基于循环实体网络的细粒度情感分析

目前,深度神经网络模型已经在文本情感分析领域取得了较好的效果,但是对于属性相关的细粒度的情感分析任务,现有研究方法的效果仍有待改进。该文提出了一种基于循环实体网络来进行细粒度情感分析的方法,在网络中嵌入预定义的评价属性类别信息,利用扩大的内部记忆链来抽取与每个属性类别相关的情感特征,并通过动态记忆单元控制与属性相关情感信息的远距离依赖,然后,对于给定的单个属性类别,利用注意力机制从内部记忆链中抽取该属性类别的情感特征进行分类。该文提出的方法在Sentihood数据上与目前精度最高的方法相比,取得了近1个百分点的提升,而且模型的收敛速度更快。

Recurrent Entity Networks for Fine-grained Sentiment Analysis

At present, deep learning models have achieved good results in text sentiment analysis. Following this thread, we propose a method based on recurrent entity networks for fine-grained sentiment analysis. In this method, predefined evaluation attribute categories information is embedded in networks, and the sentiment features about each attribute categories are extracted through the expanded internal memory chains. The dynamic memory unit controls the distant sentiment dependence about attribute categories. Finally, for a given attribute category, attention mechanism is applied to extract sentiment features from internal memory chains. In our experiment, the proposed method achieves a nearly one percentage point improvement in Sentihood data compared to the current highest accuracy method.