基于辅助记忆循环神经网络的视角级情感分析

Aspect Level Sentiment Analysis Based on Recurrent Neural Network with Auxiliary Memory

现有视角级情感分析方法大多数利用视角词信息从句子中提取特征,不能同时利用视角和视角词信息,导致模型性能较低,为此文中提出基于辅助记忆循环神经网络的视角级情感分析方法.首先通过深度双向长短期记忆网络和单词的位置信息构建位置权重记忆,利用注意力机制结合视角词建立视角记忆.再联合位置权重记忆和视角记忆输入多层门循环单元,得到视角情感特征.最后由归一化函数识别情感极性.实验表明,相对基准实验,文中方法在3个公开数据集上的效果更好,该方法是有效的.

Aspect level sentiment analysis employs information of terms to extract features from a sentence,and it cannot utilize information of both aspects and terms simultaneously.Therefore,the model performance is low.Aiming at this problem,an aspect level sentiment analysis based on recurrent neural network with auxiliary memory is proposed.Deep bidirectional long short term memory(DBLSTM)and positional information of words are exploited to build position-weighted memory.The attention mechanism is combined with aspect terms to build aspect memory,and with position-weighted memory and aspect memory to input a multi-layer gated recurrent unit.Then,sentimental features of the aspect are obtained.Finally,sentimental polarity is identified by the normalized function.Experimental results show that the proposed method achieves better results on three public datasets with high effectiveness.