面向双注意力网络的特定方面情感分析模型

特定方面情感分析已经成为自然语言处理领域的研究热点，其通过学习文本上下文的信息判别文本中特定方面的情感极性，可以更加有效地帮助人们了解用户对不同方面的情感表达.当前，将注意力机制和神经网络相结合的模型在解决特定方面情感分析任务时大多仅考虑单一层面的注意力信息，并且卷积神经网络无法获取全局结构信息、循环神经网络训练时间过长且单词间的依赖程度随着距离增加而逐渐减弱.针对上述问题，提出一种面向双注意力网络的特定方面情感分析(dual-attention networks for aspect-level sentiment analysis, DANSA)模型.首先，引入多头注意力机制，通过对输入进行多次不同的线性变换操作，获取更全面的注意力信息，同时，多头注意力机制可以实现并行化计算，保证了DANSA的训练速度.其次，DANSA引入自注意力机制，通过计算输入中每个单词与其他所有单词的注意力得分获取全局结构信息，并且单词间的依赖程度不会受到时间和句子长度的影响.最后，融合上下文自注意力信息与特定方面单词注意力信息，共同作为特定方面情感预测的依据，最终实现特定方面情感极性的预测.相比结合注意力机制的神经网络，DANSA弥补了注意力信息单一问题，不仅可以有效获取全局结构信息，还能够实现并行化计算，大大降低了训练时间.在SemEval2014数据集和Twitter数据集上进行实验，DANSA获得了更好的分类效果，进一步证明了DANSA的有效性.

Aspect-Based Sentiment Analysis Model Based on Dual-Attention Networks

Aspect-based sentiment analysis has become one of the hottest research issues in the field of natural language processing. It identifies the aspect sentiment polarity of texts by learning from context information, which can effectively help people understand the sentiment expression on different aspects. Currently, the most models with combining attention mechanism and neural network only consider a single level of attention information. When solving aspect-based sentiment analysis tasks, theses models have a lot of limitations. The convolutional neural network cannot capture the global structural information. For the recurrent neural network, the training time-consuming is too long, and the degree of dependence between words gradually decreases as the distance increases. To solve the above problems, we propose the dual-attention networks for aspect-level sentiment analysis (DANSA) model. Firstly, by introducing the multi-head attention mechanism, the model performs multiple linear transformation on the input to obtain more comprehensive attention information, which can realize parallel computing and enhance the training speed. Secondly, the self-attention mechanism is introduced to obtain global structural information by calculating the attention scores between each word and all other words in the input, and the degree of dependence between words is not affected by time and sentence length. Finally, the model makes a prediction of aspects sentiment polarity by combining the context self-attention information and the aspect of the word attention information. The extensive experiments on the SemEval2014 datasets and the Twitter datasets show that the DANSA achieves better classification performance, which further demonstrates the validity of DANSA.