基于方面级情感分析的深度语义挖掘模型

A Deep Semantic Mining Model Based on Aspect-LevelSentiment Analysis

方面级情感分析是一种细粒度的情感分类任务,具有广泛的应用前景,正因为如此,得到了广泛关注与研究,尤其是近年来,基于依赖树的图神经网络和基于注意力的网络模型的研究取得了较大进展.但是,由于在线评论表达的复杂性和依赖关系不易解析使得这些方法在情感分析的性能上得不到有效提升.为了克服这些挑战,本文提出了一种同时考虑句法语义和上下文语义的深度语义挖掘模型(Deep Semantic Mining Model,DSMM).具体地,为了深度挖掘句法背后隐含的深度语义,模型采用并行的图卷积和多头注意力机制挖掘丰富的语义;为了充分利用句法语义和上下文语义的内在关联关系,采用了关联注意力机制获取句法语义和上下文语义的相关性,并且采用自适应方面路由机制有效获取方面的情感语义,并在此基础上,通过引入基于依赖树的语义位置嵌入,进一步增强方面-意见词的关联.在三个公共数据集上的实验结果表明,该模型在复杂句情感分析中既能从不同语义空间挖掘句子的语义特征,也能有效利用句法特征强化句子的语义表征,在分类准确率和泛化能力上的表现优于相关工作.

Aspect level sentiment analysis is a fine-grained sentiment classification task,which has a wide range of application prospects.Therefore,it has been widely concerned and researched,especially in recent years,the graph neural network based on dependency tree and the network model based on attention have made great progress.However,these studies are limited by factors such as the difficulty in parsing dependency and the complex expression of online reviews.To overcome these challenges,this paper proposes a deep semantic mining model(DSMM)that considers both syntactic and contextual semantics.Specifically,in order to mine deep semantic hidden behind the syntax,the model uses parallel graph convolution and multi-head self-attention to mine rich semantic.In order to make full use of the intrinsic correlation be⁃tween syntactic semantics and contextual semantics,we used the relevance attention mechanism to obtain the correlation be⁃tween syntactic semantics and contextual semantics,and we used the adaptive aspect routing mechanism to obtain the senti⁃ment semantics of aspects effectively.Moreover,we introduced the semantic location embedding based on dependency tree to further enhance the aspect-opinion word correlation.The experimental results on three public datasets show that our mod⁃el can not only mine the semantic features of sentences from different semantic spaces,but also effectively use the syntactic features to strengthen the semantic representation of sentences in sentiment analysis of complex sentence,and the perfor⁃mance of classification accuracy and generalization ability is better than that of related work.