【摘要】 针对传统情感分类方法提取文本信息单一的问题,提出了一种融合用户信息和产品信息的分层多头注意力的情感分类模型.首先,采用分层的多头注意力代替单一注意力,从多个视角获取有效信息.然后在每个注意力中都融入用户信息和产品信息,挖掘出用户和产品信息在多个子空间上的表现特征,使模型在多个子空间上得到更全局的用户偏好和产品特点对情感评分的影响.实验结果表明,模型在IMDB、Yelp2013、Yelp2014数据集上的准确率较之前基于神经网络的情感分析模型均有所提高.更多还原

【Abstract】 Aiming at the single information problem of traditional sentiment classification method, a multi-head attention model with user and product information is proposed. Firstly, hierarchical multi-head attention is used to replace singlehead attention and obtain effective information from multiple perspectives. Secondly, using multi-head attention with user information and product information, mining the performance characteristics of user and product information in multiple subspaces, the model can get a more global impact of user preferences and product characteristics on sentiment score in multiple subspaces. Experimental results on IMDB, Yelp2013, and Yelp2014 datasets show that the performance of the proposed model is better than the other advanced baselines.更多还原