虽然CNN能提取到句子连续词间的局部信息，但是会忽略词语之间上下文语义信息；双向门控循环单元(bidirectional gated recurrent unit, BiGRU)网络不仅能够解决传统RNN模型存在的梯度消失或梯度爆炸问题，而且还能很好地弥补CNN不能有效提取长文本的上下文语义信息的缺陷，但却无法像CNN那样很好地提取句子局部特征.因此提出一种基于注意力机制的多通道CNN和双向门控循环单元(MC-AttCNN-AttBiGRU)的神经网络模型.该模型不仅能够通过注意力机制关注到句子中对情感极性分类重要的词语，而且结合了CNN提取文本局部特征和BiGRU网络提取长文本上下文语义信息的优势，提高了模型的文本特征提取能力.在谭松波酒店评论数据集和IMDB数据集上的实验结果表明：提出的模型相较于其他几种基线模型可以提取到更丰富的文本特征，可以取得比其他基线模型更好的分类效果.Abstract: CNN(convolutional neural network) and RNN(recurrent neural network) have been widely used in the field of text sentiment analysis and have achieved good results in recent years. However, there is a problem of contextual dependency between texts, although CNN can extract local features between consecutive words of a sentence, it ignores the contextual semantic information between words. BiGRU(bidirectional gated recurrent unit) network can not only solve the problem of gradient disappearance or gradient explosion in traditional RNN model, but also make up for the shortcomings that CNN can’t effectively extract contextual semantic information of long text, while it can’t extract local features as well as CNN. Therefore, this paper proposes a MC-AttCNN-AttBiGRU(multi-channels CNN and BiGRU network based on attention mechanism) model. The model can notice the important words for sentiment classification in the sentence. It combines the advantages of CNN to extract local features of text and BiGRU network to extract contextual semantic information of long text, which improves the text feature extraction ability of the model. The experimental results on the Tan Songbo Hotel Review dataset and IMDB dataset show that the proposed model can extract richer text features than other baseline models, and can achieve better classification results than other baseline models.
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