基于知识图谱与循环注意力网络的视角级情感分析

现有的视角级情感分析方法难以解决单词在不同语境下“一词多义”问题,因此性能受限.针对上述问题,文中提出基于知识图谱与循环注意力网络的视角级情感分析方法.首先,利用动态注意力机制,结合双向长短时记忆网络的文本表示和知识图谱中的同义词信息,获得知识感知状态向量.再联合位置信息构造记忆内容,并输入多层门限循环单元,计算视角词情感特征,进行视角级文本情感分类.在3个公开数据集上的实验表明,文中方法分类效果较优

Aspect Level Sentiment Analysis Based on Knowledge Graph and Recurrent Attention Network

The existing aspect level sentiment analysis methods cannot solve the problem of polysemous word in different contexts. Therefore, a method for aspect level sentiment analysis based on knowledge graph and recurrent attention network is proposed. The text representation of the bidirectional long short-term memory network is integrated with synonym information in knowledge graph using dynamic attention mechanism to obtain the state vector of knowledge perception. To classify aspect level sentiment, the memory content is constructed by combining the location information and inputting the multi-level gated recurrent unit for calculating the sentiment characteristics of aspect terms. The experimental results show that the proposed method achieves better classification results on three open datasets.