基于双层注意力循环神经网络的方面级情感分析

在方面级情感分析中,常用的方法是将循环神经网络和注意力机制结合,利用注意力获取序列中不同单词的重要程度,但这并不能获取不同句子的重要程度,仅仅依赖单层注意力难于获取深层次情感特征信息。为了解决上述问题,该文提出一种基于双层注意力循环神经网络模型。通过双层注意力分别对单词层和句子层进行建模,捕获不同单词和不同句子的重要性,利用双向的循环神经网络获取更多的序列上下文依赖信息。情感特征信息具有重要性,但在深层神经网络中却未被充分利用。因此,该文将方面、词性信息和位置信息作为模型的辅助信息,进行更深层次情感特征信息学习,有效识别不同方面的情感极性。该方法与IAN模型相比,在SemEval 2014中的Restaurant数据集和Laptop数据集上进行实验,分类准确率分别提升了2.0%和5.2%。在与TD-LSTM模型对比中,Twitter数据集的分类准确率提升了1.7%。

Double Attention Neural Network for Aspect-Based Sentiment Analysis

In aspect-based sentiment analysis, the attention mechanism is often combined in recurrent neural network to obtain the importance of different words. But it fails to obtain the importance of different sentences, nor the deep sentiment feature information. To deal with this issue, this paper proposes a double attention recurrent neural network. Double attention capture the importance of different words and different sentences in the word level and sentence level, respectively. Meanwhile, the aspects, the part of speech information and the position information are used as the auxiliary information of the model to identify the sentiment polarity of different aspects. Compared with IAN model on Restaurant dataset, Laptop dataset of the SemEval 2014, the classification accuracy is increased by 2.0% and5.2%, respectively. Compared with TD-LSTM model in Twitter dataset, the classification accuracy is raised by 1.7%.