Text Sentiment Analysis Based On Semantic Mining

Sentiment analysis,as a classic task of natural language processing,applied to a wide range of fields.Sentiment analysis plays an important role in public opinion monitoring,such as book review and film review statistics,product and service user feedback analysis,etc.From the granularity of the target concerned by the task,sentiment analysis can be divided into sentence level or document level sentiment analysis and aspect level sentiment analysis,both of which have important research value.In recent years,with the introduction of the self-attention mechanism,both kinds of sentiment analysis have made breakthroughs in performance.However,as a special task of natural language understanding,sentiment analysis not only requires the model to understand direct and simple expressions,but also requires the model to understand euphemistic and tortuous expressions mixed with irrelevant information.It is a difficult task to understand sentiment in texts containing complex sentiment expressions such as multiple negations and contrastive turns.In addition,in the aspect-based sentiment analysis task,for the text containing multiple aspects,finding the sentiment expression related to the aspect and understanding its sentiment is also a problem worth studying.In this thesis,we propose effective models for the above two problems,on the sentence-level or document-level sentiment analysis and on the aspect-level sentiment analysis,as follows:1.In the sentence-level or document-level text sentiment analysis,the existing mainstream models only calculate and infer the context-sensitive partial word meaning of the text,and cannot accurately understand the complex sentiment affected by syntactic.For this kind of problem,this thesis proposes to construct an sentiment analysis model from the perspective of combining syntactic information and semantic information,so as to improve the understanding of complex sentiment in the model.Experiments show that the proposed model can better understand complex emotions,with accuracy rates of96.5%,96.5%,and 98.8% on the IMDB,SST-2,and Yelp-2 datasets,respectively.2.In the aspect level sentiment analysis task,the existing methods use syntax dependency to construct graph model,and locate the sentiment expression related to the aspect word through graph convolution,thereby complete the aspect sentiment analysis.Some dependencies are obviously beneficial to the recognition of sentiment expressions related to aspect words,but the existing models assign the same weight to all dependencies,which does not make full utilize of the information in syntactic dependencies.In addition,existing models have the problem of introducing interference information into aspect level sentiment discrimination if there are too many graph convolutional layers,while if there are too few,they cannot fully capture relevant sentiment descriptions,resulting in misunderstandings.In response to these problems,this thesis proposes a differential weight graph convolution method to help quickly establish a stable connection between aspect words and their related emotions.It also utilizes the knowledge provided by component trees to generalize the perspective of sentiment expression,capturing comprehensive and non redundant sentiment expressions related to aspects.Combined with the experience of complex emotion analysis,it achieves aspect level emotion analysis.The experiment shows that the method proposed in this article performs outstandingly on multiple datasets,with accuracy rates of 78.1%,80.2%,87.0%,84.4%,and 92.5% respectively in the TWITTER dataset and Semival’s finegrained sentiment analysis datasets LAP14,REST14,REST15,and REST16 from 2014 to 2016.The ablation experiment verifies that the proposed improved method is effective.