Sentiment Analysis Research Incorporating Syntactic Structure Information

The current rapid development of Internet information technology makes people publish a large amount of text information on the Internet every day,including shopping reviews,movie reviews and current affairs reviews.How to extract and use the emotional information in these text data has become people's concern.As a hot spot,text sentiment analysis has also become an important branch task in the field of natural language processing.Text sentiment analysis has been a research task for many years.Its main purpose is to understand and analyze subjective texts with emotional tendencies,and to extract emotions or opinions from them.Text sentiment analysis technology based on deep learning has become the mainstream method for this task,and in recent years,the emergence of pre-trained models has greatly improved the best results on most text sentiment analysis datasets.However,these model methods mainly encode the sequence information of the text,and the use of the syntactic structure information of the text needs further research.Therefore,this thesis integrates the syntactic structure information of the text into the deep learning model,and conducts experimental investigations on sentence-level and aspect-level sentiment analysis tasks.The specific research work is as follows:(1)On the task of sentence-level sentiment analysis,this paper proposes the Tree-BERT model.This model is based on the Teacher-Student model framework in the knowledge distillation theory,and transmits the component syntactic structure information of the text learned by the teacher model Tree-LSTM network to the student model BERT in the form of emotional soft tags.Through this method,the BERT model can explicitly learn the constituent syntactic structure information of the text,achieving the effect of integrating the component syntactic structure information into the BERT model,and further improves the performance of using the BERT model alone on the datasets of Camera,Restaurant and Laptop sentence-level sentiment analysis tasks.(2)On the task of aspect-level sentiment analysis,this thesis proposes the ASA-SAWRs model.This model integrates the implicit dependency syntactic structure information representation generated by the Biaffine parser model into the ASA model proposed in this thesis.This model applies the implicit dependency syntactic structure information of text to the task of aspect-level sentiment analysis for the first time,further improves the performance of ASA model on Sem Eval2014 dataset and Twitter dataset,and alleviates the error propagation problem caused by dependency parser to the model.This thesis also discusses the influence of the part of speech of words in the context of aspect words on the task of aspect-level sentiment analysis of ASA model.Through experiments,it is found that filtering out those words with unimportant parts of speech in the original text can also improve the performance of the model on the above datasets.