基于知识图谱与循环注意力网络的视角级情感分析  

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

作　　者：邓立明 魏晶晶[4] 吴运兵[1,2,3] 余小燕 廖祥文[1,2,3] DENG Liming;WEI Jingjing;WU Yunbing;YU Xiaoyan;LIAO Xiangwen(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116;Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350116;Digital Fujian Institute of Financial Big Data,Fuzhou 350116;College of Electronics and Information Science,Fujian Jiangxia University,Fuzhou 350108 Key WordsAspect Level Sentiment Analysis,Knowledge Graph,Attention Mechanism,Deep Learning)

机构地区：[1]福州大学数学与计算机科学学院,福州350116 [2]福州大学福建省网络计算与智能信息处理重点实验室,福州350116 [3]数字福建金融大数据研究所,福州350116 [4]福建江夏学院电子信息科学学院,福州350108

出　　处：《模式识别与人工智能》2020年第6期479-487,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61976054,61772135,U1605251);国家自然科学基金青年基金项目(No.41801324);福建省自然科学基金面上项目(No.2017J01755);模式识别国家重点实验室开放课题基金项目(No.201900041)资助。

摘　　要：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.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.

关 键 词：视角级情感分析 知识图谱 注意力机制 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]