A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis
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.
万方数据
维普
