摘要
方面级情感分析是情感分析任务中更细粒度的子任务,目的是预测给定方面的情感倾向.目前方面级情感分析任务大多采用一定的神经网络提取句子的语义信息,之后进行情感极性预测.本文在此基础上,提出了基于语句结构信息的语义表示方法,即融合语句词性序列中的句型结构信息.本文分别使用两个Bi-LSTM进行语义特征和语句结构特征的提取,构建成基于句型结构的语义表示.然后将给定的方面级向量化,嵌入到基于语句结构的语义表示中,再经过Softmax层进行情感极性分类.实验证明,采用基于语句结构信息的语义表示方法进行方面级情感分析的效果更佳.
Aspect level sentiment analysis is a more fine-grained sub task of sentiment analysis tasks,the purpose of which is to predict sentiment tendencies of a certain aspect.At present,most aspect level sentiment analysis tasks use neural networks to extract semantic information of sentences,and then predict emotional polarity.Based on this,this study proposes a semantic representation method based on sentence structure information,that is,the fusion of sentence structure information in the part of speech sequence of the statement.In this work,two Bi-LSTM are used to extract the semantic feature and the structural feature of the statement,and the semantic representation based on sentence structure is constructed.Then,the given aspect level vectorization is embedded into the semantic representation based on the sentence structure,and then sent to the Softmax layer for sentiment classification.Experiments show that the semantic representation method based on the information of sentence structure is more effective.
作者
李梦磊
刘新
赵梦凡
李聪
LI Meng-Lei;LIU Xin;ZHAO Meng-Fan;LI Cong(College of Information Engineering,Xiangtan University,Xiangtan 411105,China)
出处
《计算机系统应用》
2020年第11期114-120,共7页
Computer Systems & Applications
基金
网络犯罪侦查湖南省普通高校重点实验室开放课题(2018WLFZZC003)
