An Interactive Aspect-Level Sentiment Analysis Model Based On A Dependency Syntax Tree

With the rapid development of Internet applications and the rise of major social networking platforms,people are increasingly interested in expressing their views,opinions,etc.on the Internet.By collecting,collating,and analyzing these comment texts with users’ sentiment tendencies,businesses can better understand their customers’ behavior and preferences,and governments can keep abreast of the public’s views on a certain policy and appropriately guide public opinion,which has certain social and commercial value.Aspect-Based Sentiment Analysis,which has become popular in recent years,extracts the aspect entities first and then determines the sentiment attitudes corresponding to them,in a more detailed way,and when a sentence contains multiple aspects,it is possible to match the sentiment polarities of different aspect entities according to their The ability to match different aspectual entities to their corresponding emotional polarity when multiple aspects are included in a sentence is of great interest because of its ability to meet a wider range of practical needs.Currently,most aspect-level based sentiment analysis models rely on dependency syntactic trees to obtain syntactic information and combine with neural networks for sentiment polarity prediction,but obtaining information based on dependency trees alone cannot distinguish the importance of different contextual words on the one hand,resulting in inadequate extraction of semantic information;on the other hand,the connection between contextual information and the target aspect will be ignored because attention is calculated separately,making some models inefficient.On the other hand,some important feature information is lost because the contextual information and the target aspect are neglected by calculating attention alone.Therefore,to solve the above problems,this paper proposes an Interactive Aspect-Level Sentiment Analysis Model Based on a Dependency Syntax Tree(hereafter referred to as the SIGAT model).The model consists of two main parts: one is the extraction of target aspects based on syntactic attention mechanisms.The module mainly combines syntactic information with attention mechanisms,first using a syntactic parser to generate a dependency syntax tree,then using syntactic attention mechanisms to encode the syntactic information on the dependency tree,modeling the semantic relations of contextual words,and selectively paying attention to contextual words close to the target aspect on the syntactic path,giving more attention to opinion words close to the syntactic relative distance,and introducing a Gaussian function to reduce the complexity of the computation and avoid the loss of sentiment information due to a sharp drop in weighting.Another part is the graph attention network based contextual feature extraction,aided by a dependency syntactic tree,which not only considers syntactic information but also the dependency relationship between words,enriching the representation of words;and finally,through the interactive attention mechanism,the contextual features based on the graph attention network and the target aspect features based on the syntactic attention mechanism are learned interactively,using each other’s information to supplement Finally,through the interactive attention mechanism,the contextual features based on the graph attention network and the target aspect features based on the syntactic attention mechanism are learned interactively,using each other’s information to supplement their own feature information and improve the expression of the features.Finally,to demonstrate the classification effectiveness of the SIGAT model proposed in this paper,experiments were conducted on three domain datasets:Restaurant reviews(Restaurant),Laptop reviews(Laptop)and ACL-14 Twitter in Sem Eval-2014 Task 4.By comparing with other baseline models,the SIGAT model outperformed the accuracy(Acc)and F1 values on the dataset,confirming the reliability of the model.Also,the ablation experiments and the analysis of different influencing factors revealed that the syntactic information and the interaction attention mechanism on the dependency tree in the SIGAT model made an important contribution to improving the model’s performance.